Trajectory planning in the presence of dynamic obstacles for Anguilliform-inspired robots

Abstract

This paper presents an optimal trajectory replanning approach for anguilliform-inspired robots in a dynamic environment based on the model predictive planning framework. The dynamic constraints were captured via simulations and were expressed as motion primitives. The developed approach utilizes the motion primitives for generating search trees and a replanning algorithm based on D-star lite. Along with the position, the velocity of obstacles was also used to predict collision. After the way-point generation, line-of-sight guidance was used for trajectory tracking. The trajectory generated by the above approach was found to be dynamically feasible, collision-free and optimal. This approach can help not only anguilliform-inspired robots but other bio-inspired robots to plan in a dynamic environment where they can interact with other creatures and complete tasks without colliding with them. It is envisaged that the developed approach can help in imparting autonomy to fish-inspired robots which can find applications in the applications like underwater rig maintenance, surveillance, and stealth operations for the military.