Основні поняття
SwarmPRM proposes a hierarchical, risk-aware motion planning approach using Gaussian roadmap for large-scale swarm robotic systems.
Анотація
The content introduces SwarmPRM, a novel approach for motion planning in large-scale swarm robotic systems. It discusses the challenges faced by traditional methods and presents the key features of SwarmPRM, including its hierarchical structure, risk awareness, and scalability. The content details the construction of a risk-aware Gaussian roadmap and the utilization of CVaR for collision checking. Simulation results demonstrate the superior performance of SwarmPRM over state-of-the-art methods in terms of computational efficiency and trajectory quality.
I. Introduction
- Large-scale swarm robotic systems hold promise for diverse tasks.
- Traditional motion planning faces scalability issues.
- Sampling-based algorithms show potential in swarm robotics.
II. Problem Formulation and Background
- Defines the problem of trajectory planning for swarm robots.
- Introduces sampling-based algorithms like PRM.
- Discusses optimal transport theory and Wasserstein metric.
III. Constructing Risk-Aware Gaussian Roadmap
- Details roadmap construction using Gaussian distributions.
- Explains collision checking with CVaR as a risk measure.
IV. SwarmPRM Approach for Hierarchical Motion Planning
- Outlines macroscopic and microscopic stages of motion planning.
- Describes LP formulation to compute optimal GMM trajectory.
V. Simulation Results
- Evaluates SwarmPRM in two complex environments.
- Compares performance with benchmark methods.
- Demonstrates effectiveness of CVaR in collision avoidance.
VI. Conclusion
- Summarizes the development and benefits of SwarmPRM.
- Highlights scalability, risk awareness, and superior performance.
Статистика
SwarmPRM outperforms state-of-the-art methods in computational efficiency, scalability, and trajectory quality while offering flexibility in designing swarm behaviors.
CVaR is utilized to assess likelihood of collisions between PDF samples and obstacles.
Цитати
"SwarmPRM demonstrates superiority over benchmark methods."
"CVaR enables risk-awareness in collision checking."