Robot Tool Pose Planning for Under-Constrained Motion Tasks
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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, 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
1Measurement precision
If automated motion planning is implemented, then task execution precision is improved, but system complexity increases
Solution Approach 1:
The motion planning system is divided into distinct functional modules: a planning module that generates motion plans, a heuristic module that evaluates configurations, and a cost function module that optimizes trajectories. This segmentation allows each module to specialize in specific computational tasks, improving overall precision while managing complexity through modular architecture.
Solution Approach 2:
The system performs preliminary motion planning and heuristic evaluation before actual task execution. By pre-computing optimal joint configurations and evaluating potential collision scenarios in advance, the system achieves high execution precision without requiring complex real-time computation during task performance.
2Reliability
If heuristic evaluation is used to identify optimal configurations, then collision likelihood is reduced, but computational time increases
Solution Approach 1:
The heuristic evaluation performs partial analysis by focusing only on critical configuration parameters that significantly impact collision risk, rather than evaluating all possible joint configurations exhaustively. This selective evaluation reduces computational time while maintaining high reliability in collision avoidance.
Solution Approach 2:
The system replaces traditional mechanical trial-and-error configuration methods with computational heuristic evaluation. By using algorithms to predict and evaluate joint configurations, the system achieves reliable collision avoidance without the time-consuming process of physical testing and adjustment.
3Productivity
If cost function analysis is performed to optimize joint configurations, then task execution efficiency is improved, but processing requirements increase
Solution Approach 1:
The cost function analysis optimizes task execution by dynamically adjusting joint configuration parameters based on calculated costs. The system evaluates multiple parameter combinations (joint angles, velocities, accelerations) and selects configurations that minimize execution time and energy consumption, thereby improving productivity through systematic parameter optimization.
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.