An Efficient Approach for Solving Robotic Task Sequencing Problems Considering Spatial Constraint

Abstract

In many industrial applications, the robot is required to perform a set of repetitive tasks without collision as quickly as possible to maximize productivity. It is essential to find an optimal sequence of collision-free motions to visit a set of repetitive tasks and determine the optimal robot configuration used to complete each task, which is formulated as the Robotic Task Sequencing Problem (RTSP). In this paper, we propose an approach based on a typical decoupling strategy to solve RTSP efficiently. Firstly, the task execution sequence is obtained by solving a TSP in task space and candidates of the optimal configuration for each task are selected from the collision-free configuration space according to the self-designed optimality value derived from the similarity to the initial configuration in configuration space. Then the optimal configuration for each task is determined by finding the shortest path in a graph that is constructed according to the task execution sequence and optimal configuration candidates. Finally, collision-free motion trajectories required for the robot to complete each task with the optimal configuration are generated by running a motion planning algorithm. Through a series of experiments, we show that our approach outperforms the state-of-the-art approaches when applied to the RTSP instances in a cluttered 3D environment, with up to 29.6% reduction in computation time while providing comparable performance.