aiqbench analysis
This paper addresses the critical challenge of safe human-robot collaboration by optimizing trajectories in shared workspaces using a novel two-stage optimization framework. By integrating safety-aware time windows and discrete workspace modeling, the proposed method ensures collision-free paths while maintaining high-quality task execution for orienteering problems. The research advances practical robotics applications where AI-driven planning must coexist safely with human operators.
Trajectory-Safe Orienteering for Human-Robot Shared Environments
arXiv:2609.34485v1 Announce Type: new 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.
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.
Robotics (cs.RO)
arXiv:2609.34485 [cs.RO]
(or arXiv:2609.34485v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.34485 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 203 words
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