Autonomous Path Planning With Fixed-Point Guided PSO Initialization
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Solution Overview
Problem
Existing path planning methods for autonomous systems, such as self-driving agents, face low robustness due to uncontrollable parameter settings in Particle Swarm Optimization (PSO), leading to inefficiencies in finding the best path while avoiding obstacles.
Innovation Solution
The method converts the path optimization function into an equivalent fixed-point equation using fixed-point theorems, acquiring a complete simplex sequence to determine initial population size and particle positions for PSO, ensuring diversity and effectiveness in particle search directions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If PSO parameters are set independently using empirical reference or random generation, then the algorithm implementation is simple, but the robustness of the agent in path planning is reduced
Solution Approach 1:
The patent applies preliminary action by pre-calculating the complete simplex sequence from the path optimization function before initiating PSO. This sequence determines the initial population size and particle positions in advance, ensuring that particles are strategically distributed near extreme points of the optimization landscape. This preliminary setup enhances the algorithm's robustness and path planning effectiveness without complicating the PSO execution itself.
2Ease of operation
If the initial population size and particle positions are determined randomly, then the algorithm is easy to implement, but the diversity and effectiveness of particle search directions are reduced
Solution Approach 1:
The patent performs preliminary calculation of the complete simplex sequence derived from the path optimization function f(X) before PSO execution. This sequence provides predetermined initial particle positions that are strategically distributed near extreme points, ensuring diverse and effective search directions. The initial population size is also determined from this sequence, optimizing the balance between exploration and exploitation without requiring complex adaptive mechanisms during runtime.
3Device complexity
If traditional PSO is used without fixed-point theorem integration, then the computational process is simpler, but the accuracy in finding the best path is reduced
Solution Approach 1:
The patent integrates fixed-point theorem application in the preliminary stage by calculating the complete simplex sequence from the path optimization function f(X) and its derivative f'(X). This mathematical preprocessing identifies extreme points that guide initial particle placement, significantly improving path planning accuracy. The integration adds minimal computational complexity since it involves standard calculus operations performed once before the iterative PSO process begins.
Data Source
AI summary
The present invention provides a path planning method and system for self-driving of autonomous system, and relates to the technical field of autonomous systems. The method comprises following steps of: acquiring a path optimization function of an agent; converting, based on fixed-point theorems, the path optimization function of the agent into an equivalent fixed-point equation; acquiring a complete simplex sequence based on the fixed-point equation; and, determining, based on the complete simplex sequence, an initial population size and an initial position of particles for particle swarm optimization to obtain the best path planning of the agent. In the present invention, the extremal optimization of the path optimization function of the agent is converted into solving of the fixed-point equations, and initial parameters for particle swarm optimization are determined by the complete simplex sequence.