Optimal strategy solution of mobile robot in dynamic environment

A mobile robot and optimal strategy technology, applied in data processing applications, forecasting, computing, etc., can solve problems that cannot be transformed into DRA, solutions that cannot be solved, etc., and achieve the effect of wide applicability and good optimal strategy
CN109409592AActive Publication Date: 2019-03-01ZHEJIANG UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
ZHEJIANG UNIV OF TECH
Publication Date
2019-03-01

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Abstract

The optimal strategy solution of mobile robot in dynamic environment includes the following steps: Firstly, according to the running environment of the robot, the improvement is constructed. Weightedswitching system, according to the task requirements, the task requirements are expressed mathematically by linear temporal logic (LTL), and the LTL task formulas are transformed into B u chi automataby LTL2BA toolkit. Then Cartesian product is used to get Product automata, which contains task requirement and environment information. The useless points on the feasible network topology graph are removed, and the availability of state points is further judged according to the double-label and behavior constraint criterion, thus the number of state points is simplified. The remaining points areconstructed as MDP model, and the optimal strategy is obtained by policy iteration. The invention not only solves the situation that there is no DRA, but also reduces the number of available points, reduces the complexity of the constructed MDP, and can obtain the optimal strategy more quickly.
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Description

technical field

[0001] The invention relates to an optimal strategy generation method for a mobile robot in a dynamic environment. Background technique

[0002] In recent years, with the development of science and technology, people's demand for intelligent robots in production and life is increasing, and the requirements for the level of intelligence of robots are also getting higher and higher. The application of intelligent robots must involve the movement of the robot, that is, the path planning of the robot. The current path planning methods such as genetic algorithm, particle swarm optimization algorithm, ant colony optimization algorithm, and simulated annealing algorithm are all planned according to the given robot operating environment. The optimal path in the static environment is obtained, and the search for the path is determined in a single step. For artificial neural network algorithms, heuristic search algorithms, sampling-based path planning algorithms, etc....

Claims

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