Incorporating Animal Klinotaxis Behavior and Bilateral Concentration Information: A 3-D Bio-Inspired Odor Source Localization Algorithm and Its Performance Evaluation

Odor source localization (OSL) robots play a vital role in detecting hazardous gas leaks, showing significant practical value and potential applications. In this study, we designed a 3-D bio-inspired OSL algorithm that employs a pair of bilateral sensors and uses relatively few computational resources, requiring only its previously sampled concentration data and wind information. Initially, the robot’s spatial swiveling sensor model is constructed. Following this, an animal klinotaxis behavior-inspired algorithm, which incorporates the fusion of bilateral odor information and wind information (B-AKBI algorithm), is employed during the plume-tracking phase. In the plume-finding phase, a cross-wind plane Lévy walk strategy is utilized. An adaptive strategy is developed, allowing the robot to modify step length based on detected concentration, preventing missed detection and maintaining efficiency. We investigate the impact of incorporating bilateral concentration information into the algorithm, and the results indicate a 10.8% to 14.1% increase in success rate. The robustness related to initial orientations is also studied, and the results indicate that for the six initial postures, the algorithm exhibits strong robustness with success rates ranging from 79.0% to 81%. The algorithm’s performance is assessed in three scenarios and compared with a surge-spiral algorithm when initially positioned behind the odor source. The results show the proposed algorithm achieves a higher success rate while the average search distance during the plume-tracking phase is 41.6% and 30.67% of that of the comparison methods, indicating the efficiency of the B-AKBI algorithm.