Refining Two Robots Task Execution Through Tuning Behavior Trajectory and Balancing the Communication
Author(s) -
Jorge David Figueroa Heredia,
Shouhei Shirafuji,
Hamdi Sahloul,
Jose Ildefonso U. Rubrico,
Taiki Ogata,
Tatsunori Hara,
Jun Ota
Publication year - 2018
Publication title -
journal of robotics and mechatronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.257
H-Index - 19
eISSN - 1883-8049
pISSN - 0915-3942
DOI - 10.20965/jrm.2018.p0613
Subject(s) - computer science , robot , trajectory , task (project management) , set (abstract data type) , reduction (mathematics) , object (grammar) , execution time , scale (ratio) , artificial intelligence , real time computing , simulation , distributed computing , engineering , systems engineering , geometry , quantum mechanics , programming language , physics , astronomy , mathematics
A method for modifying robot behaviors is introduced to improve robot performance during the execution of object manipulation tasks. The purpose of this method is to minimize the execution time of tasks and prevent collision with obstacles, including objects to be manipulated and the robot itself, by considering two approaches. The first is to use the potential that robots can provide, considering that the programs are based on events that are subject to the response of sensors. The second is to determine the maximum rate at which commands can be sent, without affecting the responses from the sensors, and, based on that, to accelerate or decelerate the execution of the task. The proposed method focuses on the refinement of two approaches: (a) modifying the trajectory of some behaviors, so that they are not executed step by step, but are executed in parallel, and (b) increasing the rate of sending robotic commands. To validate the proposed method, four real-world tasks are presented, including the flipping of a briefcase, the flipping of a weighing scale, the lifting of a weighing scale, and the opening of a folding chair, performed by a set of small robots. The reduction in execution time of the tasks varied between 54.2% and 73.6%; the implications of the improvement are discussed based on experimental results.
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