核心概念
This paper presents an experimental study on resilient path planning for continuum robots, considering multiple optimization criteria to enhance the robot's resilience and increase its time to maintenance operations.
摘要
The paper explores the use of two well-known path planning algorithms, Genetic Algorithm (GA) and A* Algorithm, in combination with the Analytical Hierarchy Process (AHP) decision-making algorithm to generate optimal paths for a continuum robot prototype.
The key highlights and insights are:
The authors defined four criteria to assess the resilience of the generated paths: distance, motor damage, mechanical damage, and accuracy. The AHP algorithm is used to generate weights for these criteria based on their relative importance.
Experiments were conducted to compare the performance of GA and A* algorithms under different criteria combinations. The results show that GA is more sensitive to criteria variations and can generate diverse paths, while A* performs better in terms of processing time and path quality when only the distance criterion is prioritized.
When alternative goal points are considered, GA outperforms A* in generating paths that better fit the multi-criteria optimization, despite its slower processing time. This demonstrates the advantage of GA in leveraging the alternative options to find more resilient paths.
The authors emphasize the importance of considering complex parameters, such as mechanical stress on the robot's components, to enhance the system's resilience and increase its remaining useful life. They plan to provide feedback about the mechanical stress to further adapt the robot's movements in future research.
統計資料
The paper does not provide any specific numerical data or metrics to support the key logics. The focus is on the comparative analysis of the path planning algorithms and the multi-criteria optimization approach.
引述
The paper does not contain any striking quotes that support the key logics.