Refining Two Robots Task Execution Through Tuning Behavior Trajectory and Balancing the Communication

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.

[1]  Richard P. Paul,et al.  An On-Line Dynamic Trajectory Generator , 1984 .

[2]  A. Nikoobin,et al.  Maximum allowable dynamic load of flexible mobile manipulators using finite element approach , 2008 .

[3]  Lydia E. Kavraki,et al.  Probabilistic roadmaps for path planning in high-dimensional configuration spaces , 1996, IEEE Trans. Robotics Autom..

[4]  Shital S. Chiddarwar,et al.  Conflict free coordinated path planning for multiple robots using a dynamic path modification sequence , 2011, Robotics Auton. Syst..

[5]  Jun Ota,et al.  Teaching Tasks to Multiple Small Robots by Classifying and Splitting a Human Example , 2017, J. Robotics Mechatronics.

[6]  Lynne E. Parker,et al.  ALLIANCE: an architecture for fault tolerant multirobot cooperation , 1998, IEEE Trans. Robotics Autom..

[7]  S. Zucker,et al.  Toward Efficient Trajectory Planning: The Path-Velocity Decomposition , 1986 .

[8]  Jun Ota,et al.  Fast grasping of unknown objects through automatic determination of the required number of mobile robots , 2013, Adv. Robotics.

[9]  Kimitoshi Yamazaki,et al.  Furniture Model Creation Through Direct Teaching to a Mobile Robot , 2008, J. Robotics Mechatronics.

[10]  Çetin Meriçli,et al.  Task Refinement for Autonomous Robots Using Complementary Corrective Human Feedback , 2011 .

[11]  A. Nikoobin,et al.  Maximum load-carrying capacity of autonomous mobile manipulator in an environment with obstacle considering tip over stability , 2010 .

[12]  Lars Nielsen,et al.  Torque-limited path following by online trajectory time scaling , 1990, IEEE Trans. Robotics Autom..