Paper presented at ISTVS 2025 | 55th Conference of the International Society for Terrain-Vehicle Systems https://doi.org/10.56884/HVHA4780
Authors: Ryosuke Eto, Fushiki Taguchi, Junya Yamakawa
Keywords: Local path planning; autonomous unmanned ground vehicle; reinforcement learning; rough terrain
Abstract:
Autonomous unmanned ground vehicles are expected to be utilized at disaster sites. The previously obtained map information for the site is often unreliable because disaster areas can change rapidly and unpredictably. Therefore, the vehicles must be able to recognize the surrounding rough terrain environment in real time and plan optimal routes to approach the destination safely. However, local path planning may fall into the local minimum solution and get stuck. Therefore, in this study, a method of avoiding a standstill by utilizing information entropy and route history to encourage exploratory behavior was investigated. In the proposed method, routes are selected by determining the vehicle behavior using reinforcement learning. For observation information, a Digital Elevation Model, an information entropy map of elevation, and a map that records the number of times the vehicle has traveled on the grid, in addition to the vehicle's state were used. The aim is to acquire the behavior of going to the target with selecting a safe route to avoid getting stuck by rewarding the vehicle for going to the target, avoiding uneven terrain, reducing information entropy, and not repeatedly driving in the same place. In this study, we tested the proposed method on each terrain with a single large obstacle and a dead-end by simulations. The simulation results showed that the vehicle can reach the target efficiently by introducing rewards for exploratory behavior.
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