Robotic Motion Planning for Under-Constrained Tool Poses
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Robotic systems face challenges in efficiently planning and executing tasks with under-constrained motion, leading to potential collisions and suboptimal performance, as existing technologies lack effective automation and precision in motion planning.
Innovation Solution
A motion planning system that includes a planning module capable of automating task execution by determining optimal joint configurations for robotic systems, using heuristics and cost function analysis to identify the lowest-cost configuration that minimizes collisions and achieves precise target poses, while considering tolerance and feasibility ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional motion planning methods are used for robotic systems with under-constrained motion, then the system can execute tasks with basic functionality, but the system experiences increased likelihood of collisions and suboptimal performance due to lack of effective automation and precision
Solution Approach 1:
The system performs preliminary motion planning by determining a nominal pose for the tool and identifying multiple possible joint configurations before task execution. This advance planning allows the system to predict potential collisions and select optimal configurations, improving reliability without adding real-time complexity.
Solution Approach 2:
The system uses cost function analysis to evaluate multiple possible joint configurations and selects the optimal one based on predicted collision likelihood and task performance. This feedback mechanism enables automated decision-making that improves collision avoidance while maintaining systematic complexity management.
2Measurement precision
If manual motion planning is used for robotic tasks, then the system can execute tasks with simple control, but the system lacks precision and requires human intervention to solve under-constrained problems
Solution Approach 1:
The system autonomously solves under-constrained motion planning problems by automatically determining nominal poses, identifying multiple joint configurations, and selecting optimal configurations through cost function analysis. This self-service capability eliminates the need for human intervention while achieving high precision in tool pose control.
Solution Approach 2:
The system changes the approach from manual parameter specification to automated parameter optimization. By using cost function analysis to evaluate multiple joint configurations and select the optimal one, the system achieves precise tool pose control automatically, improving both precision and automation extent.
3Manufacturing precision
If the system evaluates multiple joint configurations to achieve optimal task execution, then the system can minimize collisions and improve precision, but the computational cost and time for motion planning increases
Solution Approach 1:
The system evaluates multiple possible joint configurations but uses cost function analysis to efficiently identify the optimal configuration without exhaustively analyzing all possibilities. This partial evaluation approach achieves high precision in task execution while minimizing computational time by focusing on the most promising configurations.
4Productivity
If the system uses heuristics to predict tool motion and identify optimal configurations, then the system can improve task execution efficiency, but the system requires complex algorithms and computational resources
Solution Approach 1:
The system replaces traditional mechanical trial-and-error motion planning with computational heuristics and cost function analysis. This substitution of computational methods for mechanical exploration improves task execution efficiency by predicting optimal configurations in advance, while the structured algorithmic approach manages computational complexity systematically.
Data Source
AI summary
Systems, methods, devices, and other techniques are described for planning motions of one or more robots to perform at least one specified task. In some implementations, a task to execute with a robotic system using a tool is identified. A partially constrained pose is identified for the tool that is to apply during execution of the task. A set of possible constraints for the unconstrained pose parameter are selected for each unconstrained pose parameter. The sets of possible constraints are evaluated for the unconstrained pose parameters with respect to one or more task execution criteria. A nominal pose is determined for the tool based on a result of evaluating the sets of possible constraints for the unconstrained pose parameters with respect to the one or more task execution criteria. The robotic system is then directed to execute the task, including positioning the tool according to the nominal pose.