Robotics Part II

 

Biologically-inspired flying robots

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 Essex University Computer Science Robotic Fish G9, and the Robot Tuna built by the Institute of Field Robotics, to analyze and mathematically model thunniform motion. The Aqua Penguin, designed and built by Festo of Germany, copies the streamlined shape and propulsion by front "flippers" of penguins. Festo have also built the Aqua Ray and Aqua Jelly, which emulate the locomotion of manta ray, and jellyfish, respectively.

Robotic Fish: iSplash-II

In 2014, iSplash-II was developed by PhD student Richard James Clapham and Prof. Huosheng Hu at Essex University. It was 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.

Sailing

Sailboat robots have also been developed in order to make measurements at the surface of the ocean. A typical sailboat robot is Vaimos built by IFREMER and ENSTA-Bretagne. 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

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. 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 developed by Michael Short and colleagues at the University of Sunderland in the UK in 2000. The robot 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.

Automation

Direct interaction is used for haptic or teleoperated devices and the human have 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.

Vision

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.

Environmental interaction and navigation

Radar, GPS, and lidar, are all combined to provide proper navigation and obstacle avoidance (vehicle developed for 2007 DARPA Urban Challenge).

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.

Human-robot interaction

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.

Speech recognition

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.

Gestures

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.

Facial expression

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.

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 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.

Proxemics

Proxemics is the study of personal space, and HRI systems may try to model and work with its concepts for human interactions.

Research robotics

Two Jet Propulsion Laboratory engineers stand with three vehicles, providing a size comparison of three generations of Mars rovers. 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.

Sojourner is 65 cm (2.13 ft) long. The Mars Exploration Rovers (MER) is 1.6 m (5.2 ft) long. Curiosity on the right is 3 m (9.8 ft) long.

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 University Robotics 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.

Dynamics and kinematics

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 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

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 are 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. Currently, there are about 10 million industrial robots toiling around the world, and Japan is the top country having high density of utilizing robots in its manufacturing industry.

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 consists 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.

Other research areas

Human factors

Education and training

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

Robotics is an essential component in many modern manufacturing environments. As factories increase their use of robots, the number of robotics–related jobs grows and has 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".

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.

Occupational safety and health implications

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. 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.

History

In 1948, Norbert Wiener formulated the principles of cybernetics, the basis of practical robotics.

Fully autonomous robots only appeared in the second half of the 20th century. The first digitally operated and programmable robot, the Unimate, was installed in 1961 to lift hot pieces of metal from a die casting machine and stack them. Commercial and industrial robots are widespread today and used to perform jobs more cheaply, more accurately, and more reliably than humans. They are also employed in some jobs that are too dirty, dangerous, or dull to be suitable for humans. Robots are widely used in manufacturing, assembly, packing and packaging, mining, transport, earth and space exploration, surgery, weaponry, laboratory research, safety, and the mass production of consumer and industrial goods.

Notes

 One database, developed by the United States Department of Energy, contains information on almost 500 existing robotic technologies.