Multistrategy-Based Environment Map Building Using a Mobile Agent

This letter proposes a mobile agent-based environment map building method, which consists of three stages: 1) boundary detection, 2) global scan, and 3) map construction. According to the agent perception model, an improved wall-walking strategy is used to detect the boundary of the unknown environment. The region surrounded by the boundary is divided into meshes, and a point in each mesh is randomly chosen using a simplified internal spiral traversal strategy and used as the target point that the agent needs to reach in order. Based on this, the D
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Lite algorithm is used to control the movement of the agent to reduce the time for a global scan. Finally, the obstacle distribution and the concentration distribution of the pollution source in the unknown environment are generated through an occupancy probability strategy and a radial basis function neural network, respectively. Experimental results show that the proposed method can control the agent to detect the whole unknown environment with improved robustness and computational efficiency so as to construct the environment map completely and accurately.