Robot locomotion

Robot locomotion is the collective name for the various methods that robots use to transport themselves from place to place.

Wheeled robots are typically quite energy efficient and simple to control. However, other forms of locomotion may be more appropriate for a number of reasons, for example traversing rough terrain, as well as moving and interacting in human environments. Furthermore, studying bipedal and insect-like robots may beneficially impact on biomechanics.

A major goal in this field is in developing capabilities for robots to autonomously decide how, when, and where to move. However, coordinating a large number of robot joints for even simple matters, like negotiating stairs, is difficult. Autonomous robot locomotion is a major technological obstacle for many areas of robotics, such as humanoids (like Honda's Asimo).

Types of locomotion

Walking

Walking robots simulate human or animal motion, as a replacement for wheeled motion. Legged motion makes it possible to negotiate uneven surfaces, steps, and other areas that would be difficult for a wheeled robot to reach, as well as causes less damage to environmental terrain as wheeled robots, which would erode it.

Hexapod robots are based on insect locomotion, most popularly the cockroach and stick insect, whose neurological and sensory output is less complex than other animals. Multiple legs allow several different gaits, even if a leg is damaged, making their movements more useful in robots transporting objects.

Leg mechanism
A leg mechanism (walking mechanism) is an assembly of links and joints (a linkage) intended to simulate the walking motion of humans or animals. Mechanical legs can have one or more actuators, and can perform simple planar or complex motion.

Compared to a wheel, a leg mechanism is potentially better fitted to uneven terrain, as it can step over obstacles.

Hexapod
A six-legged walking robot should not be confused with a Stewart platform, a kind of parallel manipulator used in robotics applications.

A hexapod robot is a mechanical vehicle that walks on six legs. Since a robot can be statically stable on three or more legs, a hexapod robot has a great deal of flexibility in how it can move. If legs become disabled, the robot may still be able to walk. Furthermore, not all of the robot's legs are needed for stability; other legs are free to reach new foot placements or manipulate a payload.

Many hexapod robots are biologically inspired by Hexapoda locomotion. Hexapods may be used to test biological theories about insect locomotion, motor control, and neurobiology.

Bipedal walking

Passive dynamics
Passive dynamics refers to the dynamical behavior of actuators, robots, or organisms when not drawing energy from a supply (e.g., batteries, fuel, ATP). Depending on the application, considering or altering the passive dynamics of a powered system can have drastic effects on performance, particularly energy economy, stability, and task bandwidth. Devices using no power source are considered "passive", and their behavior is fully described by their passive dynamics.

In some fields of robotics (legged robotics in particular), design and more relaxed control of passive dynamics has become a complementary (or even alternative) approach to joint-positioning control methods developed through the 20th century. Additionally, the passive dynamics of animals have been of interest to biomechanists and integrative biologists, as these dynamics often underlie biological motions and couple with neuromechanical control.

Particularly relevant fields for investigating and engineering passive dynamics include legged locomotion and manipulation.

Zero moment point
Zero moment point is a concept related with dynamics and control of legged locomotion, e.g., for humanoid robots. It specifies the point with respect to which dynamic reaction force at the contact of the foot with the ground does not produce any moment in the horizontal direction, i.e. the point where the total of horizontal inertia and gravity forces equals 0 (zero). The concept assumes the contact area is planar and has sufficiently high friction to keep the feet from sliding.

Running

Examples: ASIMO, BigDog, HUBO 2, RunBot, and Toyota Partner Robot.

Rolling

In terms of energy efficiency on flat surfaces, wheeled robots are the most efficient. This is because an ideal rolling (but not slipping) wheel loses no energy. A wheel rolling at a given velocity needs no input to maintain its motion. This is in contrast to legged robots which suffer an impact with the ground at heelstrike and lose energy as a result.

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.

Examples: Boe-Bot, Cosmobot, Elmer, Elsie, Enon, HERO, IRobot Create, iRobot's Roomba, Johns Hopkins Beast, Land Walker, Modulus robot, Musa, Omnibot, PaPeRo, Phobot, Pocketdelta robot, Push the Talking Trash Can, RB5X, Rovio, Seropi, Shakey the robot, Sony Rolly, Spykee, TiLR, Topo, TR Araña, and Wakamaru.

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 generalised 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.

Examples:

The MIT cheetah cub is an electrically powered quadruped robot with passive compliant legs capable of self-stabilizing in large range of speeds.

The Tekken II is a small quadruped designed to walk on irregular terrains adaptively.

Metachronal motion

Coordinated, sequential mechanical action having the appearance of a traveling wave is called a metachronal rhythm or wave, and is employed in nature by ciliates for transport, and by worms and arthropods for locomotion.

Slithering

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.

