发表机构
Università di Trento; University of Trento（特伦托大学; 特伦托大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出DeCoST方法，通过两阶段解耦离散-连续优化与服务时间引导轨迹，解决共享空间中带时间窗口和可变利润的定向问题，确保无碰撞轨迹并保持任务质量。
AI 中文摘要
定向问题（OP）在现实世界中有着广泛的应用，并在人机协作中具有巨大潜力。然而，现有方法在共享工作空间中难以同时确保安全可行的轨迹和高质量的任务执行。为此，本研究探讨了具有时间窗口和可变利润的定向问题（OPTWVP）。提出了一种两阶段解耦离散-连续优化与服务时间引导轨迹（DeCoST）方法，以有效解决共享空间中的OPTWVP。同时，引入了节点安全感知时间窗口和离散化工作空间，以确保末端执行器与人类之间的无碰撞轨迹。初步结果验证了DeCoST在生成无碰撞轨迹规划的同时保持定向任务质量的有效性。
英文摘要
Orienteering problem (OP) has wide real-world applications and also great potential in human-robot collaboration. However, existing approaches struggle to simultaneously ensure safe and feasible trajectories while achieving high-quality task execution in shared workspaces. To this end, this work studies the OP with time windows and variable profits (OPTWVP). A two-stage DEcoupled discrete-Continuous Optimization with Service-time-guided Trajectory (DeCoST) approach is proposed to effectively solve OPTWVP in shared spaces. Meanwhile, the safety-aware time windows of nodes and the discretized workspace are introduced to ensure collision-free trajectories between the end effector and the human. Preliminary results validate the effectiveness of DeCoST in generating collision-free trajectory plans while preserving the quality of orienteering tasks.
CommentsPresented at I-RIM 3D 2025
