An Efficient Navigation System for Autonomous Mobile Robots on Rough Terrain Environments

Abstract

This paper presents an efficient navigation framework for autonomous mobile robots operating in rough terrain environments. The proposed approach integrates terrain perception, traversability analysis, and motion planning to enable safe and reliable navigation in complex off-road conditions. An Extended Kalman Filter (EKF) is employed to enhance robot localization, providing robust pose estimation in noisy and unstructured environments. A 2.5D elevation grid map is constructed to represent the local terrain geometry, from which a traversability cost function is formulated to evaluate terrain feasibility. To address global path planning, a Traversability-A* algorithm is proposed by extending the classical A* method with terrain-aware cost integration, allowing the planner to avoid hazardous regions. For local trajectory optimization, a Timed Elastic Band (TEB) planner is adopted to ensure kinematic feasibility and dynamic consistency. The proposed framework is implemented on a skid-steering mobile robot and validated in Gazebo-based simulation environments with uneven terrain. The results demonstrate improved navigation safety and path quality compared to conventional methods.