高温混合障碍空间中的移动机器人路径规划

为了解决高温场景中移动机器人全局路径规划所面临的安全与效率问题，提出高温热源虚拟障碍的定义，建立混合障碍空间模型，将高温场景中的路径规划问题转化为高温混合障碍空间中考虑路径温度代价和长度代价的多目标优化问题. 改进NSGA-Ⅱ算法，通过选取优秀非可行解扩展种群，提高了种群多样性和进化效率，提出新的交叉和变异概率计算方法. 根据种群进化进程和个体代价函数值调整概率，实现了种群前期搜索能力和后期收敛性的平衡. 仿真所得的最优路径结果表明，该改进算法的路径长度代价虽然比原算法和其他改进算法略有增加，但温度代价大幅降低，更有效地避免了陷入局部最优.

Path planning of mobile robots in mixed obstacle space with high temperature

The definition of virtual obstacles for high temperature heat sources was proposed in order to solve the safety and efficiency problems faced by the global path planning of mobile robots in high temperature scenarios. A hybrid obstacle space model was established. The path planning problem in high temperature scene was transformed into a multi-objective optimization problem in high temperature mixed obstacle space, which considered the cost of path temperature and length. The NSGA-Ⅱ algorithm was improved to expand the population by selecting excellent non-feasible solutions, which improved the population diversity and population evolution efficiency. A new adaptive crossover and mutation probability calculation method was proposed. The process adjustment value realized the balance between the search ability in the early stage of the population and the convergence in the later stage according to the individual cost function value and the overall evolution process of the population. The simulation results of the optimal path show that although the path length cost of the proposed improved algorithm is slightly higher than that of the original algorithm and other improved algorithms, the temperature cost is greatly reduced. The proposed improved algorithm is more effective to avoid falling into the local optimal solution.