Robot Motion Planning With Modular Collision Mappings
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Solution Overview
Problem
Conventional motion planning approaches for robots require recalculation and storage of entire mappings when the robot or workspace configuration changes, leading to inefficient computational processes and significant memory requirements.
Innovation Solution
The method involves generating and storing modular mappings for each component of the robot and external objects, allowing new mappings to be built upon existing ones, enabling flexible adaptation to changes without recreating the entire mapping, and utilizing a structured roadmap to compress memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional motion planning approaches recalculate and store entire mappings when robot or workspace configuration changes, then complete and accurate collision detection is achieved, but memory requirements and computational overhead increase significantly
Solution Approach 1:
The patent segments the robot model into multiple components (e.g., links, end-effectors, tools) and generates separate mappings for each component rather than a single mapping for the entire robot. This segmentation allows the system to store and manage smaller, modular mapping data structures, reducing overall memory requirements while maintaining complete collision detection coverage when mappings are combined during query operations.
2Reliability
If conventional motion planning approaches recalculate entire mappings when configuration changes, then accurate collision detection is maintained, but computational time and processing overhead increase
Solution Approach 1:
The patent performs preliminary action by pre-calculating and storing mappings for individual robot components during system initialization or when components are added/removed. When the robot configuration changes, only the mappings for affected components need to be updated rather than recalculating the entire robot mapping. This preliminary segmentation and selective updating significantly reduces computational time and processing overhead while maintaining accurate collision detection.
3Adaptability or versatility
If modular mappings are generated for each robot component, then memory efficiency and adaptability to changes improve, but mapping complexity and data structure requirements increase
Solution Approach 1:
The patent employs merging by combining the individual component mappings during collision detection queries to form the complete robot mapping. Each component mapping contains information about its specific workspace occupancy, and these mappings are merged (unioned) during runtime to determine the total occupied space. This merging approach enables flexible adaptation to configuration changes while managing complexity through modular, composable data structures rather than requiring complex hierarchical or nested representations.
Data Source
AI summary
A method of performing motion planning for a robot in a workspace discretized into workspace elements includes generating or receiving a first model and determining a first set comprising one or more workspace elements that are at least partially in collision with the first model for each of a plurality of states and the respective transition(s) between those states. A first mapping is generated including information regarding the first set and the respective states and transition(s). The method further includes generating or receiving a second model extending from the first model and determining a second set including one or more further workspace elements, additional to those in the first set, that are at least partially in collision with the second model for each of the plurality of states and transitions between those states. A second mapping including information regarding said second set and the respective states and transition(s) is generated.