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Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 3, Issue 3, Pages 466-487, DOI: https://doi.org/10.21307/ijssis-2017-403
License : (CC BY-NC-ND 4.0)
Published Online: 13-December-2017
If the theory of affordance is applied to a robot, performing the whole process of recognition and planning is not always required in its computer. Since the tactile sensing of a robot is important to perform any task, we focus on tactile sensing and introduce a new concept called the artificial tactile affordance system (ATAS). Its basic idea is the implementation of a recurrent mechanism in which
information obtained from the object and the behavior performed by the robot’s inducing the next behavior. We intend to implement ATAS based on the following two methodologies: (1) after each rule is transformed into an algorithm, a program module is coded based on the algorithm; ATAS is composed of several program modules, and a module is selected from the set of modules based on
sensor information; (2) a set of rules is expressed as a table composed of sensor input columns and behavior output columns, and the table rows correspond to rules; since each rule is transformed to a string of 0 and 1, we treat a long string composed of rule strings as a gene to obtain an optimum gene that adapts to its environment using a genetic algorithm (GA). For methodology 1, we established an ATAS composed of 3 to 5 modules to accomplish such tasks as object grasping, pick and place, cap
screwing, and assembling. Using methodology 1, a two-hand-arm robot equipped with an optical threeaxis
tactile sensor performed the above tasks. For methodology 2, we propose the Evolutionary
Behavior Table System (EBTS) that uses a GA to acquire the autonomous cooperation behavior of
multiple mobile robots. In validation experiments, three agents equipped with behavior tables conveyed
an object to a specified goal with higher scores than the four-agent condition. Since the redundant
agent does not interrupt the other agents, the agent acquires the collective behavior of not interrupting
other agents based on its environment information. Methodology 1 is very effective for such fine
control as handling tasks of humanoid robots, and methodology 2 is very useful to obtain general
robotic behavior that is suitable for the environment.
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