Reactive Baseline Framework for Dynamic Navigation and Obstacle Avoidance of Bipedal Robots in Simulated Indoor Environments

Abstract

Autonomous navigation in dynamic indoor environments remains a significant challenge for bipedal robots due to the need to maintain locomotion stability while responding to moving obstacles. This paper presents a lightweight reactive baseline framework for biped robots developed in ROS Noetic and Gazebo simulation. The proposed framework, inspired by existing reactive avoidance techniques for bipedal platforms, integrates a finite-state machine (FSM) navigation controller with a simple lateral sidestepping strategy for dynamic obstacle avoidance, enabling continuous goal-directed motion without complex perception or prediction modules. A custom URDF(Unified Robot Discription Format)-based biped robot model and a sinusoidally moving obstacle were used for evaluation. One hundred continuous experimental trials were conducted under identical conditions. The framework achieved a 95.0% success rate in completing the full dynamic navigation task (goal reaching, obstacle avoidance, and return-to-home). The 5.0% failure rate was explicitly analyzed and attributed to reactive oscillation and the lack of upper-torso angular momentum regulation during high-velocity lateral maneuvers. For successful runs, the framework demonstrated a highly stable average task completion time of approximately 11 seconds. These results validate the effectiveness of the proposed lightweight reactive approach as a strong, predictable baseline for future research in biped robot navigation. These results validate the effectiveness of the proposed lightweight reactive approach as a strong baseline for future research in biped robot navigation, particularly in resource-constrained or perception-limited settings