Optimization of Robotic Task Sequencing Problems by Crowding Evolutionary Algorithms
研究用拥挤进化算法求解机器人任务排序问题，为每个任务点找到多种逆运动学关节配置，并开发启发式双向参考机制高效求解预设和最优制造序列规划问题。
This study solved the robotic task sequence planning problem of scheduling joint-space tours so that each task point visit is made according to expected manufacturing criteria. Multiple solutions for robotic inverse kinematic (RIK) problems were obtained for two task sequencing problems: 1) preset manufacturing sequence planning (Preset-MSP) and 2) optimal manufacturing sequence planning (Opt-MSP). First, a real-coded twin-space crowding evolutionary algorithm (TC-EA) was developed and used to explore multiple RIK joint configurations for each task point. Then, a heuristic bidirectional reference mechanism (BRM) was developed and used for efficiently solving Preset-MSP problems. By integrating BRM, the proposed discrete-coded TC-EA also efficiently solved Opt-MSP problems. To validate the proposed approach, multiple multimodal benchmark functions and task sequencing test cases were used to compare the solving capability of the proposed TC-EA and other evolutionary multimodal solvers. The experimental results showed that the proposed methods obtained better or at least comparable solutions for all test problems. For decision makers, the proposed methods have practical applications for exploring and comparing multiple robotic manufacturing plans.