This article deals with motion planning for a multifunctional underwater robot that can perform various tasks such as swimming, walking, and grasping objects. We have developed a unified motion planning method that can generate motion planning for a variety of movements using a single algorithm. With this method, motion planning problems are modeled as finite-horizon Markov decision processes, and optimum motion planning is achieved by dynamic programming. However, conventional dynamic programming is sometimes considered to have limited applicability because of "the curse of dimensionality." To avoid this issue, we applied a random network as a state transition network to suppress the explosion in the number of states. The effectiveness of the proposed method is demonstrated through numerical simulations involving two types of task for multifunctional robots. One is a reaching task, and the other is a thrust force generation task.
All Science Journal Classification (ASJC) codes
- Biochemistry, Genetics and Molecular Biology(all)
- Artificial Intelligence