Quantum robotics is an interdisciplinary field that investigates the intersection of robotics and quantum mechanics. This field, in particular, explores the applications of quantum phenomena such as quantum entanglement
within the realm of robotics. Examples of its applications include
quantum communication in multi-agent cooperative robotic scenarios, the
use of quantum algorithms in performing robotics tasks, and the
integration of quantum devices (e.g., quantum detectors) in robotic systems.
Introduction
The free-space quantum communication between mobile platforms was
proposed for reconfigurable Quantum Key Distribution (QKD) applications
using drones in 2017. This technology was later advanced in various aspects in
mobile drone and vehicle platforms in several configurations such as
drone-to-drone, drone-to-moving vehicle, and vehicle-to-vehicle systems .Communication system technology for demonstration of BB84 quantum key distribution in optical aircraft downlinks. Airborne demonstration of a quantum key distribution receiver payload. Communication system technology for demonstration of BB84 quantum key distribution in optical aircraft downlinks.
Other researchers contributed to low size, weight and power
quantum key distribution system for small form unmanned aerial vehicles, characterization of a polarization-based receiver for mobile free space optical QKD, and optical-relayed entanglement distribution using drones as mobile nodes. The topic of free-space quantum communication between mobile platforms,
which was initially implemented to fulfill the need for free-space QKD
and entanglement distribution using mobile nodes, was brought into
robotics domain as an emerging interdisciplinary mechatronics topic to investigate and explore the interface between the quantum technologies and robotic systems domain. The main advantage of such integrated technology being the guaranteed
security in communication between multiagent and cooperative autonomous
systems. Although as a newfound emerging area, other benefits are
anticipated in the future research by accessing the fast-growing and
forthcoming quantum advantages. However, such progress can only be made
after a foundation is laid out in what is referred to as “quantum
robotics” and “quantum mechatronics”. The paper contributes to providing the complementary background needed
for the research in integrating free-space quantum communication into
the robotics field. Other contributions include modernizing the
mechatronics discipline with quantum engineering for educational
purposes which was initially proposed in. This paper further introduces quantum engineering topics needed in
training and preparing the future engineering workforce to succeed in
the rapid-paced ever-changing industry. In particular, the topics on the
quantum mechanics fundamentals such as quantum entanglement,
cryptography, teleportation, as well and the Bell test, are proposed which are suitable for engineering curriculum and University projects.
Alice and Bob Robots
In the realm of quantum mechanics, the names Alice and Bob are frequently employed to illustrate various phenomena, protocols, and applications. These include their roles in quantum cryptography, quantum key distribution, quantum entanglement, and quantum teleportation. The terms "Alice Robot" and "Bob Robot" serve as analogous expressions that merge the concepts of Alice and Bob
from quantum mechanics with mechatronic mobile platforms (such as
robots, drones, and autonomous vehicles). For example, the Alice Robot
functions as a transmitter platform that communicates with the Bob
Robot, housing the receiving detectors.
Quantum Entanglement Experiment via Spontaneous Parametric Down-Conversion (SPDC)
The experimental setup that includes the laser source, and Alice and Bob is shown in the figure below.
Quantum Entanglement Experimental Setup via SPDC
The Alice and Bob and the corresponding components.
Alice and Bob Setup in a Polarization Quantum Entanglement Experiment
The schematic representation of the Alice and Bob robots when sharing entangled photons in a quantum communication or quantum key distribution experimental setup between moving robotic platforms is shown in the figure.
Alice and Bob Quantum Robots Schematic Representation
Roboticists with three Mars rover robots. Front and center is the flight spare for the first Mars rover, Sojourner,
which landed on Mars in 1997 as part of the Mars Pathfinder Project. On
the left is a Mars Exploration Rover (MER) test vehicle that is a
working sibling to Spirit and Opportunity, which landed on Mars in 2004. On the right is a test rover for the Mars Science Laboratory, which landed Curiosity on Mars in 2012.
The goal of most robotics is to design machines that can help and assist humans.
Many robots are built to do jobs that are hazardous to people, such as
finding survivors in unstable ruins, and exploring space, mines and
shipwrecks. Others replace people in jobs that are boring, repetitive,
or unpleasant, such as cleaning, monitoring, transporting, and
assembling. Today, robotics is a rapidly growing field, as technological
advances continue; researching, designing, and building new robots
serve various practical purposes.
A roboticist is someone who specializes in robotics.
Robotics aspects
Mechanical aspectElectrical aspectSoftware aspect
Robotics usually combines three aspects of design work to create robot systems:
Mechanical construction: a frame, form or shape designed to
achieve a particular task. For example, a robot designed to travel
across heavy dirt or mud might use caterpillar tracks. Origami inspired
robots can sense and analyze in extreme environments. The mechanical aspect of the robot is mostly the creator's solution to
completing the assigned task and dealing with the physics of the
environment around it. Form follows function.
Electrical components that power and control the machinery. For example, the robot with caterpillar tracks
would need some kind of power to move the tracker treads. That power
comes in the form of electricity, which will have to travel through a
wire and originate from a battery, a basic electrical circuit. Even petrol-powered machines
that get their power mainly from petrol still require an electric
current to start the combustion process which is why most petrol-powered
machines like cars, have batteries. The electrical aspect of robots is
used for movement (through motors), sensing (where electrical signals
are used to measure things like heat, sound, position, and energy
status), and operation (robots need some level of electrical energy supplied to their motors and sensors in order to activate and perform basic operations)
Software.
A program is how a robot decides when or how to do something. In the
caterpillar track example, a robot that needs to move across a muddy
road may have the correct mechanical construction and receive the
correct amount of power from its battery, but would not be able to go
anywhere without a program telling it to move. Programs are the core
essence of a robot, it could have excellent mechanical and electrical
construction, but if its program is poorly structured, its performance
will be very poor (or it may not perform at all). There are three
different types of robotic programs: remote control, artificial
intelligence, and hybrid. A robot with remote control
programming has a preexisting set of commands that it will only perform
if and when it receives a signal from a control source, typically a
human being with remote control. It is perhaps more appropriate to view
devices controlled primarily by human commands as falling in the
discipline of automation rather than robotics. Robots that use artificial intelligence
interact with their environment on their own without a control source,
and can determine reactions to objects and problems they encounter using
their preexisting programming. A hybrid is a form of programming that
incorporates both AI and RC functions in them.
