A navigation method for traffic uncertainty topological network

By modeling the traffic network as an uncertain topology network and constructing a reinforcement learning network of variational Markov processes, the problem of timely arrival for intelligent agents in complex environments is solved, and efficient and reliable navigation path planning is achieved.

CN121089769BActive Publication Date: 2026-02-27CHENGDU UNIV OF INFORMATION TECH
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Patent Information

Application Number
CN202511647618.1
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-11-11
Publication Date
2026-02-27
Estimated Expiration
2045-11-11

AI Technical Summary

Technical Problem

Existing technologies struggle to simultaneously account for network topology uncertainty and path travel time randomness in complex and uncertain environments, resulting in low probability of random and timely arrival of intelligent agents (such as autonomous vehicles and mobile robots), poor sample efficiency, and high real-time computational overhead.

Method used

The traffic network to be navigated is modeled as an uncertain topological network, and a reinforcement learning network based on variational Markov processes is constructed, including a variational policy gradient module and a mask temporal difference module. The policy and value network are updated through policy separation, and the optimal routing policy is output through iterative training.

Benefits of technology

It improves the probability of timely arrival of the agent in complex and uncertain environments, reduces the amount of samples required and training time, reduces computational complexity, and improves the reliability and efficiency of navigation.

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Abstract

The application discloses a kind of navigation methods for traffic uncertainty topological network, intelligent traffic path planning technical field, comprising: definition uncertainty parameter, the traffic network to be navigated is modeled as uncertainty topological network, and is converted into variational Markov process;Based on variational Markov process process, construct reinforcement learning network, including the policy network based on variational policy gradient module and the value network based on mask time difference module;Strategy network and value network are updated using off-policy, and the interaction of policy-value network is combined, and the reinforcement learning network is iteratively trained to convergence, and the optimal routing strategy is output;The optimal routing strategy is deployed to agent, and the corresponding navigation path is output according to real-time observation value.The application solves the reliable navigation problem of agent when "topological uncertainty" and "travel time randomness" exist simultaneously in traffic network, and realizes the maximum random punctual arrival probability of agent.
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