Multi-vehicle path planning using scalar utility score
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Solution Overview
Problem
The multi-objective conflict-based search (MO-CBS) algorithm is computationally expensive due to the need to compute multiple Pareto-optimal solutions for the multi-agent path finding (MAPF) problem.
Innovation Solution
A path finding apparatus that uses a processor to acquire vehicle and map information, and determines a path set for multiple vehicles using a path planning algorithm that evaluates paths based on a utility score representing the optimization of multiple objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the MO-CBS algorithm is used to handle multiple objectives in MAPF, then multiple Pareto-optimal solutions can be generated, but the computational cost and runtime increase significantly
Solution Approach 1:
The patent changes the parameter representation from multiple Pareto-optimal solutions to a single solution evaluated by a utility function that aggregates multiple objectives into a scalar value. This parameter transformation allows the system to maintain multi-objective optimization capability while reducing computational complexity and runtime.
Solution Approach 2:
The patent extracts the essential evaluation capability from the MO-CBS algorithm by using a utility function that can assess path quality for multiple objectives without requiring generation of multiple Pareto-optimal solutions. This extraction maintains the core functionality while eliminating the computationally expensive parts.
2Adaptability or versatility
If the MO-CBS algorithm generates multiple Pareto-optimal solutions, then comprehensive path options are provided, but the computational expense increases
Solution Approach 1:
The patent transforms the computational approach by changing from generating multiple Pareto-optimal solutions to evaluating a single solution using a utility function that aggregates multiple objectives. This parameter change significantly reduces computational energy consumption while preserving multi-objective optimization capabilities.
Solution Approach 2:
The patent replaces the expensive computation of multiple Pareto-optimal solutions with a more economical utility function evaluation that can be performed repeatedly and efficiently. This substitution uses computationally cheaper operations to achieve the same optimization goals.
3Productivity
If a single objective evaluation method is used, then the path finding is simple and fast, but multiple objectives cannot be handled
Solution Approach 1:
The patent creates a universal utility function that can evaluate paths against multiple objectives simultaneously. This multi-functional evaluation approach allows the system to maintain fast path finding speeds while handling multiple objectives, as the utility function aggregates all objective evaluations into a single scalar assessment.
Solution Approach 2:
The patent merges multiple objective evaluations into a single utility function that produces one scalar value representing overall path quality. This combining approach allows the system to handle multiple objectives without the computational overhead of generating multiple separate solution sets, thus maintaining high productivity.
Data Source
AI summary
A path finding apparatus acquires vehicle information and map information, and determines a path set that includes a path for each of the vehicles in the vehicle set. The path set is determined using the vehicle information and the map information. The paths in the path set do not conflict with each other. To determine the path set, the path finding apparatus performs a path planning algorithm to generate the path for each of the vehicles. The path planning algorithm evaluates the path based on a utility score of the path that is a scalar value representing how much the path achieves a plurality of objectives.