Parallel Robot Motion Planning for Collision-Free Trajectories
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Solution Overview
Problem
Existing collision-free motion generation techniques are inefficient in terms of memory, time, and computing resources, leading to suboptimal performance.
Innovation Solution
The implementation of the CuRobo functionality, which utilizes parallel processing units (PPUs) to perform collision-free motion generation through algorithms like inverse kinematics (IK) optimization, gradient-based optimization, and geometric planning, enabling simultaneous solution of multiple motion optimization instances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional sequential motion generation methods are used, then memory and computing resources are consumed, but the time required for collision-free motion planning becomes excessively long
Solution Approach 1:
The motion planning problem is divided into multiple independent sub-problems by separating collision checking and trajectory optimization. Collision checking is performed in a separate thread that pre-computes collision-free paths, while trajectory optimization is performed in another thread that refines these paths. This segmentation allows parallel processing and significantly reduces the time required for collision-free motion planning.
Solution Approach 2:
The patent introduces a temporal dimension to the motion planning process by performing collision checking and trajectory optimization at different times. Collision checking is performed in advance to generate a set of collision-free paths, and then trajectory optimization is performed on these pre-computed paths. This temporal separation enables efficient resource utilization and reduces overall computation time.
2Power
If parallel processing units are introduced to speed up motion generation, then computing power increases, but device complexity increases
Solution Approach 1:
The system uses a universal computing platform with standard parallel processing units (such as GPUs or multi-core CPUs) that can be configured for different motion planning tasks. The same hardware architecture supports both collision checking and trajectory optimization, eliminating the need for specialized hardware and reducing overall system complexity while maintaining high computing power.
Solution Approach 2:
The patent introduces a message passing mechanism as an intermediary between the collision checking thread and the trajectory optimization thread. This mediator facilitates efficient communication and data exchange between parallel processing units, simplifying the system architecture by providing a standardized interface for coordination without requiring complex synchronization protocols.
3Reliability
If memory resources are allocated for storing trajectory data and collision information, then collision-free motion planning can be performed, but the amount of memory required increases
Solution Approach 1:
The patent extracts collision checking from the main trajectory optimization process and performs it in a separate thread. This extraction allows collision-free paths to be pre-computed and stored efficiently, reducing the memory required during the optimization process. The separated collision checking thread can discard collision information after use, minimizing peak memory requirements while maintaining reliable collision-free motion planning.
Solution Approach 2:
The system discards collision check results after they have been used to guide trajectory optimization, and recovers only the essential collision-free path information needed for motion generation. This selective retention of data reduces memory consumption by removing redundant collision information while preserving the necessary trajectory data for executing collision-free motion plans.
Data Source
AI summary
Apparatuses, systems, and techniques to perform collision-free motion generation (e.g., to operate a real-world or virtual robot). In at least one embodiment, at least a portion of the collision-free motion generation is performed in parallel.