Implementing Obstacle Avoidance for Mobile Laser Ranging Robots Using Reinforcement Learning

Obstacle avoidance for mobile laser ranging robots represents one of the core challenges in autonomous robot navigation. By employing reinforcement learning methods, robots can autonomously learn optimal obstacle avoidance strategies through environmental interactions, eliminating the need for pre-defined maps or complex rule-based systems.

Within the reinforcement learning framework, the robot functions as an agent where laser rangefinder sensor readings constitute the state space, while robot motion control commands form the action space. A typical implementation involves designing an appropriate reward function - for instance, assigning positive rewards for successful obstacle avoidance and negative rewards for collisions. In code implementation, this reward function might be structured as: reward = distance_to_obstacle * scaling_factor - collision_penalty.

Deep Q-Networks (DQN) or policy gradient methods are commonly applied to handle such continuous state space problems. The laser ranging data undergoes preprocessing (often including normalization and noise filtering) before being fed into neural networks, which then output action decisions. The training process involves the robot accumulating experience through continuous trial-and-error, gradually learning to recognize obstacle patterns in complex environments and generating appropriate avoidance responses. Key implementation components include experience replay buffers for storing state-action-reward transitions and target networks for stable Q-value estimation.

Compared to traditional rule-based obstacle avoidance methods, reinforcement learning solutions offer superior adaptability to dynamically changing environments, with obstacle avoidance performance continuously improving as training progresses. However, challenges remain in training efficiency and generalization capabilities, requiring careful design of network architectures (such as convolutional layers for spatial feature extraction from laser scans) and training strategies including curriculum learning and reward shaping techniques.