Robot Path Planning With Pareto Selection Stability
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Solution Overview
Problem
Existing robot path planning methods struggle to simultaneously optimize multiple conflicting objectives such as travel time and energy consumption while avoiding undesirable changes due to rank reversal phenomena.
Innovation Solution
A method and system for planning robot paths using Pareto optimal paths and decision-making techniques to mitigate rank reversal, incorporating fuzzy functions and Choquet-integral for optimizing multiple objectives, and updating paths dynamically to adapt to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple objective functions are optimized simultaneously using traditional methods, then the robot path planning can consider multiple factors (travel time, energy consumption), but the system suffers from rank reversal phenomena causing undesirable changes in path selection
Solution Approach 1:
The patent introduces an intermediary mechanism (decision-making framework with stability criteria) that mediates between multiple objective functions and the final path selection. This intermediary layer prevents direct rank reversal by filtering and stabilizing the selection process, ensuring that path choices remain consistent even when objective function values fluctuate.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors the stability of path selections and adjusts the decision-making process accordingly. When rank reversal is detected or anticipated, the system feeds back to modify the selection criteria, preventing undesirable changes and maintaining reliable path selection across varying conditions.
2Adaptability or versatility
If the robot path is updated dynamically to adapt to changing conditions, then the robot can respond to new obstacles and priorities, but the path may change frequently due to rank reversal rather than actual environmental changes
Solution Approach 1:
The patent applies dynamics by making the path planning system adaptive to genuine environmental changes while filtering out spurious changes caused by rank reversal. The system dynamically adjusts its decision criteria based on the stability of objective function values, allowing legitimate path updates while preventing unnecessary fluctuations.
Solution Approach 2:
The patent prepares for potential rank reversal issues by implementing preventive measures before they cause problems. Stability criteria and validation mechanisms are built into the path selection process in advance, cushioning against the negative effects of rank reversal and ensuring smooth, reliable path transitions.
3Loss of information
If fuzzy functions and Choquet-integral are used to handle uncertainty in objective functions, then the system can better manage vagueness and ambiguity, but the computational complexity increases
Solution Approach 1:
The patent transforms the handling of uncertainty by changing the mathematical parameters and structures used in evaluation. By employing fuzzy functions and Choquet-integral, the system alters how objective function values are represented and combined, enabling more nuanced handling of uncertainty while managing computational complexity through efficient implementation strategies.
Data Source
AI summary
A method for planning a robot path of a robot arrangement having at least one robot includes providing an objective group of at least two objective functions to be optimized simultaneously by a robot path, each objective function mapping a robot path to a numeric value; determining first values of the objective functions; providing a first criterion for choosing a robot path from a path group of at least two robot paths which are pareto optimal with respect to the objective group so that for each of the robot paths none of the objective functions can be improved in value without degrading at least one of the other objective function values. The method further includes determining a first robot path based on the first values of the objective functions and the first criterion, and at least one cycle of the following steps: updating the criterion and/or the values of the objective functions, and determining an updated robot path based on the current values of the objective functions and the current criterion using decision making mitigating a rank reversal between robot paths of the path group.