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In this article, we propose a distributed negotiation framework that allows a set of cooperative agents to find a common ground with their neighbors while attempting to modify their initial opinion by the least possible amount. Based on such a framework, we develop a distributed agreement approach where the effort spent in the local agreement reflects the relevance of the agents in a weighted consensus process. In particular, we assume that players whose ideas happen to satisfactory mediate the standpoint of their interlocutors will end-up being more influential in the overall decision-making process. We conclude the article by applying the proposed methodology in the context of distributed data aggregation scenarios, as a way to mitigate the effect of outliers (e.g., faulty sensors)., QC 20220916

In this work, we propose a global planning strategy specifically designed for precision agriculture settings, where field activities may have different requirements ranging from a full orchard inspection to sparse targeted per-plant interventions. This global planning strategy is formulated as a novel Multi-Platform Steiner Traveling Salesman Problem (MP-STSP) where, in order to guarantee the exploitation of multiple moving platforms and the minimization of the overall operational time, the proposed formulation explicitly takes into account the time required to perform each task. By doing so, the computed itineraries attempt to balance the workload among the deployed platforms. Comparative simulations, inspired by the needs of the EU H2020 Project PANTHEON, are provided to numerically demonstrate the effectiveness of the proposed planning strategy for an orchard precision agriculture setting., SCOPUS: cp.p, info:eu-repo/semantics/published

This article presents a novel information-based mission planner for a drone tasked to monitor a spatially distributed dynamical phenomenon. For the sake of simplicity, the area to be monitored is discretized. The insight behind the proposed approach is that, due to the spatiotemporal dependencies of the observed phenomenon, one does not need to collect data on the entire area, which is one of the main limiting factors in unmanned aerial vehicle (UAV) applications due to their limited autonomy. In fact, unmeasured states can be estimated using an estimator, such as a Kalman filter. In this context, the planning problem becomes the one of generating a flight plan that maximizes the quality of the state estimation while satisfying the flight constraints (e.g. flight time). The first result of this article is the formulation of this problem as a special orienteering problem where the cost function is a measure of the quality of the estimation. This results in a mixed-integer semidefinite formulation, which can be optimally solved for small instances of the problem. For larger instances, a heuristic is proposed, which provides suboptimal results. Simulations numerically demonstrate the capabilities and efficiency of the proposed path-planning strategy. We believe that this approach has the potential to increase dramatically the area that a drone can monitor, thus increasing the number of applications where monitoring with drones can become economically convenient. Note to Practitioners - This article was motivated by the problem of performing large-scale field monitoring activities using unmanned aerial vehicle (UAV), which at the moment is very time-consuming and limits the definitive adoption of UAVs for this kind of activities. This problem is caused by the limited autonomy of commercial UAVs and the lack of systematic ways to plan missions so as to maximize the amount of information collected. This work starts from the observation that, in many applications, the phen, SCOPUS: ar.j, info:eu-repo/semantics/published

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info:eu-repo/semantics/published

In this letter, inspired by the needs of the European H2020 Project PANTHEON,11[Online]. Available: https://www.project-pantheon.eu. we propose a full navigation stack purposely designed for the autonomous navigation of Ackermann steering vehicles in precision farming settings. The proposed stack is composed of a local planner and a pose regulation controller, both implemented in ROS. The local planner generates, in real-Time, optimal trajectories described by a sequence of successive poses. The planning problem is formulated as a real-Time cost-function minimization problem over a finite time horizon where the Ackermann kinematics and the presence of obstacles are encoded as constraints. The control law ensures the convergence toward each of these poses. To do so, in this letter we propose a novel non-smooth control law designed to ensure the solvability of the pose regulation problem for the Ackermann vehicle. Theoretical characterization of the convergence property of the proposed pose regulation controller is provided. Numerical simulations along with real-world experiments are provided to corroborate the effectiveness of the proposed navigation strategy., SCOPUS: ar.j, info:eu-repo/semantics/published

info:eu-repo/semantics/published