Robotics research
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 but other investigations, such as MIT's
cyberflora project, are almost wholly academic.
A first particular new innovation in robot design is the opensourcing of robot-projects. 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 the 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.
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The second is
Evolutionary Robots. This 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, then tested on real robots once the evolved algorithms are good enough.
Currently, there are about 1 million industrial robots toiling around the world, and Japan is the top country having high density of utilizing robots in its manufacturing industry.
Dynamics and kinematics
The study of motion can be divided into
kinematics and
dynamics. Direct 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.