Applied robotics
As many robots are designed for specific tasks, this method of
classification becomes more relevant. For example, many robots are
designed for assembly work, which may not be readily adaptable for other
applications. They are termed "assembly robots". For seam welding, some
suppliers provide complete welding systems with the robot i.e. the
welding equipment along with other material handling facilities like
turntables, etc. as an integrated unit. Such an integrated robotic
system is called a "welding robot" even though its discrete manipulator
unit could be adapted to a variety of tasks. Some robots are
specifically designed for heavy load manipulation, and are labeled as
"heavy-duty robots".
Current and potential applications include:
Manufacturing. Robots have been increasingly used in manufacturing since the 1960s. According to the Robotic Industries Association US data, in 2016 the automotive industry was the main customer of industrial robots with 52% of total sales. In the auto industry, they can amount for more than half of the "labor". There are even "lights off" factories such as an IBM keyboard manufacturing factory in Texas that was fully automated as early as 2003.
Food processing. Commercial
examples of kitchen automation are Flippy (burgers), Zume Pizza
(pizza), Cafe X (coffee), Makr Shakr (cocktails), Frobot (frozen
yogurts), Sally (salads), salad or food bowl robots manufactured by Dexai (a Draper Laboratory spinoff, operating on military bases), and integrated food bowl assembly systems manufactured by Spyce Kitchen (acquired by Sweetgreen) and Silicon Valley startup Hyphen. Other examples may include manufacturing technologies based on 3D Food Printing.
The InSight lander with solar panels deployed in a cleanroom
At present, mostly (lead–acid) batteries
are used as a power source. Many different types of batteries can be
used as a power source for robots. They range from lead–acid batteries,
which are safe and have relatively long shelf lives but are rather heavy
compared to silver–cadmium batteries which are much smaller in volume
and are currently much more expensive. Designing a battery-powered robot
needs to take into account factors such as safety, cycle lifetime, and weight. Generators, often some type of internal combustion engine,
can also be used. However, such designs are often mechanically complex
and need fuel, require heat dissipation, and are relatively heavy. A
tether connecting the robot to a power supply would remove the power
supply from the robot entirely. This has the advantage of saving weight
and space by moving all power generation and storage components
elsewhere. However, this design does come with the drawback of
constantly having a cable connected to the robot, which can be difficult
to manage. Potential power sources could be:
Actuators are the "muscles" of a robot, the parts which convert stored energy into movement. By far the most popular actuators are electric motors that rotate a
wheel or gear, and linear actuators that control industrial robots in
factories. There are some recent advances in alternative types of
actuators, powered by electricity, chemicals, or compressed air.
The vast majority of robots use electric motors, often brushed and brushless DC motors in portable robots or AC motors in industrial robots and CNC
machines. These motors are often preferred in systems with lighter
loads, and where the predominant form of motion is rotational.
Various types of linear actuators move in and out instead of by
spinning, and often have quicker direction changes, particularly when
very large forces are needed such as with industrial robotics. They are
typically powered by compressed air (pneumatic actuator) or an oil (hydraulic actuator)
Linear actuators can also be powered by electricity which usually
consists of a motor and a leadscrew. Another common type is a mechanical
linear actuator such as a rack and pinion on a car.
Series elastic actuators
Series elastic actuation (SEA) relies on the idea of introducing
intentional elasticity between the motor actuator and the load for
robust force control. Due to the resultant lower reflected inertia,
series elastic actuation improves safety when a robot interacts with the
environment (e.g., humans or workpieces) or during collisions. Furthermore, it also provides energy efficiency
and shock absorption (mechanical filtering) while reducing excessive
wear on the transmission and other mechanical components. This approach
has successfully been employed in various robots, particularly advanced
manufacturing robots and walking humanoid robots.
The controller design of a series elastic actuator is most often performed within the passivity framework as it ensures the safety of interaction with unstructured environments. Despite its remarkable stability and robustness, this framework suffers
from the stringent limitations imposed on the controller which may
trade-off performance. The reader is referred to the following survey
which summarizes the common controller architectures for SEA along with
the corresponding sufficient passivity conditions. One recent study has derived the necessary and sufficient passivity conditions for one of the most common impedance control architectures, namely velocity-sourced SEA. This work is of particular importance as it drives the non-conservative
passivity bounds in an SEA scheme for the first time which allows a
larger selection of control gains.
Pneumatic artificial muscles also known as air muscles, are special
tubes that expand (typically up to 42%) when air is forced inside them.
They are used in some robot applications.
Muscle wire, also known as shape memory alloy, is a material that
contracts (under 5%) when electricity is applied. They have been used
for some small robot applications.
EAPs or EPAMs are a plastic material that can contract substantially
(up to 380% activation strain) from electricity, and have been used in
facial muscles and arms of humanoid robots, and to enable new robots to float, fly, swim or walk.
Recent alternatives to DC motors are piezo motors or ultrasonic motors. These work on a fundamentally different principle, whereby tiny piezoceramic
elements, vibrating many thousands of times per second, cause linear or
rotary motion. There are different mechanisms of operation; one type
uses the vibration of the piezo elements to step the motor in a circle
or a straight line. Another type uses the piezo elements to cause a nut to vibrate or to drive a screw. The advantages of these motors are nanometer resolution, speed, and available force for their size. These motors are already available commercially and being used on some robots.
Elastic nanotubes are a promising artificial muscle technology in
early-stage experimental development. The absence of defects in carbon nanotubes enables these filaments to deform elastically by several percent, with energy storage levels of perhaps 10 J/cm3
for metal nanotubes. Human biceps could be replaced with an 8 mm
diameter wire of this material. Such compact "muscle" might allow future
robots to outrun and outjump humans.
Sensors allow robots to receive information about a certain
measurement of the environment, or internal components. This is
essential for robots to perform their tasks, and act upon any changes in
the environment to calculate the appropriate response. They are used
for various forms of measurements, to give the robots warnings about
safety or malfunctions, and to provide real-time information about the
task it is performing.
