Industrial Robot Trajectory Planning Using Worst-State Collision Search
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Solution Overview
Problem
Existing industrial robot collision avoidance motion planning techniques require dense waypoint spacing to reliably detect collisions, which increases computation time and can result in uneven robot motion, while sparse spacing may miss collisions with thin obstacles.
Innovation Solution
A robot collision avoidance motion planning technique using a worst state search and optimization, where a continuous trajectory is defined with sparsely spaced waypoints, and a worst state search identifies critical interference points, followed by optimization to improve these states until collision avoidance criteria are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dense waypoint spacing is used to reliably detect collisions with thin obstacles, then collision detection reliability is improved, but computation time increases and robot motion becomes uneven
Solution Approach 1:
The patent extracts only the critical portions of the trajectory where collisions with thin obstacles could occur, identified as 'worst states' between waypoints. Instead of analyzing all dense waypoints, the system identifies and focuses computational effort on these critical worst state locations, eliminating unnecessary computations at non-critical points while maintaining collision detection reliability.
Solution Approach 2:
The patent replaces the mechanical approach of using dense waypoint spacing with a computational optimization approach. Instead of relying on spatial density to ensure collision detection, the system uses mathematical optimization to identify worst states and applies targeted computations only at these critical locations, substituting brute-force spatial sampling with intelligent computational selection.
2Reliability
If dense waypoint spacing is used to reliably detect collisions, then collision detection reliability is improved, but robot motion becomes uneven and jerky
Solution Approach 1:
The patent extracts only the critical worst state locations between waypoints for collision analysis, rather than processing all dense waypoints. This extraction allows the system to maintain collision detection reliability while avoiding the need to slow down at every dense waypoint, thereby preserving motion smoothness.
Solution Approach 2:
The patent performs preliminary identification of worst state locations between waypoints before executing the motion. By pre-identifying where collisions could occur and planning avoidance around these specific points, the system ensures collision detection reliability without requiring continuous slowdowns throughout the trajectory, thus maintaining motion smoothness.
3Loss of time
If sparse waypoint spacing is used to reduce computation time, then computation time is reduced, but collision detection reliability deteriorates
Solution Approach 1:
The patent performs preliminary action by identifying worst state locations between sparse waypoints before motion execution. This preliminary identification ensures that even with sparse waypoint spacing, the system proactively detects potential collision zones and plans appropriate avoidance, maintaining collision detection reliability without requiring dense waypoint spacing.
Solution Approach 2:
The patent replaces the mechanical reliance on dense waypoint spacing with a computational optimization system that identifies worst states. This substitution allows sparse waypoint spacing to be used (reducing computation time) while the optimization algorithm compensates by accurately identifying critical collision risks that would be missed by simple sparse sampling.
4Ease of operation
If sparse waypoint spacing is used to ensure smooth robot motion, then motion smoothness is improved, but collision detection reliability deteriorates
Solution Approach 1:
The patent performs preliminary identification of worst state locations between sparse waypoints before motion execution. By proactively identifying where collisions could occur with sparse spacing, the system can plan avoidance paths in advance, ensuring both motion smoothness (from sparse spacing) and collision detection reliability (from preliminary worst state identification).
Solution Approach 2:
The patent replaces the mechanical approach of using dense waypoint spacing to ensure collision detection with a computational optimization approach. This substitution allows the system to use sparse waypoint spacing for smooth motion while the optimization algorithm identifies worst states and ensures collision detection reliability through intelligent computational analysis rather than spatial density.
Data Source
AI summary
A robot collision avoidance motion planning technique using a worst state search and optimization. The motion planning technique begins with a geometric definition of obstacles, start and goal points, and an initial set of waypoints which may be sparsely spaced. Given an inter-point interpolation method such as linear or spline, a continuous trajectory can be described as a function of the waypoints and an arc length parameter. A worst state search is then performed which finds a location between each adjacent pair of waypoints having a worst state of distance to obstacle, considering all parts of the robot and tool. A collision avoidance constraint is defined using the worst state locations, and an optimization of the waypoint locations is then performed to improve the worst states until all collisions are eliminated and an obstacle avoidance minimum distance criteria is met.