基于视觉引导的番茄连续采摘序列优化方法

Vision-guided Tomato Continuous Picking Sequence Optimization Method

摘要:

针对采摘机器人在连续采摘多个目标番茄时存在采摘成功率低、规划路径长等问题，提出一种基于视觉引导的多目标番茄采摘序列优化方法。建立空间异构双目立体视觉定位系统，获取多目标番茄三维坐标，判断番茄的成熟度与遮挡情况，建立非封闭空间下基于视觉引导的番茄采摘任务空间与集合，并将连续采摘问题转换为三维旅行商问题；构建基于改进麻雀算法（VG-ISSA）的连续采摘序列优化方法，采用立方混沌映射对种群初始化，获得随机性、遍历性高的麻雀种群，结合粒子群优化策略对探索者位置进行自适应调整，加入Levy飞行策略增强追随者的遍历性，提出一种视觉信息引入策略，使算法能够根据实际遮挡情况进行合理序列优化；通过仿真与实验室番茄采摘实验对所提方法进行验证，并与遗传算法、粒子群算法、标准麻雀算法进行比较，结果表明：所改进算法相较于遗传算法、粒子群算法、标准麻雀算法响应时间分别减少19.8%、32.9%、42.4%，采摘路径长度分别减少25.8%、24.0%、16.24%，实验证明所提方法在采摘机器人实现番茄连续采摘过程中具有一定的先进性。

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.