Sampling-Based Motion Planning with Scene Graphs Under Perception Constraints
Sampling-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.
The authors present MOPS-PRM, a roadmap-based motion planner for high-degree-of-freedom robots that integrates multi-object perception constraints into sampling-based planning using scene graphs; reported improvements in detection count and tracking rate are based on simulated and real-world experiments described in the abstract.
Source: arXiv Robotics — research abstracts · Read original article ↗
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percent improvement in detected objects
≈36
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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.
Open source S5percent improvement in track rate
≈17
View original evidence
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.
Open source S5
- paper: not_reported
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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.
Open source S5
Sampling-Based Motion Planning with Scene Graphs Under Perception Constraints
Open source S1
arXiv:2603.03514v2 Announce Type: replace Abstract: It will be increasingly common for robots to operate in cluttered human-centered environments such as homes, workplaces, and hospitals, where the robot is often tasked to maintain perception constraints, such as monitoring people or multiple objects, for safety and reliability while executing its task. However, existing perception-aware approaches typically focus on
Open source S2
low-degree-of-freedom (DoF) systems or only consider a single object in the context of high-DoF robots. This motivates us to consider the problem of perception-aware motion planning for high-DoF robots that accounts for multi-object monitoring constraints. We employ a scene graph representation of the environment, offering a great potential for incorporating long-horizon task and motion planning thanks to its rich s
Open source S3
emantic and spatial information. However, it does not capture perception-constrained information, such as the viewpoints the user prefers. To address these challenges, we propose MOPS-PRM, a roadmap-based motion planner, that integrates the perception cost of observing multiple objects or humans directly into motion planning for high-DoF robots. The perception cost is embedded to each object as part of a scene graph,
Open source S4
Source：arXiv Robotics — research abstracts · arxiv.org