Current robotic and prosthetic hands receive far less tactile information than the human hand. Recent research has developed a tactile sensor array that mimics the mechanical properties and touch receptors of human fingertips. The sensor array is constructed as a rigid core surrounded by
conductive fluid contained by an elastomeric skin. Electrodes are
mounted on the surface of the rigid core and are connected to an
impedance-measuring device within the core. When the artificial skin
touches an object the fluid path around the electrodes is deformed,
producing impedance changes that map the forces received from the
object. The researchers expect that an important function of such
artificial fingertips will be adjusting the robotic grip on held
objects.
Scientists from several European countries and Israel developed a prosthetic hand in 2009, called SmartHand, which functions like a real one —allowing patients to write with it, type on a keyboard,
play piano, and perform other fine movements. The prosthesis has
sensors which enable the patient to sense real feelings in its
fingertips.
Other common forms of sensing in robotics use lidar, radar, and sonar. Lidar measures the distance to a target by illuminating the target with laser light and measuring the reflected light with a sensor. Radar uses radio waves to determine the range, angle, or velocity of objects. Sonar uses sound propagation to navigate, communicate with or detect objects on or under the surface of the water.
Mechanical grippers
One of the most common types of end-effectors are "grippers". In its
simplest manifestation, it consists of just two fingers that can open
and close to pick up and let go of a range of small objects. Fingers
can, for example, be made of a chain with a metal wire running through
it. Hands that resemble and work more like a human hand include the Shadow Hand and the Robonaut hand. Hands that are of a mid-level complexity include the Delft hand. Mechanical grippers can come in various types, including friction and
encompassing jaws. Friction jaws use all the force of the gripper to
hold the object in place using friction. Encompassing jaws cradle the
object in place, using less friction.
Suction end-effectors
Suction end-effectors, powered by vacuum generators, are very simple astrictive devices that can hold very large loads provided the prehension surface is smooth enough to ensure suction.
Pick and place robots for electronic components and for large
objects like car windscreens, often use very simple vacuum
end-effectors.
Suction is a highly used type of end-effector in industry, in part because the natural compliance
of soft suction end-effectors can enable a robot to be more robust in
the presence of imperfect robotic perception. As an example: consider
the case of a robot vision system that estimates the position of a water
bottle but has 1 centimeter of error. While this may cause a rigid
mechanical gripper to puncture the water bottle, the soft suction
end-effector may just bend slightly and conform to the shape of the
water bottle surface.
General purpose effectors
Some advanced robots are beginning to use fully humanoid hands, like the Shadow Hand, MANUS, and the Schunk hand. They have powerful Robot Dexterity Intelligence (RDI), with as many as 20 degrees of freedom and hundreds of tactile sensors.
Control robotics areas
Puppet Magnus, a robot-manipulated marionette with complex control systemsExperimental planar robot arm and sensor-based, open-architecture robot controllerRuBot II can manually resolve Rubik's cubes.
The mechanical structure of a robot must be controlled to perform tasks. The control of a robot involves three distinct phases – perception, processing, and action (robotic paradigms). Sensors
give information about the environment or the robot itself (e.g. the
position of its joints or its end effector). This information is then
processed to be stored or transmitted and to calculate the appropriate
signals to the actuators (motors), which move the mechanical structure to achieve the required co-ordinated motion or force actions.
The processing phase can range in complexity. At a reactive
level, it may translate raw sensor information directly into actuator
commands (e.g. firing motor power electronic gates based directly upon
encoder feedback signals to achieve the required torque/velocity of the
shaft). Sensor fusion
and internal models may first be used to estimate parameters of
interest (e.g. the position of the robot's gripper) from noisy sensor
data. An immediate task (such as moving the gripper in a certain
direction until an object is detected with a proximity sensor) is
sometimes inferred from these estimates. Techniques from control theory
are generally used to convert the higher-level tasks into individual
commands that drive the actuators, most often using kinematic and
dynamic models of the mechanical structure.
At longer time scales or with more sophisticated tasks, the robot may need to build and reason with a "cognitive" model. Cognitive models
try to represent the robot, the world, and how the two interact.
Pattern recognition and computer vision can be used to track objects. Mapping techniques can be used to build maps of the world. Finally, motion planning and other artificial intelligence
techniques may be used to figure out how to act. For example, a planner
may figure out how to achieve a task without hitting obstacles, falling
over, etc.
Modern commercial robotic control systems are highly complex,
integrate multiple sensors and effectors, have many interacting
degrees-of-freedom (DOF) and require operator interfaces, programming
tools and real-time capabilities. They are oftentimes interconnected to wider communication networks and in many cases are now both IoT-enabled and mobile. Progress towards open architecture, layered, user-friendly and
'intelligent' sensor-based interconnected robots has emerged from
earlier concepts related to Flexible Manufacturing Systems (FMS), and several 'open or 'hybrid' reference architectures
exist which assist developers of robot control software and hardware to
move beyond traditional, earlier notions of 'closed' robot control
systems have been proposed. Open architecture controllers are said to be better able to meet the
growing requirements of a wide range of robot users, including system
developers, end users and research scientists, and are better positioned
to deliver the advanced robotic concepts related to Industry 4.0. In addition to utilizing many established features of robot
controllers, such as position, velocity and force control of end
effectors, they also enable IoT interconnection and the implementation
of more advanced sensor fusion and control techniques, including
adaptive control, Fuzzy control and Artificial Neural Network (ANN)-based control. When implemented in real-time, such techniques can potentially improve
the stability and performance of robots operating in unknown or
uncertain environments by enabling the control systems to learn and
adapt to environmental changes.[54]
There are several examples of reference architectures for robot
controllers, and also examples of successful implementations of actual
robot controllers developed from them. One example of a generic
reference architecture and associated interconnected, open-architecture
robot and controller implementation was used in a number of research and
development studies, including prototype implementation of novel
advanced and intelligent control and environment mapping methods in
real-time.
Manipulation
KUKAindustrial robot operating in a foundryPuma, one of the first industrial robotsBaxter, a modern and versatile industrial robot developed by Rodney BrooksLefty, first checker playing robot
A definition of robotic manipulation has been provided by Matt Mason
as: "manipulation refers to an agent's control of its environment
through selective contact".
Robots need to manipulate objects; pick up, modify, destroy, move
or otherwise have an effect. Thus the functional end of a robot arm
intended to make the effect (whether a hand, or tool) are often referred
to as end effectors, while the "arm" is referred to as a manipulator. Most robot arms have replaceable end-effectors, each allowing them to
perform some small range of tasks. Some have a fixed manipulator that
cannot be replaced, while a few have one very general-purpose
manipulator, for example, a humanoid hand.
