Manipulator Trajectory Planning for Real-Time Collision-Free Finishing
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Solution Overview
Problem
Current robotic manipulators are not suitable for small production volume operations due to long programming time and low reusability, as they require predefined motions and are unable to generate collision-free, time-optimal, and energy-optimal trajectories in real-time, especially in dynamic environments with high degrees of freedom.
Innovation Solution
An anytime graph search-based trajectory planning algorithm that computes collision-free trajectories for high DOF manipulation systems in real-time, using a processor to determine and select positions based on costs, and actuators to move the tool, with enhancements for computational efficiency, collision detection, and adaptive heuristic switching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional predefined motion programming is used for robotic manipulators, then the system is simple to operate, but the programming time is long and reusability is low
Solution Approach 1:
The trajectory planning system performs self-service by automatically generating collision-free trajectories in real-time without requiring expert human operators to program predefined motions. The system uses stochastic trajectory planners that autonomously compute optimal paths considering workspace constraints, other manipulators' positions, and task requirements, thereby eliminating long programming times and enabling reusability across different small production volume operations.
2Productivity
If stochastic trajectory planners are used to generate real-time trajectories, then the computation speed is improved, but the trajectories are neither time nor energy optimal and contain redundant motions
Solution Approach 1:
The system dynamically adapts the trajectory planning approach by combining stochastic methods for real-time computation with optimization techniques. The trajectory planner continuously adjusts motion parameters based on current workspace conditions, other manipulators' positions, and task requirements, enabling real-time generation of trajectories that are both computationally feasible and optimized for time and energy efficiency without redundant motions.
3Manufacturing precision
If expert human operators program predefined motions for mass production lines, then the trajectories are optimized, but the system cannot adapt to errors in the operating environment
Solution Approach 1:
The trajectory planning system incorporates feedback mechanisms that continuously monitor the operating environment, including positions of other manipulators, workspace constraints, and task progress. Based on this feedback, the system dynamically adjusts and regenerates trajectories in real-time, enabling adaptation to environmental changes and errors without suspending the production process, while maintaining optimized motion paths.
4Productivity
If robotic manipulators are deployed in small production volume operations with high DOF, then the productivity is improved, but the computational complexity increases significantly
Solution Approach 1:
The trajectory planning system segments the high-dimensional configuration space into manageable subspaces or uses hierarchical planning approaches. By breaking down the complex computation into smaller, more tractable problems, the system can handle high DOF manipulators in small production volume operations without overwhelming computational complexity, enabling real-time trajectory generation for productive operations.
Data Source
AI summary
Methods, systems, and apparatus for automatically moving a tool attached to a robotic manipulator from a start position to a goal position. The method includes determining, using a processor, a plurality of next possible positions from the start position. The method includes selecting a second position from the plurality of next possible positions based on respective costs associated with moving the tool from the start position to each of the possible positions in the plurality of next possible positions. The method includes moving, using a plurality of actuators, the tool to the second position. The method includes determining an updated plurality of next possible positions, selecting a next position, and moving the tool to the next position until the goal position is reached.