Robot Motion Planning for Under-Constrained Tool Poses
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Solution Overview
Problem
Robotic systems face challenges in efficiently planning and executing tasks with under-constrained motion, leading to potential collisions and suboptimal performance, particularly in industrial settings where precision and accuracy are critical.
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 predict tool motion and minimize collisions, while defining tolerance and feasibility ranges to ensure precise task completion.
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 simple control, but the robotic system experiences collisions and suboptimal performance due to inability to predict and optimize tool motion
Solution Approach 1:
The system performs preliminary motion prediction and collision analysis before executing the task. The planning module predicts tool motion trajectories and identifies potential collisions in advance, allowing the robotic system to adjust its motion plan to avoid collisions and achieve optimal performance.
Solution Approach 2:
The system dynamically adjusts joint configurations based on predicted tool motion and collision risks. The planning module continuously evaluates pose parameters and modifies the motion plan in real-time to optimize task execution while avoiding collisions, making the system adaptive to under-constrained motion conditions.
2Adaptability or versatility
If the robotic system uses fixed joint configurations to execute tasks, then the control system is simple, but the system cannot adapt to under-constrained motion leading to collisions and suboptimal performance
Solution Approach 1:
The planning module pre-calculates optimal joint configurations by predicting tool motion trajectories and analyzing potential collisions before task execution. This preliminary analysis allows the system to adapt to under-constrained motion without requiring complex real-time control adjustments during execution.
Solution Approach 2:
The system uses feedback from pose parameter evaluation and collision analysis to continuously refine joint configuration selections. The planning module monitors the robotic system's state and adjusts configurations to maintain optimal performance while adapting to changing conditions during task execution.
3Manufacturing precision
If the system performs detailed motion planning and collision analysis, then task execution precision is improved, but the computational time and processing resources increase
Solution Approach 1:
The system performs partial motion planning by focusing computational resources on critical aspects such as collision-prone areas and key pose parameters rather than analyzing every possible motion trajectory in detail. This selective approach maintains task execution precision while reducing overall computational time.
4Productivity
If the robotic system operates without predictive motion analysis, then the control system is simple and fast, but collisions occur and task performance is suboptimal
Solution Approach 1:
The planning module performs preliminary prediction of tool motion and identification of potential collisions before the robotic system begins task execution. This advance planning enables the system to execute tasks more efficiently by avoiding collisions and optimizing motion trajectories, while the complexity is contained in the pre-computation phase rather than real-time control.
Data Source
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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.