A Path Planning Learning Strategy Design for a Wheel-Legged Vehicle Considering Both Distance and Energy Consumption | IEEE Journals & Magazine | IEEE Xplore

A Path Planning Learning Strategy Design for a Wheel-Legged Vehicle Considering Both Distance and Energy Consumption


Abstract:

To obtain an optimal feasible path, this paper presents a path planning learning strategy for a wheel-legged vehicle considering both distance and energy consumption. Fir...Show More

Abstract:

To obtain an optimal feasible path, this paper presents a path planning learning strategy for a wheel-legged vehicle considering both distance and energy consumption. Firstly, a new reward function and update rule of the Q-Learning algorithm, considering the influence of obstacle crossing parameters and modification of path energy consumption, is presented to ensure the path shortening and energy consumption reduction. Secondly, the future energy consumption is introduced into the modification of path energy consumption. It evaluates the potential energy consumption between each state reached by the vehicle and the target state. The priority sequence of Q table update is provided, which greatly speeds up the convergence speed of the algorithm. Finally, the proposed strategy is verified on different size maps with 0-1m obstacle height. Results show that, in the complex map, the proposed strategy is effective to shorten 7.6m distance compared with the wheeled driving strategy and to reduce energy consumption by 31% compared with the wheel-legged obstacle crossing strategy. It has a faster convergence speed. Compared with the A*-based and Dijkstra-based strategies, their planning effect is approximately the same, but the energy consumption using the proposed strategy can be reduced by 3.5%.
Published in: IEEE Transactions on Vehicular Technology ( Volume: 72, Issue: 4, April 2023)
Page(s): 4277 - 4293
Date of Publication: 21 November 2022

ISSN Information:

Funding Agency:


References

References is not available for this document.