This paper proposes a general plan and coordination strategy for robot system. The state space for robot system is constructed according to the task requirement and system characteristic. Reachable state of the system is figured out by the system’s internal and external constraints. Task plan and coordination are then transformed as trajectory solving problem in the state space, by which the realizable conditions for the given task are discussed. If the task is realizable, the optimal strategy for task execution could be investigated and obtained in state space. Otherwise, it could be transformed to be realizable via adjusting the system configuration and/or task constraint, and the transformation condition could also be determined. This contributes to design, plan, and coordination of the robotic tasks. Experiments of the manipulator path planning and multi-robot formation movement are conducted to show the validity and generalization of the proposed method.
Teleoperated networked robot often has unpredictable behaviors due to uncertain time delay from data transmission over Internet. The robot cannot accomplish the desired actions of the remote operator in time, which severely impairs reliability and efficiency of the robot system. This paper investigated a novel approach, learning user intention, to compensate the uncertain time delay with the autonomy of a mobile robot. The user intention to control and operate the robot was modeled and incrementally inferred based on Bayesian techniques so that the desired actions could be recognized and completed by the robot autonomously. Thus the networked robot is able to fulfill the task assigned without frequent interaction with the user, which decreases data transmission and improves the efficiency of the whole system. Experimental results show the validity and feasibility of the proposed method.
