Reactive-probabilistic hybrid search method for odour source localization in an obstructed environment

Abstract

In odour source localization, the probabilistic inference method effectively manages to find odour plumes, both at the start and during the middle of the search. However, the reactive method is more adept at tracking the odour plumes. Combining two search methods could yield a comprehensive algorithm for odour source detection, particularly effective in obstructed environments where the concurrent tasks of finding and tracking the source are performed interchangeably. In this study, we aim to achieve a balance between the exploration of the probabilistic method and the exploitation of the reactive method by alternating between the two. A switching coefficient is calculated based on burstiness, which measures the periodic characteristics of gas detection. This coefficient determines whether the search agent is within the primary plume flow or outside of it. Based on the determination, the agent decides its next course of action using the probabilistic inference method or the reactive method. We verify and evaluate the proposed method with an autonomous mobile robot in an obstructed indoor environment. Having achieved nearly 10% in distance travelled reduction and more than a 30% decrease in average search time compared to a previously studied Infotaxis–Dijkstra switching algorithm, we can confidently assert the efficiency of the proposed method.

Keywords

• odor source localization
• probabilistic robotics
• bio-inspired system
• system integration
• path planning