Bio-Inspired Nonlinear Navigation Dynamics for Autonomous Vehicles in Obstacle-Dense Environments

Abstract

Autonomous vehicle navigation in obstacle-dense environments is challenging because safe motion requires continuous adaptation to limited clearance, local congestion, and frequent directional change. Recent studies have shown that bio-inspired control and nonlinear navigation methods can improve adaptive motion generation in complex environments. However, many existing approaches still separate path planning, obstacle interaction, and control correction, which can lead to oscillation, conservative detours, or reduced stability. This article presents a bio-inspired nonlinear navigation framework that integrates target attraction, obstacle repulsion, directional adaptation, and speed regulation within a unified control structure. The method is evaluated in sparse, moderate, and obstacle-dense environments using path length, obstacle clearance, and navigation time. The results show smoother trajectories, safe obstacle clearance, and efficient goal-reaching performance as environmental complexity increases. These findings suggest that bio-inspired nonlinear navigation is a promising strategy for autonomous vehicles in cluttered environments.