Rapid location technology of odor sources by multi‐UAV

Abstract

A multi-unmanned aerial vehicle (UAV) offers the advantages of flexibility, safety, and efficiency with respect to odor source localization. When the traditional particle swarm optimization (PSO) algorithm is used to locate the odor source, the search efficiency of the UAVs is reduced owing to unplanned search routes, and local extreme points and internal collisions may occur, making it impossible to quickly locate the odor source. In response to these problems, this paper proposes an improved particle swarm optimization (IPSO) algorithm, which combines the PSO with the Zigzag algorithm to preplan the search routes and improve the efficiency of the UAVs to search the plume. In addition, the distance between the UAVs is used as an adjustment factor to adaptively adjust the inertia weights of the particles to prevent the UAVs from falling into a local optimum during the plume-tracking stage and further improve the positioning efficiency. Finally, the PSO combined with the artificial potential field algorithm is used to avoid the internal collision of the UAVs when tracking the plume. The algorithm is verified through simulations and physical experiments. The results show that the IPSO significantly improves the efficiency and can achieve rapid and effective location of the odor source, which verifies the effectiveness and applicability of the algorithm.

Artificial Olfactory Signal Modulation for Detection in Changing Environments

“…It has been shown that mammals use spatial-temporal olfactory information to navigate and locate sources . As a result, there is considerable interest in providing similar capabilities to robots and UAVs to enable these sorts of tasks in autonomous and stand-off monitoring systems. To explore the feasibility of using temporal modulated responses for plume tracking, a concentration profile (Figure a) was simulated to represent temporal variations that could be experienced by an organism or an autonomous tracking system maneuvering up a concentration gradient to a source, periodically exiting the plume and returning based on a drop in concentration .…”

Optimization-Based Trajectory Tracking Approach for Multi-Rotor Aerial Vehicles in Unknown Environments

“…The trajectory planning problems in the plan-based control paradigm, in general, are solved adhering to these steps: path search (Wang et al, 2023), initial trajectory generation and trajectory refinement (Liu et al, 2022), high-level control command generation that can be achieved by several approaches, including differential flatness mapping (Talke et al, 2022), receding horizon planning, and finally low-level control commands generation using a flight controller, for example, PX4, DJI. These flight controllers operate independently irrespective of high-level planners.…”

Odor source localization of multi-robots with swarm intelligence algorithms: A review

“…The rapid development of UAV technology has provided an excellent platform, which has led to the 3D spatial OSL problem based on the UAV platform receiving the attention of most researchers (Jing et al, 2021). Preliminary research results have been achieved (Neumann et al, 2013;Pobkrut et al, 2016;Villa et al, 2016;Luo et al, 2018;Gunawardena et al, 2021;Liu et al, 2022). However, compared with OSL in two dimensions, these results are still far from adequate and need further exploration.…”