For simplicity, most mobile robots have four wheels or a number of continuous tracks.
Some researchers have tried to create more complex wheeled robots with
only one or two wheels. These can have certain advantages such as
greater efficiency and reduced parts, as well as allowing a robot to
navigate in confined places that a four-wheeled robot would not be able
to.
Two-wheeled balancing robots
Balancing robots generally use a gyroscope
to detect how much a robot is falling and then drive the wheels
proportionally in the same direction, to counterbalance the fall at
hundreds of times per second, based on the dynamics of an inverted pendulum. Many different balancing robots have been designed. While the Segway
is not commonly thought of as a robot, it can be thought of as a
component of a robot, when used as such Segway refer to them as RMP
(Robotic Mobility Platform). An example of this use has been as NASA's Robonaut that has been mounted on a Segway.
A one-wheeled balancing robot is an extension of a two-wheeled
balancing robot so that it can move in any 2D direction using a round
ball as its only wheel. Several one-wheeled balancing robots have been
designed recently, such as Carnegie Mellon University's "Ballbot" which is the approximate height and width of a person, and Tohoku Gakuin University's "BallIP". Because of the long, thin shape and ability to maneuver in tight
spaces, they have the potential to function better than other robots in
environments with people.
Several attempts have been made in robots that are completely inside a
spherical ball, either by spinning a weight inside the ball, or by rotating the outer shells of the sphere. These have also been referred to as an orb bot or a ball bot.
Six-wheeled robots
Using six wheels instead of four wheels can give better traction or grip in outdoor terrain such as on rocky dirt or grass.
Tracked robots
Tracks provide even more traction than a six-wheeled robot. Tracked
wheels behave as if they were made of hundreds of wheels, therefore are
very common for outdoor off-road robots, where the robot must drive on
very rough terrain. However, they are difficult to use indoors such as
on carpets and smooth floors. Examples include NASA's Urban Robot
"Urbie".
Walking is a difficult and dynamic problem to solve. Several robots
have been made which can walk reliably on two legs, however, none have
yet been made which are as robust as a human. There has been much study
on human-inspired walking, such as AMBER lab which was established in
2008 by the Mechanical Engineering Department at Texas A&M
University. Many other robots have been built that walk on more than two legs, due to these robots being significantly easier to construct. Walking robots can be used for uneven terrains, which would provide
better mobility and energy efficiency than other locomotion methods.
Typically, robots on two legs can walk well on flat floors and can
occasionally walk up stairs. None can walk over rocky, uneven terrain. Some of the methods which have been tried are:
The zero moment point (ZMP) is the algorithm used by robots such as Honda's ASIMO. The robot's onboard computer tries to keep the total inertial forces (the combination of Earth's gravity and the acceleration and deceleration of walking), exactly opposed by the floor reaction force (the force of the floor pushing back on the robot's foot). In this way, the two forces cancel out, leaving no moment (force causing the robot to rotate and fall over). However, this is not exactly how a human walks, and the difference is
obvious to human observers, some of whom have pointed out that ASIMO
walks as if it needs the lavatory. ASIMO's walking algorithm is not static, and some dynamic balancing is
used (see below). However, it still requires a smooth surface to walk
on.
Hopping
Several robots, built in the 1980s by Marc Raibert at the MIT
Leg Laboratory, successfully demonstrated very dynamic walking.
Initially, a robot with only one leg, and a very small foot could stay
upright simply by hopping. The movement is the same as that of a person on a pogo stick. As the robot falls to one side, it would jump slightly in that direction, in order to catch itself. Soon, the algorithm was generalized to two and four legs. A bipedal robot was demonstrated running and even performing somersaults. A quadruped was also demonstrated which could trot, run, pace, and bound. For a full list of these robots, see the MIT Leg Lab Robots page.
Dynamic balancing (controlled falling)
A more advanced way for a robot to walk is by using a dynamic
balancing algorithm, which is potentially more robust than the Zero
Moment Point technique, as it constantly monitors the robot's motion,
and places the feet in order to maintain stability. This technique was recently demonstrated by Anybots' Dexter Robot, which is so stable, it can even jump. Another example is the TU Delft Flame.
Perhaps the most promising approach uses passive dynamics where the momentum of swinging limbs is used for greater efficiency. It has been shown that totally unpowered humanoid mechanisms can walk down a gentle slope, using only gravity
to propel themselves. Using this technique, a robot need only supply a
small amount of motor power to walk along a flat surface or a little
more to walk up a hill. This technique promises to make walking robots at least ten times more efficient than ZMP walkers, like ASIMO.
Flying
A modern passenger airliner is essentially a flying robot, with two humans to manage it. The autopilot can control the plane for each stage of the journey, including takeoff, normal flight, and even landing. Other flying robots are uninhabited and are known as unmanned aerial vehicles
(UAVs). They can be smaller and lighter without a human pilot on board,
and fly into dangerous territory for military surveillance missions.
Some can even fire on targets under command. UAVs are also being
developed which can fire on targets automatically, without the need for a
command from a human. Other flying robots include cruise missiles, the Entomopter, and the Epson micro helicopter robot.
Robots such as the Air Penguin, Air Ray, and Air Jelly have
lighter-than-air bodies, are propelled by paddles, and are guided by
sonar.
Biomimetic flying robots (BFRs)
A flapping wing BFR generating lift and thrust
BFRs take inspiration from flying mammals, birds, or insects. BFRs
can have flapping wings, which generate the lift and thrust, or they can
be propeller-actuated. BFRs with flapping wings have increased stroke
efficiencies, increased maneuverability, and reduced energy consumption
in comparison to propeller-actuated BFRs. Mammal and bird inspired BFRs share similar flight characteristics and
design considerations. For instance, both mammal and bird inspired BFRs
minimize edge fluttering and pressure-induced wingtip curl
by increasing the rigidity of the wing edge and wingtips. Mammal and
insect inspired BFRs can be impact resistant, making them useful in
cluttered environments.
