Alcherio Martinoli

Wanting Jin, Agatha Duranceau, İzzet Kağan Erünsal et al.

Efficient Gas Source Localization (GSL) in real-world settings is crucial, especially in emergency scenarios. Mobile robots equipped with low-cost, in-situ gas sensors offer a safer alternative to human inspection in hazardous environments. Probabilistic algorithms enhance GSL efficiency with scattered gas measurements by comparing gas concentration measurements gathered by robots to physical dispersion models. However, accurately deriving gas concentrations from data acquired with low-cost sensors is challenging due to the nonlinear sensor response, environmental dependencies (e.g., humidity, temperature, and other gas influences), and robot motion. Mitigating these disturbance factors requires frequent sensor calibration in controlled environments, which is often impractical for real-world deployments. To overcome these issues, we propose a novel feature extraction algorithm that leverages the relative ranking of gas measurements within the dynamically accumulated dataset. By comparing the rank differences between gathered and modeled values, we estimate the probabilistic distribution of source locations across the entire environment. We validate our approach in high-fidelity simulations and physical experiments, demonstrating consistent localization accuracy with uncalibrated gas sensors. Compared to existing methods, our technique eliminates the need for gas sensor calibration, making it well-suited for real-world applications.

I. Kagan Erunsal, Rodrigo Ventura, Alcherio Martinoli

The complex tasks such as surveillance, construction, search and rescue can benefit of the maneuverability of multirotor Micro Aerial Vehicles (MAVs) to obtain robust, cooperative system behavior and formation control is a prominent component of the these complex tasks. This work focuses on the problem of three-dimensional formation control of multirotor MAVs by using exclusively relative sensory information. It proposes a centralized Nonlinear Model Predictive Control (NMPC) approach in a leader-follower scheme. A realistic six degrees of freedom mathematical model of a multirotor MAVs is introduced and leveraged in the control laws. The formulation of the problem is performed based on NMPC and relative sensing framework with respect to local coordinate frames of the robots. This type of formulation makes the formation independent of the full knowledge of global or common reference frames and the utilization of expensive global localization sensors. Real-time Iteration (RTI) based solution to optimal control problem (OCP) is proposed by taking the novel formulation into account. An extensive scenario is designed to test and validate the strategy. Evaluation of the results suggests that satisfactory robust performance is achieved and maintained under model uncertainty and noise in local sensors and even in cases where the dynamics of the formation suddenly changes.