Reinforcement Learning–Based Mobile Robot Obstacle Avoidance

Overview

Implemented a Deep Deterministic Policy Gradient (DDPG) based navigation agent for a mobile robot navigating a grid environment using ultrasonic sensors for obstacle detection. The agent learned a continuous control policy from scratch, without any hand-crafted rules, and was validated across 200 evaluation runs.

The work demonstrated that model-free deep reinforcement learning can learn effective obstacle avoidance policies for mobile robots entirely from trial-and-error interaction with a simulated environment — no human-designed heuristics, no explicit path planning. The results were validated and published in an international academic journal.

The Challenge

Model-free reinforcement learning for robot navigation presents significant training challenges: sparse rewards in large environments, the sim-to-real gap when sensor models are simplified, and the need for thousands of episodes before useful behavior emerges. DDPG was chosen for its ability to handle continuous action spaces — essential for smooth robot motion — but required careful reward shaping and hyperparameter tuning to achieve stable convergence.

The Actor-Critic architecture of DDPG introduces additional training complexity: both networks must converge together, and instabilities in one network can destabilize the other. Managing this required careful learning rate scheduling, target network update frequency tuning, and experience replay buffer design.

Technical Approach

• DDPG (Deep Deterministic Policy Gradient) implementation for continuous action control — outputs smooth velocity commands rather than discrete turn/move decisions
• Actor-Critic neural network architecture with separate actor (policy) and critic (value) networks, both trained end-to-end from ultrasonic sensor observations
• Experience replay buffer storing past transitions for off-policy learning, breaking temporal correlations that destabilize online training
• Ultrasonic sensor simulation providing the agent with obstacle proximity readings in all directions — a realistic sensor model matching common mobile robot hardware
• Reward shaping: positive reward for goal-directed progress, negative penalties for collisions and hazardous proximity to obstacles, encouraging safe and efficient navigation
• Training pipeline in TensorFlow over 75,000+ episodes — comprehensive training duration to ensure policy convergence across diverse environment configurations
• Rigorous evaluation protocol: 200 independent evaluation runs across varied starting positions and obstacle layouts, providing statistically meaningful success rate measurements

Key Outcomes