Trajectory-Safe Orienteering for Human-Robot Shared Environments

2026-09-28

AI Summary

This study addresses the challenge of simultaneously ensuring trajectory safety and execution quality for orientation-constrained tasks in human-robot shared environments. To this end, it proposes DeCoST, a two-stage optimization framework that reduces computational complexity by decoupling discrete and continuous optimization processes. Furthermore, this work introduces a novel service-time-guided trajectory generation mechanism that integrates workspace discretization with safety-aware time window modeling, thereby enabling dynamic obstacle avoidance between the end-effector and the human operator. Experimental results demonstrate that the proposed approach guarantees collision-free safe planning while effectively maintaining high-quality execution performance for orientation-constrained tasks.

Abstract

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.

Problem

• Orienteering Problem
• Human-Robot Collaboration
• Trajectory Safety
• Shared Workspaces

Innovation

• Orienteering Problem
• Human-Robot Collaboration
• Decoupled Optimization
• Collision-free Trajectory
• Safety-aware Time Windows