Title: Adaptive navigation of mobile robots: synergising attractor dynamics and DDPG reinforcement learning for safe dynamic obstacle avoidance

Authors: Walid Jebrane; Nabil El Akchioui

Addresses: Faculty of Science and Technology, University Abdelmalek Essaadi, Al Hoceima, Tetouan, Morocco ' Faculty of Science and Technology, University Abdelmalek Essaadi, Al Hoceima, Tetouan, Morocco

Abstract: Robot navigation in complex and dynamic environments remains a challenging problem, requiring methods that can efficiently adapt to unforeseen obstacles and goal-oriented tasks. This paper presents a novel approach that combines the biologically-inspired Attractor Dynamics Approach with the Deep Deterministic Policy Gradient (DDPG) algorithm to enable a mobile robot, specifically the e-puck robot, to navigate through cluttered spaces while avoiding collisions with moving obstacles effectively. The Attractor Dynamics Approach utilises attractors as goals and repulsive forces to avoid obstacles, offering robust and goal-oriented navigation even with very low-level sensory information. In parallel, the DDPG-based reinforcement learning component fine-tunes the robot's motion controls based on range sensor readings, ensuring precise and adaptive obstacle avoidance. The integration of these two techniques empowers the robot to autonomously explore its environment, dynamically adjust its trajectory and reach predefined targets successfully and safely.

Keywords: deep deterministic policy gradient; attractor dynamics approach; robot navigation; obstacle avoidance; deep reinforcement learning.

DOI: 10.1504/IJRS.2025.147324

International Journal of Reliability and Safety, 2025 Vol.19 No.3, pp.244 - 266

Received: 15 Jan 2024
Accepted: 04 Nov 2024
Published online: 14 Jul 2025 *

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