TSO-HA*-Net: A Hybrid Global Path Planner for the Inspection Vehicles Used in Caged Poultry Houses
Yueping Sun, Zhanxue Cao, Weihao Yan, Xueao Lv, Ziheng Zhang, Dean Zhao · Agriculture · 2025
Traditional track-based inspection schemes for caged poultry houses face issues with vulnerable tracks and cumbersome maintenance, while existing rail-less alternatives lack robust, reliable path planners. This study proposes TSO-HA*-Net, a hybrid global path planner that combines TSO-HA* with topological planning, which allows the inspection vehicle to continuously traverse a predetermined trackless route within each poultry house and conduct house-to-house inspections. Initially, the spatiotemporally optimized Hybrid A* (TSO-HA*) is employed as the lower-level planner to efficiently construct a semi-structured topological network by integrating predefined inspection rules into the global grid map of the poultry houses. Subsequently, the Dijkstra’s algorithm is adopted to plan a smooth inspection route that aligns with the starting and ending poses, conforming to the network. TSO-HA* retains the smoothness of HA* paths while reducing both time and computational overhead, thereby enhancing speed and efficiency in network generation. Experimental results show that compared to LDP-MAP and A*-dis, utilizing the distance reference tree (DRT) for h2 calculation, the total planning time of the TSO-HA* algorithm is reduced by 66.6% and 96.4%, respectively, and the stored nodes are reduced by 99.7% and 97.4%, respectively. The application of the collision template in TSO-HA* results in a minimum reduction of 4.0% in front-end planning time, and the prior collision detection further decreases planning time by an average of 19.1%. The TSO-HA*-Net algorithm achieves global topological planning in a mere 546.6 ms, thereby addressing the critical deficiency of a viable global planner for inspection vehicles in poultry houses. This study provides valuable case studies and algorithmic insights for similar inspection task.