| 矮化密植果园多臂采摘机器人任务规划 |
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| 引用本文： | 李涛,邱权,赵春江,谢丰.矮化密植果园多臂采摘机器人任务规划[J].农业工程学报,2021,37(2):1-10. |
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| 作者姓名： | 李涛 邱权 赵春江 谢丰 |
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| 作者单位： | 1.北京市农林科学院北京农业智能装备技术研究中心，北京 100097;2.北京市农林科学院北京农业信息技术研究中心，北京 100097;1.北京市农林科学院北京农业智能装备技术研究中心，北京 100097；3.江苏大学农业工程学院，镇江 212000 |
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| 基金项目： | 国家自然科学基金资助项目（61973040）；中国博士后科学基金资助项目（2020M680445）；北京市农林科学院博士后科研基金资助项目（2020-ZZ-001） |
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| 摘 要： | 为提高矮化密植果园多机械臂采摘机器人的协同作业效率,该研究对存在重叠访问域的多臂协同采摘机器人任务规划进行分析,将多臂协同任务规划问题归纳为异步重叠访问域的多旅行商问题,给出了基于遗传算法的优化求解方法。试验结果表明:该研究所提任务规划算法在求解4个机械臂采摘43和90颗果实的任务规划问题时,分别在500和2000次迭代后收敛,相比于随机遍历算法,作业遍历时长可缩短40.97%和54.98%;采摘90颗果实,单机械臂的遍历时长约为该方法的4.28倍;采摘3种不同分布条件下的28颗果实,相比于顺序规划法和随机遍历法,该方法的作业遍历时长分别缩短10.69%和27.18%、20.45%和23.33%以及12.94%和21.69%。综上,基于遗传算法的任务规划方法能够协调规划多臂采摘机器人系统的作业任务,确保各个机械臂避免发生冲突,以较短时间遍历所有目标果实,提升作业效率。研究结果可为其他多机械臂采摘机器人任务规划提供参考。
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| 关 键 词： | 机器人 控制 收获 任务规划 协同作业 旅行商问题 遗传算法 |
| 收稿时间： | 2020/11/30 0:00:00 |
| 修稿时间： | 2021/1/20 0:00:00 |
Task planning of multi-arm harvesting robots for high-density dwarf orchards |
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| Li Tao,Qiu Quan,Zhao Chunjiang,Xie Feng.Task planning of multi-arm harvesting robots for high-density dwarf orchards[J].Transactions of the Chinese Society of Agricultural Engineering,2021,37(2):1-10. |
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| Authors: | Li Tao Qiu Quan Zhao Chunjiang Xie Feng |
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| Institution: | 1.Beijing Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China;;2.Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; 1.Beijing Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China;3.College of Agriculture, Jiangsu University, Zhenjiang 212000, China |
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| Abstract: | Abstract: High-density dwarf culture has the characters of high yield and easy mechanization, thereby to be expected as a new development direction of fresh fruit industry. In harvesting, the mechanism of multi-arm cooperation can remarkably improve the operation efficiency of agricultural robots in the orchards of high-density dwarf culture. Generally, multiple picking zone are established for the collaboration of multi-arm harvesting robots, in order to assign proper picking objects to every manipulator. However, these different zones can be partially overlapped, leading to potential conflicts of manipulators in a robot. Path planning can be utilized to generate a trajectory leading the tip of manipulator to the goal without collisions. In this study, a global optimization using a Genetic Algorithm(GA) was proposed to solve the planning problem of harvesting tasks with overlapped picking domains in the multi-arm robots toward high-density dwarf orchards. 1) A multi-arm structure without dead zone in the fruit harvest was specially formulated as a sort of asynchronously multiple traveling salesman problem with overlapped zones. The following mild assumptions were made: a) The visual sensing system can precisely locate the fruits to be harvested; b) The fruits totally covered by leaves were not considered; c) The exceptional cases during the switching phases from one fruit to another were not considered in the planning stage, meaning that the traveling time linearly depended on the Euclidean distance. Five rules were established for the manipulators to work with better cooperative behaviors in a safe region of the workspace, where no collision occurred. 2)A modified GA was applied to optimize the collision-free trajectory planning of a flexible manipulator, further to ensure the operational safety without conflicts in the shortest traveling time. Gene codes were used to formulate various domain-manipulator pairs, thereby to determine picking sequences in a manipulator. The total traveling time of travelers was the objective to be optimized, where the population of gene codes was optimized iteratively in the proposed GA. In this case, the critical phases were coding and decoding. The double chromosomes were selected to formulate various picking sequences. After that, three operators were introduced, including selection, crossover, and mutation. The rule of synchronization was designed to avoid different travelers visiting the same cities simultaneously, in order to cope with the a synchronism in a queuing situation.3)Once the optimized solution was obtained, the planner can easily achieve the proper sequences for each manipulator to pick fruits via decoding the double chromosomes. As such, each manipulator performed the corresponding task, and the overall picking time was therefore reduced. The test results showedthat:1) The proposed planning converged at 500 and 2 000 iterations, when solving 43 fruits and 90 fruits planning problem with 4 manipulators.2) Compared with the sequential traverse, the modified GA increased the efficiency by 41% and 54.98%, when harvesting 43 fruits and 90 fruits, whereas, increased the efficiency by 4.25 times, compared with a single manipulator robot when harvesting 90 fruits.3) Compared with the sequential traverse and random traverse, the modified GA increased the efficiency by 110.69% and 27.18%, 20.45% and 23.33%, 12.94% and 21.69% under three different distributions, respectively. The contributions can be: a) A new planning strategy was proposed for the multi-arm harvester to avoid collisions, where each manipulator behaved cooperatively. b) A novel genetic algorithm was presented, where the coding and decoding was first proposed in this field. c) The rule of synchronization was first proposed to deal with the simultaneous visits in the queuing phenomenon during planning. d)A four-arm cooperative harvester was used to verify the effectiveness of the system, where the ergodic picking of each arm can be achieved without conflicts within a minimum duration. The framework can be generalized to many configurations of harvesters, suitable for robots with different number of arms, different varieties of fruit, and different ranges of overlapped domains. |
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| Keywords: | robots control harvesting task planning cooperative work traveling salesman problem genetic algorithm |
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