Mammal inspired BFRs typically take inspiration from bats, but the flying squirrel has also inspired a prototype. Examples of bat inspired BFRs include Bat Bot and the DALER. Mammal inspired BFRs can be designed to be multi-modal; therefore,
they're capable of both flight and terrestrial movement. To reduce the
impact of landing, shock absorbers can be implemented along the wings. Alternatively, the BFR can pitch up and increase the amount of drag it experiences. By increasing the drag force, the BFR will decelerate and minimize the
impact upon grounding. Different land gait patterns can also be
implemented.
Dragonfly inspired BFR.
Bird inspired BFRs can take inspiration from raptors, gulls, and
everything in-between. Bird inspired BFRs can be feathered to increase
the angle of attack range over which the prototype can operate before
stalling. The wings of bird inspired BFRs allow for in-plane deformation, and the
in-plane wing deformation can be adjusted to maximize flight efficiency
depending on the flight gait. An example of a raptor inspired BFR is the prototype by Savastano et al. The prototype has fully deformable flapping wings and is capable of
carrying a payload of up to 0.8 kg while performing a parabolic climb,
steep descent, and rapid recovery. The gull inspired prototype by Grant
et al. accurately mimics the elbow and wrist rotation of gulls, and they
find that lift generation is maximized when the elbow and wrist
deformations are opposite but equal.
Insect inspired BFRs typically take inspiration from beetles or
dragonflies. An example of a beetle inspired BFR is the prototype by
Phan and Park, and a dragonfly inspired BFR is the prototype by Hu et al. The flapping frequency of insect inspired BFRs are much higher than those of other BFRs; this is because of the aerodynamics of insect flight. Insect inspired BFRs are much smaller than those inspired by mammals or
birds, so they are more suitable for dense environments.
Biologically-inspired flying robots
Visualization of entomopter flying on Mars (NASA)
A class of robots that are biologically inspired, but which do not attempt to mimic biology, are creations such as the Entomopter. Funded by DARPA, NASA, the United States Air Force, and the Georgia Tech Research Institute and patented by Prof. Robert C. Michelson for covert terrestrial missions as well as flight in the lower Mars atmosphere, the Entomopter flight propulsion system uses low Reynolds number wings similar to those of the hawk moth
(Manduca sexta), but flaps them in a non-traditional "opposed x-wing
fashion" while "blowing" the surface to enhance lift based on the Coandă effect
as well as to control vehicle attitude and direction. Waste gas from
the propulsion system not only facilitates the blown wing aerodynamics,
but also serves to create ultrasonic emissions like that of a Bat
for obstacle avoidance. The Entomopter and other biologically-inspired
robots leverage features of biological systems, but do not attempt to
create mechanical analogs.
Snaking
Two robot snakes. The left one has 64 motors (with 2 degrees of freedom per segment), the right one 10.
Several snake
robots have been successfully developed. Mimicking the way real snakes
move, these robots can navigate very confined spaces, meaning they may
one day be used to search for people trapped in collapsed buildings. The Japanese ACM-R5 snake robot can even navigate both on land and in water.
Skating
A small number of skating
robots have been developed, one of which is a multi-mode walking and
skating device. It has four legs, with unpowered wheels, which can
either step or roll. Another robot, Plen, can use a miniature skateboard or roller-skates, and skate across a desktop.
Capuchin, a climbing robot
Climbing
Several different approaches have been used to develop robots that
have the ability to climb vertical surfaces. One approach mimics the
movements of a human climber on a wall with protrusions; adjusting the center of mass and moving each limb in turn to gain leverage. An example of this is Capuchin, built by Ruixiang Zhang at Stanford University, California. Another
approach uses the specialized toe pad method of wall-climbing geckoes, which can run on smooth surfaces such as vertical glass. Examples of this approach include Wallbot and Stickybot.
China's Technology Daily reported on 15 November 2008, that Li Hiu Yeung and his research group of New Concept Aircraft (Zhuhai) Co., Ltd. had successfully developed a bionic gecko robot named "Speedy Freelander".
According to Yeung, the gecko robot could rapidly climb up and down a
variety of building walls, navigate through ground and wall fissures,
and walk upside-down on the ceiling. It was also able to adapt to the
surfaces of smooth glass, rough, sticky or dusty walls as well as
various types of metallic materials. It could also identify and
circumvent obstacles automatically. Its flexibility and speed were
comparable to a natural gecko. A third approach is to mimic the motion
of a snake climbing a pole.
Swimming (Piscine)
It is calculated that when swimming some fish can achieve a propulsive efficiency greater than 90%. Furthermore, they can accelerate and maneuver far better than any man-made boat or submarine,
and produce less noise and water disturbance. Therefore, many
researchers studying underwater robots would like to copy this type of
locomotion. Notable examples are the Robotic Fish G9, and Robot Tuna built to analyze and mathematically model thunniform motion. The Aqua Penguin, copies the streamlined shape and propulsion by front "flippers" of penguins. The Aqua Ray and Aqua Jelly emulate the locomotion of manta ray, and jellyfish, respectively.
Robotic Fish: iSplash-II
In 2014, iSplash-II was developed as the first robotic fish
capable of outperforming real carangiform fish in terms of average
maximum velocity (measured in body lengths/ second) and endurance, the
duration that top speed is maintained. This build attained swimming speeds of 11.6BL/s (i.e. 3.7 m/s). The first build, iSplash-I (2014) was the first robotic platform to apply a full-body length carangiform
swimming motion which was found to increase swimming speed by 27% over
the traditional approach of a posterior confined waveform.
Sailboat robots have also been developed in order to make measurements at the surface of the ocean. A typical sailboat robot is Vaimos. Since the propulsion of sailboat robots uses the wind, the energy of
the batteries is only used for the computer, for the communication and
for the actuators (to tune the rudder and the sail). If the robot is
equipped with solar panels, the robot could theoretically navigate
forever. The two main competitions of sailboat robots are WRSC, which takes place every year in Europe, and Sailbot.
Control systems may also have varying levels of autonomy.
Direct interaction is used for haptic or teleoperated devices, and the human has nearly complete control over the robot's motion.
Operator-assist modes have the operator commanding
medium-to-high-level tasks, with the robot automatically figuring out
how to achieve them.
An autonomous robot may go without human interaction for extended
periods of time . Higher levels of autonomy do not necessarily require
more complex cognitive capabilities. For example, robots in assembly
plants are completely autonomous but operate in a fixed pattern.
