Motion Planning Graph UI for Dynamic Robot Configurations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current motion planning technologies face challenges in efficiently generating motion planning graphs for robots with changing characteristics, such as varying end effectors or objects being held, and in handling dynamic environments, due to limitations in processor storage and computational efficiency.
Innovation Solution
A processor-executable motion planning graph toolkit provides an intuitive user interface for generating kinematic-checked and collision-checked motion planning graphs, allowing users to interact with digital robot models and environments, autonomously generating poses and edges, and dynamically switching between planning graphs based on changing robot dimensions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If motion planning graphs are generated for robots with changing characteristics (varying end effectors, objects held), then adaptability improves, but processor storage requirements increase
Solution Approach 1:
The system dynamically switches between different pre-generated motion planning graphs based on the robot's current configuration (end effector type, objects held). Instead of storing all possible configurations simultaneously, the system generates and stores multiple planning graphs for different configurations, then selects the appropriate graph at runtime based on current robot state, making the storage system dynamic and adaptive.
Solution Approach 2:
The motion planning problem is segmented into multiple discrete configurations, each with its own dedicated motion planning graph. The configuration space is divided based on different end effectors and objects, with separate planning graphs generated for each segment. This allows the system to handle changing characteristics by switching between segments rather than managing a single complex graph.
2Reliability
If comprehensive motion planning graphs are generated to account for all robot characteristics and obstacles, then collision avoidance improves, but computational time increases
Solution Approach 1:
The system performs preliminary action by pre-generating motion planning graphs for different robot configurations and storing them for future use. During runtime, when the robot needs to execute a task, the appropriate pre-generated graph is already available, eliminating the need for time-consuming real-time graph generation and ensuring reliable collision avoidance.
3Speed
If real-time motion planning adjustments are made for dynamic environments, then responsiveness improves, but computational load increases
Solution Approach 1:
The system performs preliminary action by pre-generating motion planning graphs for different robot configurations and storing them for future use. During runtime, when the robot needs to execute a task, the appropriate pre-generated graph is already available, eliminating the need for time-consuming real-time graph generation and ensuring reliable collision avoidance.
Data Source
AI summary
A processor-based system provides a user interface to facilitate generation of motion planning graphs or roadmaps, for example via autonomous generate of additional poses, for example neighboring poses, based on specified seed or origin pose, and/or generate or sub-lattice connecting poses and/or edges to couple separate regions or sub-lattices of the motion planning graph together. Such may include performing collision-checking and/or kinematic checking (e.g., taking into account kinematic constraints of robots being modeled). The resulting motion planning graphs or roadmaps are useful in controlling robots during runtime. Robots may include robots with appendages and/or robots in the form of autonomous vehicles.