Multi objective motion planning of fruit harvesting manipulator based on improved BIT* algorithm

The primary challenge for fruit-harvesting robots in unstructured orchard environments lies in achieving fast and accurate fruit picking while avoiding obstacles like branches. This paper introduces a rapid and efficient multi-objective motion planning method based on the improved BIT* algorithm. Two depth cameras are employed to acquire the locations of both targets and obstacles, and an obstacle map of the harvesting environment is generated using the octree method. For collision detection, a combination of bounding box and grid-based techniques is applied. The proposed bidirectional BIT* (Bi-BIT*) algorithm builds forward and backward trees simultaneously during initialization, alternating searches to reduce the time required for the initial solution. The manipulator's joint paths are interpolated using a quintic polynomial, and a multi-objective optimization problem is solved to achieve a smooth joint motion trajectory while minimizing energy consumption and pulsation. Both two-dimensional and three-dimensional simulations demonstrate that the Bi-BIT* algorithm consistently outperforms three other algorithms, achieving the highest overall scores. In the harvesting experiment of Scenario 1, the Bi-BIT* algorithm had an average execution time of 7.32 s—36.4% faster than the Informed RRT* algorithm, 19.0% faster than the RRT-Connect algorithm, and 28.7% faster than the BIT* algorithm. Additionally, the Bi-BIT* algorithm achieved a 96% planning success rate and an 84% execution success rate, surpassing the other three algorithms. In Experiment Scenario 2, the Bi-BIT* algorithm had an average execution time of 8.59 s, which is 41.0% faster than the Informed RRT* algorithm, 6.3% faster than the RRT-Connect algorithm, and 19.5% faster than the BIT* algorithm. Furthermore, the Bi-BIT* algorithm demonstrated superior planning and execution success rates of 92% and 88%, respectively, compared to the other algorithms. These experimental results confirm that the proposed multi-objective motion planning method enables the harvesting manipulator to avoid obstacles efficiently and accurately, completing the harvesting task with high performance.