Path planning for construction site inspection robots based on the hybrid optimization of improved APF and improved ACO

In response to the dual challenges of complex static obstacles and frequent dynamic disturbances in the construction site environment, this paper proposes a hybrid path planning strategy that integrates global static planning and local dynamic re-planning. The research first adopts the grid method to model the environment. For global static programming, the deterministic annealing (DA) algorithm is introduced to control the temperature parameters, the potential function of the artificial potential field (APF) method is improved, and the free energy potential function is constructed, effectively solving the problem that traditional APF is prone to fall into local minima. For local dynamic programming, the algorithm adaptively divides the environment into multiple sub-regions, transforms the traversal problem into a traveling salesman problem, optimizes the traversal sequence using the simulated annealing algorithm, and adopts an improved ant colony algorithm to search for the shortest paths between sub-regions and to deal with dynamic obstacles, thereby forming a collaborative planning system. Simulation experiments show that the proposed hybrid algorithm significantly improves performance: in an ultra-complex static environment, its path planning success rate is as high as 81.3%, the path length is reduced by 11.1% and 14.7% respectively compared with the A* and Dijkstra algorithms, and the computing time is reduced by 27.4% and 37.9% respectively. In a high dynamic obstacle environment, the success rate of local re-planning remains at 89.0%. Under extremely high obstacle density, the algorithm can still maintain a path stability of 77.6% and an obstacle avoidance success rate of 80.2%. In the tests conducted in actual construction site scenarios, it has also demonstrated excellent adaptability and optimization effects. This study effectively integrates the global optimization ability of DA-APF and the local adaptive ability of Simulated Annealing-Ant Colony Optimization (SA-ACO) Algorithm, significantly improving the real-time performance, obstacle avoidance accuracy and overall robustness of path planning, providing a better solution for the autonomous navigation of construction site inspection robots.