Energy-Efficient Path Planning of Reconfigurable Robots in Complex Environments

Planning the energy-efficient and collision-free paths for reconfigurable robots in complex environments is more challenging than conventional fixed-shaped robots due to their flexible degrees of freedom while navigating through tight spaces. This article presents a novel algorithm, energy-efficient batch informed trees* (BIT*) for reconfigurable robots, which incorporates BIT*, an informed, anytime sampling-based planner, with the energy-based objectives that consider the energy cost for robot's each reconfigurable action. Moreover, it proposes to improve the direct sampling technique of informed RRT* by defining an greedy informed set that shrinks as a function of the state with the maximum admissible estimated cost instead of shrinking as a function of the current solution, thereby improving the convergence rate of the algorithm. Experiments were conducted on a tetromino hinged-based reconfigurable robot as a case study to validate our proposed path planning technique. The outcome of our trials shows that the proposed approach produces energy-efficient solution paths, and outperforms existing techniques on simulated and real-world experiments.

Automated summary

This article proposes a novel algorithm for planning energy-efficient and collision-free paths for reconfigurable robots. The algorithm combines BIT*, an informed planner, with energy-based objectives that consider the energy cost of each reconfigurable action. Additionally, it improves the convergence rate of the algorithm by enhancing the direct sampling technique of informed RRT*. Experimental results on a tetromino hinged-based reconfigurable robot demonstrate the effectiveness of the proposed path planning technique.