Probabilistic Robot Motion Planning With Cached Swept Volumes
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Solution Overview
Problem
Motion planning systems for robotics face inefficiencies in collision detection and path adjustment due to the need for real-time computation of swept volumes, which can lead to delays and increased computational complexity, especially in environments with multiple robots.
Innovation Solution
The implementation of a probabilistic roadmap (PRM) with cached swept volume profile data allows for pre-computation of swept volumes during the construction phase, enabling efficient collision detection and dynamic path adjustments during the query phase by using pre-calculated data to identify and avoid potential collisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If swept volumes are computed in real-time during the query phase, then collision detection accuracy is improved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent pre-computes swept volume profiles for all edges in the probabilistic roadmap during the construction phase and caches them. This preliminary action stores the computational results in advance, eliminating the need for real-time swept volume calculations during the query phase, thus resolving the contradiction between detection accuracy and computational complexity
Solution Approach 2:
The patent creates simplified representations (copies) of swept volumes in the form of profile data structures that capture essential collision information without requiring full geometric computations. These copied profile data can be quickly queried and compared during path planning, maintaining detection accuracy while reducing computational burden
2Reliability
If swept volumes are re-computed for each path adjustment, then collision detection reliability is improved, but processing speed decreases
Solution Approach 1:
The system performs swept volume computations in advance during the construction phase and caches the results. When path adjustments are needed during the query phase, the system reliably uses the pre-computed swept volume profiles instead of re-computing them, thus maintaining collision detection reliability while significantly improving processing speed
Solution Approach 2:
The patent implements a dynamic query phase that adapts to different path planning scenarios by efficiently querying the cached swept volume profiles. The system can dynamically adjust paths using the pre-computed data without undergoing expensive re-computations, enabling fast and reliable collision detection for multiple robots
3Measurement precision
If multiple robots coordinate movement in shared spaces with real-time swept volume calculation, then path collision detection accuracy is improved, but computational time increases
Solution Approach 1:
The patent pre-computes and caches swept volume profiles for all possible robot paths during the construction phase. When coordinating multiple robots in shared spaces, the system queries these cached profiles to detect potential collisions between robot paths, achieving accurate collision detection without the computational overhead of real-time swept volume calculations for each robot pair
Solution Approach 2:
The cached swept volume profiles serve multiple functions: they are used for collision detection between different robots, path validation, and dynamic path adjustment. This universal application of pre-computed data across multiple robots and scenarios improves coordination accuracy while minimizing computational time
Data Source
AI summary
Techniques and systems are disclosed for using swept volume profile data cached in association with a PRM to improve various aspects of motion planning for a robot. In some implementations, a first probabilistic road map representing possible paths to be travelled by a robot within a physical area is generated. An initial path for the robot within the first probabilistic road map is determined. Data indicating a second probabilistic road map representing a path to be travelled by a movable object within the physical area is obtained. A potential obstruction associated with one or more edges included in the subset of edges is detected. An adjusted path for the robot within the first probabilistic road map is then determined based on the potential obstruction.