Examples: Snake-arm robot, Roboboa, and Snakebot.

Swimming

An autonomous underwater vehicle (AUV) is a robot that travels underwater without requiring input from an operator. AUVs constitute part of a larger group of undersea systems known as unmanned underwater vehicles, a classification that includes non-autonomous remotely operated underwater vehicles (ROVs) – controlled and powered from the surface by an operator/pilot via an umbilical or using remote control. In military applications a AUV is more often referred to as unmanned undersea vehicle (UUV). Underwater gliders are a subclass of AUVs.

Brachiating

Brachiation allows robots to travel by swinging, using energy only to grab and release surfaces. This motion is similar to an ape swinging from tree to tree. The two types of brachiation can be compared to bipedal walking motions (continuous contact) or running (richochetal). Continuous contact is when a hand/grasping mechanism is always attached to the surface being crossed; richochetal employs a phase of aereal "flight" from one surface/limb to the next.

Hybrid

Robots can also be designed to perform locomotion in multiple modes. For example, the Bipedal Snake Robo can both slither like a snake and walk like a biped robot.

Approaches

Gait engineering

Product optimization
Production optimization is the practice of making changes or adjustments to a product to make it more desirable.

A product has a number of attributes. For example, a soda bottle can have different packaging variations, flavors, nutritional values. It is possible to optimize a product by making minor adjustments. Typically, the goal is to make the product more desirable and to increase marketing metrics such as Purchase Intent, Believability, Frequency of Purchase, etc.

Motion planning
Motion planning (also known as the navigation problem or the piano mover's problem) is a term used in robotics for the process of breaking down a desired movement task into discrete motions that satisfy movement constraints and possibly optimize some aspect of the movement.

For example, consider navigating a mobile robot inside a building to a distant waypoint. It should execute this task while avoiding walls and not falling down stairs. A motion planning algorithm would take a description of these tasks as input, and produce the speed and turning commands sent to the robot's wheels. Motion planning algorithms might address robots with a larger number of joints (e.g., industrial manipulators), more complex tasks (e.g. manipulation of objects), different constraints (e.g., a car that can only drive forward), and uncertainty (e.g. imperfect models of the environment or robot).

Motion planning has several robotics applications, such as autonomy, automation, and robot design in CAD software, as well as applications in other fields, such as animating digital characters, video game, artificial intelligence, architectural design, robotic surgery, and the study of biological molecules.

Motion capture may be performed on humans, insects and other organisms.
Motion capture (sometimes referred as mo-cap or mocap, for short) is the process of recording the movement of objects or people. It is used in military, entertainment, sports, medical applications, and for validation of computer vision and robotics. In filmmaking and video game development, it refers to recording actions of human actors, and using that information to animate digital character models in 2D or 3D computer animation. When it includes face and fingers or captures subtle expressions, it is often referred to as performance capture. In many fields, motion capture is sometimes called motion tracking, but in filmmaking and games, motion tracking usually refers more to match moving.

In motion capture sessions, movements of one or more actors are sampled many times per second. Whereas early techniques used images from multiple cameras to calculate 3D positions, often the purpose of motion capture is to record only the movements of the actor, not his or her visual appearance. This animation data is mapped to a 3D model so that the model performs the same actions as the actor. This process may be contrasted with the older technique of rotoscoping, as seen in Ralph Bakshi's The Lord of the Rings (1978) and American Pop (1981). The animated character movements were achieved in these films by tracing over a live-action actor, capturing the actor's motions and movements. To explain, an actor is filmed performing an action, and then the recorded film is projected onto an animation table frame-by-frame. Animators trace the live-action footage onto animation cels, capturing the actor's outline and motions frame-by-frame, and then they fill in the traced outlines with the animated character. The completed animation cels are then photographed frame-by-frame, exactly matching the movements and actions of the live-action footage. The end result of which is that the animated character replicates exactly the live-action movements of the actor. However, this process takes a considerable amount of time and effort.

Camera movements can also be motion captured so that a virtual camera in the scene will pan, tilt or dolly around the stage driven by a camera operator while the actor is performing. At the same time, the motion capture system can capture the camera and props as well as the actor's performance. This allows the computer-generated characters, images and sets to have the same perspective as the video images from the camera. A computer processes the data and displays the movements of the actor, providing the desired camera positions in terms of objects in the set. Retroactively obtaining camera movement data from the captured footage is known as match moving or camera tracking.

Machine learning, typically with reinforcement learning.
Machine learning (ML) is a field of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) from data, without being explicitly programmed.

The name machine learning was coined in 1959 by Arthur Samuel. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders, and computer vision.

Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning.

Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to "produce reliable, repeatable decisions and results" and uncover "hidden insights" through learning from historical relationships and trends in the data.

Source from Wikipedia