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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

Active Publication Date: 2019-03-01
ZHEJIANG UNIV OF TECH
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  • Claims
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AI Technical Summary

Problems solved by technology

However, there is a disadvantage of using DRA. In some cases, the LTL formula cannot be converted into DRA, which makes the traditional solution unable to solve all situations. On the other hand, the MDP obtained by the traditional method still has some redundant states. can be further reduced

Method used

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  • Optimal strategy solution of mobile robot in dynamic environment
  • Optimal strategy solution of mobile robot in dynamic environment
  • Optimal strategy solution of mobile robot in dynamic environment

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Embodiment Construction

[0029] The LTL-MDP solution of the present invention will be further described through simple examples below in conjunction with the accompanying drawings.

[0030] The flow chart of the invention is as follows figure 1 As shown, first, according to the operating environment of the robot figure 2 , constructing an improved-weighted switching system image 3 , according to the task requirements: after the pickup, the robot must reach the dropoff before returning to the pickup. Similarly, the robot must go through the pickup after the dropoff to return to the dropoff. Use linear temporal logic (LTL) to express the task requirements mathematically, and use the LTL2BA tool The package converts the LTL task formula into a Büchi automaton; then performs Cartesian product of the two to obtain the Product automaton, which contains task requirements and environmental information; removes useless points on the feasibility network topology map (some points only have input or Only the ...

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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.

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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Application Information

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IPC IPC(8): G06Q10/04
CPCG06Q10/047
Inventor 欧林林范振雍禹鑫燚陆文祥
Owner ZHEJIANG UNIV OF TECH
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