Another classification takes into account the interaction between human control and the machine motions.
Teleoperation. A human controls each movement, each machine actuator change is specified by the operator.
Supervisory. A human specifies general moves or position changes and the machine decides specific movements of its actuators.
Task-level autonomy. The operator specifies only the task and the robot manages itself to complete it.
Full autonomy. The machine will create and complete all its tasks without human interaction.
Computer vision
is the science and technology of machines that see. As a scientific
discipline, computer vision is concerned with the theory behind
artificial systems that extract information from images. The image data
can take many forms, such as video sequences and views from cameras.
In most practical computer vision applications, the computers are
pre-programmed to solve a particular task, but methods based on
learning are now becoming increasingly common.
Computer vision systems rely on image sensors that detect electromagnetic radiation which is typically in the form of either visible light or infra-red light. The sensors are designed using solid-state physics. The process by which light propagates and reflects off surfaces is explained using optics. Sophisticated image sensors even require quantum mechanics
to provide a complete understanding of the image formation process.
Robots can also be equipped with multiple vision sensors to be better
able to compute the sense of depth in the environment. Like human eyes,
robots' "eyes" must also be able to focus on a particular area of
interest, and also adjust to variations in light intensities.
There is a subfield within computer vision where artificial systems are designed to mimic the processing and behavior of biological system,
at different levels of complexity. Also, some of the learning-based
methods developed within computer vision have a background in biology.
Though a significant percentage of robots in commission today are
either human controlled or operate in a static environment, there is an
increasing interest in robots that can operate autonomously in a dynamic
environment. These robots require some combination of navigation hardware and software
in order to traverse their environment. In particular, unforeseen
events (e.g. people and other obstacles that are not stationary) can
cause problems or collisions. Some highly advanced robots such as ASIMO and Meinü robot have particularly good robot navigation hardware and software. Also, self-controlled cars, Ernst Dickmanns' driverless car, and the entries in the DARPA Grand Challenge,
are capable of sensing the environment well and subsequently making
navigational decisions based on this information, including by a swarm
of autonomous robots. Most of these robots employ a GPS navigation device with waypoints, along with radar, sometimes combined with other sensory data such as lidar, video cameras, and inertial guidance systems for better navigation between waypoints.
The state of the art in sensory intelligence for robots will have to
progress through several orders of magnitude if we want the robots
working in our homes to go beyond vacuum-cleaning the floors. If robots
are to work effectively in homes and other non-industrial environments,
the way they are instructed to perform their jobs, and especially how
they will be told to stop will be of critical importance. The people who
interact with them may have little or no training in robotics, and so
any interface will need to be extremely intuitive. Science fiction
authors also typically assume that robots will eventually be capable of
communicating with humans through speech, gestures, and facial expressions, rather than a command-line interface.
Although speech would be the most natural way for the human to
communicate, it is unnatural for the robot. It will probably be a long
time before robots interact as naturally as the fictional C-3PO, or Data of Star Trek, Next Generation.
Even though the current state of robotics cannot meet the standards of
these robots from science-fiction, robotic media characters (e.g.,
Wall-E, R2-D2) can elicit audience sympathies that increase people's
willingness to accept actual robots in the future. Acceptance of social robots is also likely to increase if people can
meet a social robot under appropriate conditions. Studies have shown
that interacting with a robot by looking at, touching, or even imagining
interacting with the robot can reduce negative feelings that some
people have about robots before interacting with them. However, if pre-existing negative sentiments are especially strong,
interacting with a robot can increase those negative feelings towards
robots.
Interpreting the continuous flow of sounds coming from a human, in real time, is a difficult task for a computer, mostly because of the great variability of speech. The same word, spoken by the same person may sound different depending on local acoustics, volume, the previous word, whether or not the speaker has a cold, etc.. It becomes even harder when the speaker has a different accent. Nevertheless, great strides have been made in the field since Davis,
Biddulph, and Balashek designed the first "voice input system" which
recognized "ten digits spoken by a single user with 100% accuracy" in
1952. Currently, the best systems can recognize continuous, natural speech, up to 160 words per minute, with an accuracy of 95%. With the help of artificial intelligence, machines nowadays can use people's voice to identify their emotions such as satisfied or angry.
Robotic voice
Other hurdles exist when allowing the robot to use voice for interacting with humans. For social reasons, synthetic voice proves suboptimal as a communication medium, making it necessary to develop the emotional component of robotic voice through various techniques. An advantage of diphonic branching is the emotion that the robot is
programmed to project, can be carried on the voice tape, or phoneme,
already pre-programmed onto the voice media. One of the earliest
examples is a teaching robot named Leachim developed in 1974 by Michael J. Freeman. Leachim was able to convert digital memory to rudimentary verbal speech on pre-recorded computer discs. It was programmed to teach students in The Bronx, New York.
Facial expressions can provide rapid feedback on the progress of a
dialog between two humans, and soon may be able to do the same for
humans and robots. Robotic faces have been constructed by Hanson Robotics using their elastic polymer called Frubber,
allowing a large number of facial expressions due to the elasticity of
the rubber facial coating and embedded subsurface motors (servos). The coating and servos are built on a metal skull. A robot should know how to approach a human, judging by their facial expression and body language.
Whether the person is happy, frightened, or crazy-looking affects the
type of interaction expected of the robot. Likewise, robots like Kismet and the more recent addition, Nexi can produce a range of facial expressions, allowing it to have meaningful social exchanges with humans.
One can imagine, in the future, explaining to a robot chef how to
make a pastry, or asking directions from a robot police officer. In both
of these cases, making hand gestures
would aid the verbal descriptions. In the first case, the robot would
be recognizing gestures made by the human, and perhaps repeating them
for confirmation. In the second case, the robot police officer would
gesture to indicate "down the road, then turn right". It is likely that
gestures will make up a part of the interaction between humans and
robots. A great many systems have been developed to recognize human hand gestures.
Proxemics
Proxemics is the study of personal space, and HRI systems may try to model and work with its concepts for human interactions.
Artificial emotions
Artificial emotions can also be generated, composed of a sequence of facial expressions or gestures. As can be seen from the movie Final Fantasy: The Spirits Within,
the programming of these artificial emotions is complex and requires a
large amount of human observation. To simplify this programming in the
movie, presets were created together with a special software program.
