Sampling-Based Motion Planning with Scene Graphs Under Perception Constraints
Overview
This paper introduces MOPS-PRM, a motion planning method for high-degree-of-freedom robots that incorporates perception constraints for monitoring multiple objects or humans. By embedding perception costs into a scene graph representation, the approach selectively samples configurations to satisfy user-preferred viewpoints while executing tasks. Extensive evaluations in simulation and real-world settings show approximately 36% improvement in average detected objects and 17% better track rate compared to other perception-constrained baselines, with comparable planning times and path lengths.
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Reported quantity · percent improvement in detected objects: 36 other · Basis not reported
“and used to selectively sample configurations for roadmap construction, implicitly enforcing the perception constraints. Our method is extensively validated in both simulated and real-world experiments, achieving more than ~36% improvement in the average number of detected objects and ~17% better track rate against other perception-constrained baselines, with comparable planning times and path lengths.”
Exact source · revision 1Source owner not reported
Reported quantity · percent improvement in track rate: 17 other · Basis not reported
“and used to selectively sample configurations for roadmap construction, implicitly enforcing the perception constraints. Our method is extensively validated in both simulated and real-world experiments, achieving more than ~36% improvement in the average number of detected objects and ~17% better track rate against other perception-constrained baselines, with comparable planning times and path lengths.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalSampling-Based Motion Planning with Scene Graphs Under Perception Constraints
This paper introduces MOPS-PRM, a motion planning method for high-degree-of-freedom robots that incorporates perception constraints for monitoring multiple objects or humans. By embedding perception costs into a scene graph representation, the approach selectively samples configurations to satisfy user-preferred viewpoints while executing tasks. Extensive evaluations in simulation and real-world settings show approximately 36% improvement in average detected objects and 17% better track rate compared to other perception-constrained baselines, with comparable planning times and path lengths.
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