Abstract:In order to solve the problems of low picking success rate and long planning path when the picking robot picks multiple target tomatoes continuously, a multi-objective tomato picking sequence optimization method based on visual guidance was proposed. A spatially heterogeneous binocular stereo vision positioning system was established to obtain the three-dimensional coordinates of multi-objective tomatoes, to judge the maturity and occlusion of tomatoes, establish the space and collection of tomato picking tasks based on visual guidance in non-enclosed space, and transform the continuous picking problem into a three-dimensional traveling salesman problem. A continuous picking sequence optimization method based on improved sparrow algorithm (VG-ISSA) was constructed, the population was initialized by cubic chaos mapping, and the sparrow population with high randomness and ergodic nature was obtained, the position of the explorer was adaptively adjusted by combining the particle swarm optimization strategy, the Levy flight strategy was added to enhance the traversal of the followers, and a visual information introduction strategy was proposed, so that the algorithm could carry out reasonable sequence optimization according to the actual occlusion. The results showed that compared with the genetic algorithm, particle swarm optimization and standard sparrow algorithm, the improved algorithm reduced the response time by 19.8%, 32.9% and 42.4%, and the path length was reduced by 25.8%, 24.0% and 16.24%, respectively. Experiments showed that the proposed method had certain advancement in the process of continuous tomato picking by picking robot.
