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In this letter, inspired by the needs of the European H2020 Project PANTHEON 1 , 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 paper 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.

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In this work, we propose a route planning strategy 1 for heterogeneous mobile robots in Precision Agriculture (PA) 2 settings. Given a set of agricultural tasks to be performed 3 at specific locations, we formulate a multi-Steiner Traveling 4 Salesman Problem (TSP) to define the optimal assignment of 5 these tasks to the robots as well as the respective optimal paths 6 to be followed. The optimality criterion aims to minimize the total 7 time required to execute all the tasks, as well as the cumulative 8 execution times of the robots. Costs for travelling from one 9 location to another, for maneuvering and for executing the task 10 as well as limited energy capacity of the robots are considered. 11 In addition, we propose a sub-optimal formulation to mitigate the 12 computational complexity by leveraging the fact that generally 13 in PA settings only a few locations require agricultural tasks in a 14 certain period of interest compared to all possible locations in the 15 field. A formal analysis of the optimality gap between the optimal 16 and the sub-optimal formulations is provided. The effectiveness 17 of the approach is validated in a simulated orchard where three 18 heterogeneous aerial vehicles perform inspection tasks. 19 Note to Practitioners-This paper aims at providing an efficient 20 solution to PA needs by deploying a team of robots able 21 to perform agricultural tasks at given locations in large-scale 22 orchards. In particular, a novel general optimization problem is 23 proposed that, given a set of mobile and possibly heterogeneous 24 robots and a set of agricultural tasks to carry out, defines the 25 assignment of these tasks to the robots as well as the routes to 26 follow, while minimizing the total and the cumulative execution 27 times of the robots. Existing approaches for route optimization 28 in PA generally involves complete coverage of the field by one 29 or multiple robots and do not account for maneuvering costs 30 with general layouts of the field. We consider costs for travelling 31 from one location to another, for executing the task and for 32 maneuvering without any restriction on the layout of the plants 33 as well as we take into account the limited energy capacity of the 34 robots. We also provide a sub-optimal formulation which reduces 35 the computational burden by relaxing the optimization of the 36 maneuvering costs at the locations where agricultural tasks are 37 Manuscript