用多目标决策提升连续体机器人的抗损能力。
Increasing Resilience of Continuum Robots via Motion Planning Algorithms
- 融合层次分析法优化遗传与A*算法路径质量。
- 遗传算法生成路径多样性更高，执行时间不受环境复杂度影响。
- 适合需延长维护周期的高可靠性机器人场景。
本文针对连续体机器人的运动规划开展实验研究，重点探索多准则决策在路径规划算法中的应用及其对生成路径和执行时间的影响。采用遗传算法和A*算法，并引入层次分析法（Analytical Hierarchy Process）评估路径质量，综合考虑距离、电机损伤、机械臂损伤和精度四项指标，以提升机器人整体韧性。实验在两种模拟环境进行：一种含单路径与多路径点，另一种仅含多路径点。尽管简化了机器人模型与环境，仍保留部分真实原型特征。结果表明，相较于A*算法，遗传算法的性能时间不随环境基数变化，生成路径更具多样性，从而增强机器人抗损能力。
原文摘要 · Abstract (English)
This paper presents an experimental study of motion planning for resilient continuum robots. In this study we mainly focused on multi-criteria decision-making, its application for path-planning algorithms, impact on the generated path and execution time. To do this, we used two well-known algorithms for path planning, namely Genetic algorithm and A star algorithm, and modified them by adding the Analytical Hierarchy Process algorithm to evaluate the quality of the paths generated. In our experiment the Analytical Hierarchy Process considers four different criteria, i.e. distance, motors damage, mechanical damage of the robot's arm and accuracy, each considered to contribute to the resilience of a continuum robot. The use of different criteria is necessary to increase the time to maintenance operations of the continuum robot. We conducted the experiments using two different simulated environments of the robot. Although we significantly simplified the robot's model and its environment, we still implemented some of the features of the environment based on the real robot prototype. In particular, one of the environments has single- as well as multi-path points, and other consists of the multi-path points only. The results show that, in contrast to A star, the performance time of Genetic algorithm does not depend on the environment's cardinality. It generates more diverse paths, which increases the robot's resilience.
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