Task Planning of Space-Robot Clusters Based on Modified Differential Evolution Algorithm

Abstract

This study studies the problem of on-orbit maintenance task planning for space-robot clusters. Aiming at the problem of low maintenance efficiency of space-robot cluster task-planning, this study proposes a cluster-task-planning method based on energy and path optimization. First, by introducing the penalty-function method, the task planning problem of the space-robot cluster under limited energy is analyzed, and the optimal-path model for task planning with comprehensive optimization of revenue and energy consumption are constructed; then, the maintenance task points are clustered to reduce the scale of the problem, thus reducing the difficulty of solving the problem; finally, a modified differential evolution algorithm is proposed to solve the problem of space-robot cluster task-planning, improve the performance of space-robot cluster task-assignment and path planning. Simulation results show that the proposed optimal-path model of space-robot cluster and the modified differential evolution algorithm can effectively solve the task-planning problem of spatial robot clusters.

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

“…Vesterstrom et al [205] conducted the experiments over numerical benchmarks and demonstrated the DE has a better performance compared to GA and PSO. For the robot task planning problem, Xiao et al [206] modified the DE algorithm by combining the roulette and multi-neighborhood operations (to solve local optimal solution), the de-crossover strategy (to increase the convergence speed), and the multi-population integration strategy (to get high computing resources). The DE optimal path model could provide good performance as compared to the shortest path model under limited energy usage.…”

A Dynamic Motion Analysis of a Six-Wheel Ground Vehicle for Emergency Intervention Actions

“…A navigation architecture becomes efficient if the mobility of the robot is divided into specialized software modules. This architecture consists of software modularity (as a consequence of introducing a new sensor or maintaining some obstacle avoidance modules based on certain cinematics), robot location control based on the different functionalities and learning algorithms, techniques of time-domain analysis (response time of the sensors, temporal depth, space localization, and decision making based of the dynamics of the robot), and decoupled control [ 32 , 33 , 34 , 35 , 36 ]. Implementing machine learning and deep learning techniques requires precise information so that the path planning allows locating the robot in space and memorizing the position of obstacles.…”