RL-Based Vibration-Aware Path Planning for Mobile Robots’ Health and Safety

Sathian Pookkuttath (), Braulio Felix Gomez and Mohan Rajesh Elara
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Sathian Pookkuttath: Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore
Braulio Felix Gomez: Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore
Mohan Rajesh Elara: Engineering Product Development Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, Singapore

Mathematics, 2025, vol. 13, issue 6, 1-24

Abstract: Mobile robots are widely used, with research focusing on autonomy and functionality. However, long-term deployment requires their health and safety to be ensured. Terrain-induced vibrations accelerate wear. Hence, self-awareness and optimal path selection, avoiding such terrain anomalies, is essential. This study proposes an RL-based vibration-aware path planning framework, incorporating terrain roughness level classification, a vibration cost map, and an optimized vibration-aware path planning strategy. Terrain roughness is classified into four levels using IMU sensor data, achieving average prediction accuracy of 97% with a 1D CNN model. A vibration cost map is created by assigning vibration costs to each predicted class on a 2D occupancy grid, incorporating obstacles, vibration-prone areas, and the robot’s pose for navigation. An RL model is applied that adapts to changing terrain for path planning. The RL agent uses an MDP-based policy and a deep RL training model with PPO, taking the vibration cost map as input. Finally, the RL-based vibration-aware path planning framework is validated through virtual and real-world experiments using an in-house mobile robot. The proposed approach is compared with the A* path planning algorithm using a performance index that assesses movement and the terrain roughness level. The results show that it effectively avoids rough areas while maintaining the shortest distance.

Keywords: terrain classification; condition monitoring; path planning; 1D CNN; reinforcement learning; mobile robots; operational safety (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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