A Study of Modified Infotaxis Algorithms in 2D and 3D Turbulent Environments

Abstract

Emergency response to hazardous gases in the environment is an important research field in environmental monitoring. In recent years, with the rapid development of sensor technology and mobile device technology, more autonomous search algorithms for hazardous gas emission sources are proposed in uncertain environment, which can avoid emergency personnel from contacting hazardous gas in a short distance. Infotaxis is an autonomous search strategy without a concentration gradient, which uses scattered sensor data to track the location of the release source in turbulent environment. This paper optimizes the imbalance of exploitation and exploration in the reward function of Infotaxis algorithm and proposes a mobile strategy for the three-dimensional scene. In two-dimensional and three-dimensional scenes, the average steps of search tasks are used as the evaluation criteria to analyze the information trend algorithm combined with different reward functions and mobile strategies. The results show that the balance between the exploitation item and exploration item of the reward function proposed in this paper is better than that of the reward function in the Infotaxis algorithm, no matter in the two-dimensional scenes or in the three-dimensional scenes.

Evaluation of Selected Algorithms for Air Pollution Source Localisation Using Drones

“…Tests were performed for various cases of contamination field distribution. All algorithms were tested for the gas diffusion model given by the following equation [22] (Equation ( 1)):…”

“…That time also grew rapidly due to the fact that it was necessary to perform operations on matrices (map) with increasing dimensions of: 31 × 31, 61 × 61, 101 × 101, and 201 × 201, respectively. Moreover, these cited examples, e.g., [22], were based on the impractical stop condition of the Infotaxis algorithm, whereby when the distance between the position of the search agent and the source of contamination decreases below a predetermined threshold, the robot is considered to have found the source of contamination. This is a condition that makes the practical implementation of the algorithm pointless, because since the location of the source is known a priori, there is no need to search for it.…”

“…The direct operation of the algorithm will not be discussed here; only the implementation details will be presented, as this algorithm was used to perform a comparison with simple solutions. The implementation in the Python [31] language available, and in particular the article [22], was used to implement Infotaxis in the MATLAB environment. Then, the operation of the following variants for the 2D space was tested: Infotaxis 4, Infotaxis 8, Infotaxis II 4, and Infotaxis II 8.…”

“…As the next position, the agent chooses the one that will result in the maximum information entropy drop. Research on this approach is still ongoing, such as the work [22]. The work mainly involved the study of different kinds of reward functions-Infotaxis, Infotaxis II, and Sinfotaxis variants-and the varying number of allowable directions the agent may travel for each step.…”