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Energy-Efficient Path Planning in Uneven Terrains Using Adaptive Reinforcement Learning

D. A. Warnakulasuriya,Juha Plosila,Hashem Haghbayan

Published 2025 in 2025 10th International Conference on Control and Robotics Engineering (ICCRE)

ABSTRACT

Efficient navigation of mobile robots through partially known, uneven terrains remains a significant challenge due to the impact of terrain features on motion costs. This paper presents a novel adaptive reinforcement learning approach using a dynamic reward function to address this issue. The proposed algorithm enables learning of energy-efficient paths by estimating cumulative energy costs in a two-and-a-half dimensional (2.5D) grid world, without requiring prior models or energy-cost maps. Unlike conventional reinforcement learning approaches that optimize step-wise energy, our method focuses on minimizing the total traversal energy. Based on classical Q-learning, the agent iteratively improves its policy through experience. Simulation results show that the proposed approach reduces energy consumption by 10.9% compared to shortest-path methods and achieves comparable performance to deterministic, model-based planners optimized for energy.

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