Path Planning and Control of Building Robots Based on Reinforcement Learning Algorithm in Intelligent Construction

At present, the construction industry is actively promoting new construction methods such as intelligent and object-intensive construction. Among them, mobile operation robots, as one of the important solutions in the intelligent construction environment, involve key technologies such as obstacle avoidance, path planning, positioning, navigation, sensing and communication, and motion control and path planning problems are considered to be its most complex tasks. This paper studies the algorithm principle and optimization method based on reinforcement learning for the path planning and control problem of mobile operation robots in the intelligent construction environment. Reinforcement learning realizes strategy iteration through a reward and punishment mechanism, and can adaptively find the best course of action in an unfamiliar setting. Q-learning, as a classic algorithm, seeks to maximize long-term rewards by updating the value function. Nevertheless, the conventional Q-learning algorithm has issues with sparse rewards, sluggish convergence, and a propensity to enter local optimality when Q values are initialized. To this end, this paper introduces the artificial potential field method, uses gravitational and repulsive potential fields to optimize path planning, and improves the Q value function by integrating the reward mechanism of potential fields. The gravitational potential field attracts the robot to approach the target point, while the repulsive potential field avoids collision with obstacles. Experiments show that this method effectively solves the issue of path planning in intricate settings and offers a fresh concept for intelligent navigation of construction robots.