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.
Highlights
With the continuous consumption of earth’s energy, human vision has turned to space, and large-scale space solar-power stations (SSPS) have entered the vision of various space powers.On–orbit services, such as the assembly and maintenance of large space equipment [1,2] such as SSPS, are hot topics in current research
The task-clustering method is used to cluster the maintenance tasks to reduce the problem size and reduce the problem solving time; a modified differential evolution algorithm including multiple neighborhood operations, roulette, de-crossover, multiple groups and other strategies is proposed to solve the problem of task allocation and path planning of multi-robots, complete the mission planning of the space-robot cluster, and improve the maintenance efficiency
Analysis the modified differential evolution algorithm is very sensitive to parameters, different parameters will lead to different programming results for the same calculation
Summary
With the continuous consumption of earth’s energy, human vision has turned to space, and large-scale space solar-power stations (SSPS) have entered the vision of various space powers. Hussein and Marinplaza et al [25] proposed a general method to solve the problem of multirobot task assignment (MRTA) This method combines simulated annealing and genetic algorithm and can appropriately use all available resources to optimize the allocation, thereby improving the overall system performance and reducing costs. Combined the characteristics of particle swarm optimization, ant colony algorithm and 3-opt algorithm, and proposed a hybrid algorithm PSO-ACO-3-opt for solving standard TSPs. The task-planning method of the spacer-robot cluster will directly affect the efficiency of the entire system. The task-clustering method is used to cluster the maintenance tasks to reduce the problem size and reduce the problem solving time; a modified differential evolution algorithm including multiple neighborhood operations, roulette, de-crossover, multiple groups and other strategies is proposed to solve the problem of task allocation and path planning of multi-robots, complete the mission planning of the space-robot cluster, and improve the maintenance efficiency.
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