Abstract
In this paper a biologically inspired neural dynamics and map planning based approach are simultaneously proposed for AUV (Autonomous Underwater Vehicle) path planning and obstacle avoidance in an unknown dynamic environment. Firstly the readings of an ultrasonic sensor are fused into the map using the D-S (Dempster-Shafer) inference rule and a two-dimensional occupancy grid map is built. Secondly the dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation. The AUV path is autonomously generated from the dynamic activity landscape of the neural network and previous AUV location. Finally, simulation results show high quality path optimization and obstacle avoidance behaviour for the AUV.
Highlights
The basic feature of an autonomous mobile robot is its capability to operate independently in unknown or partially known environments
In order to clearly show the effectiveness of the D-S evidence theory, in this paper proposed an approach to calculate the accuracy of the constructed map
Due to the lateral excitatory connections among neurons, the positive neural activity from the target in the neural network will propagate toward the current AUV location through neural activity propagation and the neural activity is constantly changed
Summary
The basic feature of an autonomous mobile robot is its capability to operate independently in unknown or partially known environments. The autonomy implies that the robot is capable of reacting to static obstacles and unpredictable dynamic events that may impede the successful execution of a task [1] To achieve this goal, solutions need to be developed in map building, path planning and navigation. There are some learning based models for motion planning of mobile robots for unknown environments. For example Yang and Luo [21,22] proposed a neural network model for complete coverage path planning in non-stationary environments. Map building and path planning algorithms are proposed for an AUV in an unknown environment.
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