Dynamic obstacle avoidance control based on a novel dynamic window approach for agricultural robots

Abstract

With the ongoing advancements in autonomous navigation technology, agricultural robots are increasingly being deployed across various sectors of agriculture. Among the critical components of this technology, dynamic obstacle avoidance in complex agricultural environments serves as the foundation for enhancing the autonomy and safety of these robots. The Dynamic Window Approach (DWA) is a widely recognized method for achieving local obstacle avoidance. It operates by sampling the robot's velocity space and then evaluating the sampled trajectories using a value function to determine the optimal velocity pair. However, a significant limitation of the traditional DWA method lies in its fixed weights for the value function, which restricts its performance to manual tuning and renders it less adaptable to intricate and dynamic obstacle environments. To address this limitation, we introduced an innovative approach by integrating the Twin Delayed Deep Deterministic Policy Gradient (TD3) method into the weight determination process of the DWA algorithm's value function. This integration enabled the weight coefficients to adapt dynamically in response to environmental variations, thereby enhancing the algorithm's flexibility and effectiveness. Our extensive simulation and field testing revealed that while the traditional DWA algorithm struggled to navigate complex dynamic obstacle environments, the proposed TD3-DWA algorithm achieved a success rate of over 90 % in obstacle avoidance. This outcome underscored the algorithm's adaptability and robustness, positioning it as a reliable solution for ensuring safe and efficient navigation in agricultural robotics.