Industrial Robot Fly-By Path Optimization for Collision-Free Energy Saving
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Solution Overview
Problem
Current industrial robotic path planning methods are inefficient in optimizing energy savings and reducing cycle time, especially when collisions are detected, as they rely on manual trial and error and are dependent on programmer expertise, and fail to accurately predict trajectories and cycle times with the inclusion of zones.
Innovation Solution
A method that initializes clone paths where collisions are detected, applies mutations to generate candidate paths, simulates robotic movement, removes colliding paths, calculates breed ratings, and stores the path with the lowest rating to determine an optimal collision-free path, using a directed acyclic graph to optimize energy consumption and cycle time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual trial and error methods are used for robotic path planning, then programmer expertise can handle collision avoidance, but energy optimization and cycle time reduction are inefficient
Solution Approach 1:
The system performs self-optimization of robotic paths through automated algorithms that evaluate multiple candidate paths, calculate energy consumption, and select optimal trajectories without requiring manual programmer intervention for each path adjustment
Solution Approach 2:
The patent replaces manual trial-and-error mechanical adjustment with computer-based algorithms including genetic algorithms and simulated annealing that automatically optimize path parameters, zone permutations, and fly-by values to minimize energy consumption and cycle time
2Reliability
If traditional path mutation methods are used when collision is detected, then collision-free paths can be found, but energy savings and cycle time optimization are not achieved
Solution Approach 1:
The system performs preliminary evaluation of candidate paths by calculating breed ratings that predict energy consumption and cycle time before actual execution, allowing selection of optimal paths that are both collision-free and energy-efficient
Solution Approach 2:
The patent optimizes multiple path parameters simultaneously including zone permutations, fly-by values, and intermediate location selections to achieve both collision avoidance and energy minimization, using algorithms that evaluate how parameter changes affect both safety and efficiency
3Ease of operation
If programmer expertise is dependent for path planning, then manual adjustment can be made, but automation and efficiency are reduced
Solution Approach 1:
The system achieves self-service automation where the robotic system automatically generates, evaluates, and selects optimized paths using embedded algorithms that calculate energy consumption, detect collisions, and determine optimal trajectories without external programmer intervention
Solution Approach 2:
The patent implements feedback mechanisms where the system evaluates candidate paths using breed ratings based on simulated energy consumption and cycle time, then uses this feedback to guide the optimization algorithm toward selecting paths that minimize energy usage while maintaining collision-free operation
4Manufacturing precision
If zones are included in path planning, then more precise control is achieved, but trajectory prediction accuracy and cycle time calculation become unreliable
Solution Approach 1:
The system performs preliminary simulation of robotic movement along candidate paths to accurately predict trajectories and calculate cycle times before selecting the optimal path, ensuring that zone-based precision control does not compromise prediction accuracy
Data Source
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AI summary
Methods for optimizing energy savings and reducing cycle time (154) for mutating an industrial robotic path when a collision is detected. A method includes initializing a plurality of clone paths (240) where a collision was detected, wherein a clone path (240) is a clone of the initial path (166) and the initial path (166) comprises a source location (210), a plurality of intermediate locations (220), and a target location (230); for each clone path (240), determining a candidate path (172) to store in a population (164), determining an optimal breed (174) comprising the candidate path (172) with an optimal rating (170), wherein the optimal rating (170) is determined by the smallest breed rating (168) in the population (164), and returning the optimal breed (174).