Track papers across labs, topics, and trends.
Search papers, labs, and topics across Lattice.
This paper introduces a novel path planning algorithm for substation robots that integrates deep reinforcement learning (DRL) with ant colony optimization (ACO) to address challenges posed by complex substation environments. The method uses a pheromone-guided exploration strategy to reduce ineffective exploration, a sample screening mechanism based on ACO path experience to improve Q-network training, and dynamic decision weight adjustment for transitioning from heuristic guidance to autonomous learning. Experimental results demonstrate that the proposed algorithm achieves higher sample efficiency, shorter path lengths, and better dynamic obstacle avoidance compared to PPO, DDQN, and A*, with field validation confirming improved task completion rates.
Substation robots can now navigate complex environments with significantly improved efficiency and safety thanks to a novel DRL algorithm boosted by ant colony optimization, outperforming traditional methods by up to 24% in sample efficiency.
Substation robots face significant challenges in path planning due to the complex electromagnetic environment, dense equipment layout, and safety-critical operational requirements. This paper proposes a path planning algorithm based on deep reinforcement learning enhanced by ant colony optimization, establishing a synergistic optimization framework that combines bio-inspired algorithms with deep learning. The proposed method addresses critical path planning issues in substation inspection and maintenance operations. The approach includes: 1) designing a pheromone-guided exploration strategy that transforms environmental prior knowledge into spatial bias to reduce ineffective exploration; 2) establishing a high-quality sample screening mechanism that enhances Q-network training through ant colony path experience to improve sample efficiency; 3) implementing dynamic decision weight adjustment that enables gradual transition from heuristic guidance to autonomous learning decisions. Experimental results in complex environments demonstrate the method’s superiority. Compared to state-of-the-art baselines including PPO, DDQN, and A*, the proposed method achieves 24% higher sample efficiency, 18% reduction in average path length, and superior dynamic obstacle avoidance. Field validation in a 2,500-square-meter substation confirms a 14.8% improvement in task completion rate compared to standard DRL approaches.