This decreased the amount of time needed to make the film. These presets
could possibly be transferred for use in real-life robots. An example
of a robot with artificial emotions is Robin the Robot [hy] developed by an Armenian
IT company Expper Technologies, which uses AI-based peer-to-peer
interaction. Its main task is achieving emotional well-being, i.e.
overcome stress and anxiety. Robin was trained to analyze facial
expressions and use his face to display his emotions given the context.
The robot has been tested by kids in US clinics, and observations show
that Robin increased the appetite and cheerfulness of children after
meeting and talking.
Personality
Many of the robots of science fiction have a personality, something which may or may not be desirable in the commercial robots of the future. Nevertheless, researchers are trying to create robots which appear to have a personality: i.e. they use sounds, facial expressions, and body language to try to
convey an internal state, which may be joy, sadness, or fear. One
commercial example is Pleo, a toy robot dinosaur, which can exhibit several apparent emotions.
Much of the research in robotics focuses not on specific industrial tasks, but on investigations into new types of robots, alternative ways to think about or design robots, and new ways to manufacture them. Other investigations, such as MIT's cyberflora project, are almost wholly academic.
To describe the level of advancement of a robot, the term "Generation Robots" can be used. This term is coined by Professor Hans Moravec, Principal Research Scientist at the Carnegie Mellon UniversityRobotics Institute in describing the near future evolution of robot technology. First-generation robots, Moravec predicted in 1997, should have an intellectual capacity comparable to perhaps a lizard and should become available by 2010. Because the first generation robot would be incapable of learning, however, Moravec predicts that the second generation robot would be an improvement over the first and become available by 2020, with the intelligence maybe comparable to that of a mouse. The third generation robot should have intelligence comparable to that of a monkey. Though fourth generation robots, robots with human intelligence, professor Moravec predicts, would become possible, he does not predict this happening before around 2040 or 2050.
The study of motion can be divided into kinematics and dynamics. Direct kinematics or forward kinematics refers to the calculation of end effector position, orientation, velocity, and acceleration when the corresponding joint values are known. Inverse kinematics
refers to the opposite case in which required joint values are
calculated for given end effector values, as done in path planning. Some
special aspects of kinematics include handling of redundancy (different
possibilities of performing the same movement), collision avoidance, and singularity avoidance. Once all relevant positions, velocities, and accelerations have been calculated using kinematics, methods from the field of dynamics are used to study the effect of forces
upon these movements. Direct dynamics refers to the calculation of
accelerations in the robot once the applied forces are known. Direct
dynamics is used in computer simulations of the robot. Inverse dynamics
refers to the calculation of the actuator forces necessary to create a
prescribed end-effector acceleration. This information can be used to
improve the control algorithms of a robot.
In each area mentioned above, researchers strive to develop new
concepts and strategies, improve existing ones, and improve the
interaction between these areas. To do this, criteria for "optimal"
performance and ways to optimize design, structure, and control of
robots must be developed and implemented.
Open source robotics
research seeks standards for defining, and methods for designing and
building, robots so that they can easily be reproduced by anyone.
Research includes legal and technical definitions; seeking out
alternative tools and materials to reduce costs and simplify builds; and
creating interfaces and standards for designs to work together. Human
usability research also investigates how to best document builds through
visual, text or video instructions.
Evolutionary robotics
Evolutionary robots is a methodology that uses evolutionary computation to help design robots, especially the body form, or motion and behavior controllers. In a similar way to natural evolution, a large population of robots is allowed to compete in some way, or their ability to perform a task is measured using a fitness function.
Those that perform worst are removed from the population and replaced
by a new set, which have new behaviors based on those of the winners.
Over time the population improves, and eventually a satisfactory robot
may appear. This happens without any direct programming of the robots by
the researchers. Researchers use this method both to create better
robots, and to explore the nature of evolution. Because the process often requires many generations of robots to be simulated, this technique may be run entirely or mostly in simulation, using a robot simulator software package, then tested on real robots once the evolved algorithms are good enough. According to the International Federation of Robotics (IFR) study World Robotics 2023, there were about 4,281,585 operational industrial robots by the end of 2023.
Bionics and biomimetics
Bionics and biomimetics apply the physiology and methods of locomotion of animals to the design of robots. For example, the design of BionicKangaroo was based on the way kangaroos jump.
Swarm robotics
Swarm robotics
is an approach to the coordination of multiple robots as a system which
consist of large numbers of mostly simple physical robots. ″In a robot
swarm, the collective behavior of the robots results from local
interactions between the robots and between the robots and the
environment in which they act.″
Quantum computing
There has been some research into whether robotics algorithms can be run more quickly on quantum computers than they can be run on digital computers. This area has been referred to as quantum robotics.
Robotics engineers design robots, maintain them, develop new
applications for them, and conduct research to expand the potential of
robotics. Robots have become a popular educational tool in some middle and high schools, particularly in parts of the USA, as well as in numerous youth summer camps, raising interest in
programming, artificial intelligence, and robotics among students.
Employment
A robot technician builds small all-terrain robots (courtesy: MobileRobots, Inc.).
Robotics is an essential component in many modern manufacturing
environments. As factories increase their use of robots, the number of
robotics–related jobs grow and have been observed to be steadily rising. The employment of robots in industries has increased productivity and
efficiency savings and is typically seen as a long-term investment for
benefactors. A study found that 47 percent of US jobs are at risk to
automation "over some unspecified number of years". These claims have been criticized on the ground that social policy, not AI, causes unemployment. In a 2016 article in The Guardian, Stephen Hawking stated "The
automation of factories has already decimated jobs in traditional
manufacturing, and the rise of artificial intelligence is likely to
extend this job destruction deep into the middle classes, with only the
most caring, creative or supervisory roles remaining". The rise of robotics is thus often used as an argument for universal basic income.
According to a GlobalData September 2021 report, the robotics
industry was worth $45bn in 2020, and by 2030, it will have grown at a
compound annual growth rate (CAGR) of 29% to $568bn, driving jobs in
robotics and related industries.
A discussion paper drawn up by EU-OSHA highlights how the spread of robotics presents both opportunities and challenges for occupational safety and health (OSH).
