Hexapod robot obstacle avoidance method based on adaptive weight reinforcement learning

A hexapod robot, self-adaptive weight technology, applied in the direction of self-adaptive control, instrument, control/regulation system, etc., can solve the problem that the robot cannot walk.

Active Publication Date: 2020-08-18
NANJING UNIV
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Problems solved by technology

Using this gait can show good walking ability on a plane, but in irregular terrain, especially in an unknown environment, the ro

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  • Hexapod robot obstacle avoidance method based on adaptive weight reinforcement learning
  • Hexapod robot obstacle avoidance method based on adaptive weight reinforcement learning
  • Hexapod robot obstacle avoidance method based on adaptive weight reinforcement learning

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Embodiment 1

[0069] like figure 1 As shown, the hexapod robot obstacle avoidance method based on adaptive weight reinforcement learning disclosed by the present invention includes the following steps:

[0070] Step 1: The hexapod robot measures the distance between the robot and obstacles in the front, left and right directions through the ranging sensor, and converts the measured obstacle distance into a finite set of states through the fuzzy membership function;

[0071] Step 2. Establish a hexapod robot obstacle avoidance model according to the limited state set, and then use the adaptive weight reinforcement learning algorithm to learn the optimal network model parameters θ*;

[0072] Step 3: Obtain the optimal target strategy of the hexapod robot for obstacle avoidance according to the above-mentioned trained optimal network model parameters θ*, and obtain the action a to be taken by the hexapod robot for obstacle avoidance at time t from the optimal target strategy t .

[0073] Fur...

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Abstract

The invention discloses a hexapod robot obstacle avoidance method based on adaptive weight reinforcement learning. The method comprises the following steps: enabling a hexapod robot to measure the distance between the robot and each nearby obstacle through a distance measurement sensor, and converting the measured obstacle distance into a limited state set through a fuzzy membership function; establishing a hexapod robot obstacle avoidance model according to the limited state set, and learning an optimal network model parameter theta * by using an adaptive weight reinforcement learning algorithm; and obtaining an optimal target strategy for obstacle avoidance of the hexapod robot according to the trained optimal network model parameter theta *, and obtaining an action at which the hexapodrobot needs to avoid the obstacle at the moment t according to the optimal target strategy. The obstacle avoidance method can achieve a good obstacle avoidance effect in a position environment with alarge number of obstacles, and has a good market application prospect.

Description

technical field [0001] The invention relates to an obstacle avoidance method for a robot, in particular to an obstacle avoidance method for a hexapod robot based on self-adaptive weight reinforcement learning. Background technique [0002] The hexapod robot has multiple redundant degrees of freedom in structure, so it has a high adaptability to the terrain environment. Hexapod robots can walk in the field with complex road conditions, overcome obstacles, and complete transportation operations in non-structural environments that cannot be completed by wheeled or tracked types. They are used in forest logging, mining, underwater construction, nuclear industry, military transportation and It has a very broad application prospect in fields such as detection and planetary detection. Therefore, the research on hexapod robots has always attracted the attention of experts and scholars from various countries, but how to improve the mobility of hexapod robots in unstructured environm...

Claims

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Application Information

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IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 李华雄任其成陈春林王岚唐开强王子辉朱张青辛博
Owner NANJING UNIV
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