SAP-CoPE: Social-Aware Planning using Cooperative Pose Estimation with Infrastructure Sensor Nodes

Abstract:Autonomous mobile robots must operate smoothly in human-populated indoor environments, where onboard-only sensing suffers from limited perception range and occlusions, and socially compliant motion planning must account for human psychological comfort zones. These factors complicate accurate recognition of human intentions and the generation of comfortable, social-aware trajectories. To address these challenges, we propose SAP-CoPE, an indoor navigation framework that integrates cooperative infrastructure sensing, human pose estimation, and a social-aware MPC planner. In the perception module, an uncertainty-aware optimization problem is formulated to estimate 3D human poses from camera observations, with optional incorporation of sparse LiDAR point-cloud measurements, while considering human joint coherence. In the planning module, the estimated human pose and orientation are used to construct a personal-space (PS) field within an MPC framework, enabling the robot to account for both physical safety and psychological comfort. Extensive real-world evaluations demonstrate the effectiveness of the proposed approach in generating social-aware trajectories for autonomous systems.

Robotics (cs.RO)

arXiv:2504.05727 [cs.RO]

(or arXiv:2504.05727v3 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2504.05727 Focus to learn more arXiv-issued DOI via DataCite

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.