# Task Planning of Space-Robot Clusters Based on Modified Differential Evolution Algorithm
## 1 Introduction  
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 mission planning of space robots plays an important role in on-orbit services. In recent years, research on space robot mission planning has made considerable progress [3; 4], which has laid a solid foundation for the on-orbit assembly and maintenance of large space agencies. Due to the limited efficiency of a single space robot, the collaborative operation of a cluster composed of multiple space robots is an effective way to complete large space tasks. Space-robot cluster task allocation and path planning are one of the core problems of multirobot system research [5; 6; 7]. Whether the task planning is reasonable is directly related to the final performance of the entire multirobot system. As the complexity and scale of the tasks performed increase, the effectiveness and efficiency of the task-planning method becomes increasingly important. Aiming at the problem of collaborative task planning for robot clusters, many documents have proposed a series of models and algorithms [8; 9; 10; 11; 12; 13; 14], mainly including behavior-based allocation methods, market-mechanism methods, swarm-intelligence methods and idle chain-based methods. [PERSON] and [PERSON] et al. [15] proposed an intelligent self-organizing algorithm to solve the problem of collaborativesearch attack tasks of multiple agents. The architecture divides the global optimization problem into several local optimization problems. Each agent can solve its own local optimization problem, and then make the best decision for the multiagent system through the information exchange between the agents. [PERSON] and [PERSON] et al. [16] proposed a task planning algorithm based on the correlation between robot capabilities and team collaboration, under which an initialized associative array R was used to group the robots so that the robots in the group had better collaboration capabilities when performing tasks. [PERSON] and [PERSON] et al. [17] proposed an iterative strategy with an improved mission planning framework. By improving the onboard computing and communication resource management of agents, based on this strategy, each agent will reevaluate unreasonable planning results. [PERSON] et al. [18] solved the task planning problem of multirobot systems by using group intelligence technology and proposed three complementary algorithms, forming a new method of increasing the amount of tasks executed. [PERSON] and [PERSON] [19] developed a task-independent and system-independent classification method based on optimization theory, which is used to study task allocation problems. Moreover, its goal is to allocate limited resources in a way that effectively accomplishes certain tasks. The problem of multirobot task assignment is a challenging research topic in the field of robotics [20; 21; 22; 23; 24]. The task assignment problem of the spacer-robot cluster can be transformed into an optimization problem, and then some intelligent-optimization methods can be used to solve the optimization problem. [PERSON] and [PERSON] 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. [PERSON] and [PERSON] [26] proposed an optimized multirobot task-allocation and scheduling method based on fish swarm algorithm. [PERSON] and [PERSON] et al. [27] proposed a memetic algorithm based on ant colony optimization. At the same time, the problem of the maximum travel distance and total travel distance of multiple robots is optimized.