The greatest OSH benefits stemming from the wider use of robotics
should be substitution for people working in unhealthy or dangerous
environments. In space, defense, security, or the nuclear industry, but
also in logistics, maintenance, and inspection, autonomous robots are
particularly useful in replacing human workers performing dirty, dull or
unsafe tasks, thus avoiding workers' exposures to hazardous agents and
conditions and reducing physical, ergonomic and psychosocial risks. For
example, robots are already used to perform repetitive and monotonous
tasks, to handle radioactive material or to work in explosive
atmospheres. In the future, many other highly repetitive, risky or
unpleasant tasks will be performed by robots in a variety of sectors
like agriculture, construction, transport, healthcare, firefighting or
cleaning services.
Moreover, there are certain skills to which humans will be better
suited than machines for some time to come and the question is how to
achieve the best combination of human and robot skills. The advantages
of robotics include heavy-duty jobs with precision and repeatability,
whereas the advantages of humans include creativity, decision-making,
flexibility, and adaptability. This need to combine optimal skills has
resulted in collaborative robots
and humans sharing a common workspace more closely and led to the
development of new approaches and standards to guarantee the safety of
the "man-robot merger". Some European countries are including robotics
in their national programs and trying to promote a safe and flexible
cooperation between robots and operators to achieve better productivity.
For example, the German Federal Institute for Occupational Safety and
Health (BAuA) organizes annual workshops on the topic "human-robot collaboration".
In the future, cooperation between robots and humans will be
diversified, with robots increasing their autonomy and human-robot
collaboration reaching completely new forms. Current approaches and
technical standards aiming to protect employees from the risk of working with collaborative robots will have to be revised.
User experience
Great user experience predicts the needs, experiences, behaviors,
language and cognitive abilities, and other factors of each user group.
It then uses these insights to produce a product or solution that is
ultimately useful and usable. For robots, user experience begins with an
understanding of the robot's intended task and environment, while
considering any possible social impact the robot may have on human
operations and interactions with it.
It defines that communication as the transmission of information
through signals, which are elements perceived through touch, sound,
smell and sight. The author states that the signal connects the sender to the receiver
and consists of three parts: the signal itself, what it refers to, and
the interpreter. Body postures and gestures, facial expressions, hand
and head movements are all part of nonverbal behavior and communication.
Robots are no exception when it comes to human-robot interaction.
Therefore, humans use their verbal and nonverbal behaviors to
communicate their defining characteristics. Similarly, social robots
need this coordination to perform human-like behaviors.
Careers
Robotics is an interdisciplinary field, combining primarily mechanical engineering and computer science but also drawing on electronic engineering
and other subjects. The usual way to build a career in robotics is to
complete an undergraduate degree in one of these established subjects,
followed by a graduate (masters') degree in Robotics. Graduate degrees
are typically joined by students coming from all of the contributing
disciplines, and include familiarization of relevant undergraduate level
subject matter from each of them, followed by specialist study in pure
robotics topics which build upon them. As an interdisciplinary subject,
robotics graduate programmes tend to be especially reliant on students
working and learning together and sharing their knowledge and skills
from their home discipline first degrees.
Robotics industry careers then follow the same pattern, with most
roboticists working as part of interdisciplinary teams of specialists
from these home disciplines followed by the robotics graduate degrees
which enable them to work together. Workers typically continue to
identify as members of their home disciplines who work in robotics,
rather than as 'roboticists'. This structure is reinforced by the nature
of some engineering professions, which grant chartered engineer status
to members of home disciplines rather than to robotics as a whole.
Robotics careers are widely predicted to grow in the 21st
century, as robots replace more manual and intellectual human work. Some
workers who lose their jobs to robotics may be well-placed to retrain
to build and maintain these robots, using their domain-specific
knowledge and skills.
A wooden, steam-propelled bird, which was able to fly
Flying pigeon
Archytas of Tarentum
Third century B.C. and earlier
One of the earliest descriptions of automata appears in the Lie Zi text, on a much earlier encounter between King Mu of Zhou
(1023–957 BC) and a mechanical engineer known as Yan Shi, an
'artificer'. The latter allegedly presented the king with a life-size,
human-shaped figure of his mechanical handiwork.
Yan Shi (Chinese: 偃师)
First century A.D. and earlier
Descriptions of more than 100 machines and automata, including a
fire engine, a wind organ, a coin-operated machine, and a steam-powered
engine, in Pneumatica and Automata by Heron of Alexandria
Leonardo Torres Quevedo presented the Telekino at the Paris Academy of Science, a radio-based control system with different operational states, for testing airships without risking human lives. He conduct the initial test controlling a tricycle almost 100 feet
away, being the first example of a radio-controlled unmanned ground
vehicle.
Leonardo Torres Quevedo builds the first truly autonomous machine capable of playing chess. As opposed to the human-operated The Turk and Ajeeb, El Ajedrecista had an integrated automaton built to play chess without human guidance. It only played an endgame with three chess pieces, automatically moving a white king and a rook to checkmate the black king moved by a human opponent.
In his paper Essays on Automatics published in 1914, Leonardo
Torres Quevedo proposed a machine that makes "judgments" using sensors
that capture information from the outside, parts that manipulate the
outside world like arms, power sources such as batteries and air
pressure, and most importantly, captured information and past
information. It was defined as an organism that can control reactions in
response to external information and adapt to changes in the
environment to change its behavior.
First installed industrial robot. The first digitally operated and programmable robot, Unimate, was installed in 1961 to lift hot pieces of metal from a die casting machine and stack them.
First full-scale humanoid intelligent robot, and first android.
Its limb control system allowed it to walk with the lower limbs, and to
grip and transport objects with its hands, using tactile sensors. Its
vision system allowed it to measure distances and directions to objects
using external receptors, artificial eyes, and ears. And its
conversation system allowed it to communicate with a person in Japanese,
with an artificial mouth.
The world's first microcomputer
controlled electric industrial robot, IRB 6 from ASEA, was delivered to
a small mechanical engineering company in southern Sweden. The design
of this robot had been patented in 1972.
First multitasking, the parallel programming language used for robot
control. It was the Event Driven Language (EDL) on the IBM/Series/1
process computer, with the implementation of both inter-process communication (WAIT/POST) and mutual exclusion (ENQ/DEQ) mechanisms for robot control.
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