Abstract:The capability to monitor water status from crops on a regular basis can enhance productivity and water use efficiency. In this paper, high-resolution thermal imagery acquired by an unmanned aerial vehicle (UAV) was used to map plant water stress and its spatial variability, including sectors under full irrigation and deficit irrigation over nectarine and peach orchards at 6.12 cm ground sample distance. The study site was classified into sub-regions based on crop properties, such as cultivars and tree training systems. In order to enhance the accuracy of the mapping, edge extraction and filtering were conducted prior to the probability modelling employed to obtain crop-property-specific ('adaptive' hereafter) lower and higher temperature references (T wet and T dry respectively). Direct measurements of stem water potential (SWP, ψ stem ) and stomatal conductance (g s ) were collected concurrently with UAV remote sensing and used to validate the thermal index as crop biophysical parameters. The adaptive crop water stress index (CWSI) presented a better agreement with both ψ stem and g s with determination coefficients (R 2 ) of 0.72 and 0.82, respectively, while the conventional CWSI applied by a single set of hot and cold references resulted in biased estimates with R 2 of 0.27 and 0.34, respectively. Using a small number of ground-based measurements of SWP, CWSI was converted to a high-resolution SWP map to visualize spatial distribution of the water status at field scale. The results have important implications for the optimal management of irrigation for crops.

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In this paper, we present an autonomous exploration method for unmanned aerial vehicles in unknown urban environment. We address two major aspects of exploration-map building and obstacle avoidance-by combining model predictive control (MPC) with a local obstacle map builder. An onboard laser scanner is used to build the online map of obstacles around the vehicle during the flight. A real-time MPC algorithm with a cost function that penalizes the distance to the nearest obstacle replans the path. The adjusted trajectory is sent to the position tracking layer in the Berkeley UAV avionics. The proposed approach is implemented on Berkeley rotorcraft UAVs and successfully tested in urban flight experiment setup.

Given a number of patrollers that are required to detect an intruder in a channel, the channel patrol problem consists of determining the periodic trajectories that the patrollers must trace out so as to maximized the probability of detection of the intruder. We formulate this problem as an optimal control problem. We assume that the patrollers' sensors are imperfect and that their motions are subject to turn-rate constraints, and that the intruder travels straight down a channel with constant speed.Using discretization of time and space, we approximate the optimal control problem with a large-scale nonlinear programming problem which we solve to obtain an approximately stationary solution and a corresponding optimized trajectory for each patroller. In numerical tests for one, two, and three underwater patrollers, an underwater intruder, different trajectory constraints, several intruder speeds and other specific parameter choices, we obtain new insightnot easily obtained using simply geometric calculations -into efficient patrol trajectory design under certain conditions for multiple patrollers in a narrow channel where interaction between the patrollers is unavoidable due to their limited turn rate.

R ecently, there has been a great interest in the development of advanced unmanned aerial vehicles (UAVs) capable of missions in complex dynamic environments. Conventional waypointbased navigation systems are typically unable to sense and avoid obstacles, and, therefore, they are not suitable for missions in cluttered urban environments. Advanced flight control systems for urban navigation should be able to adjust the flight path dynamically with the information on the surroundings collected using onboard sensors in real time.Autonomous exploration in an unknown environment requires a map generation on the surroundings and path planning for collision-free navigation. These topics have been intensively covered by the robotics community since the late 1970s. Based on these efforts, various algorithms and implementations are currently available for guiding mobile robots in an unknown or partially known twodimensional (2-D) world. In the 1990s, researchers introduced probability theories into map building techniques [1], enhancing robustness and performance of those algorithms even with less costly and inaccurate sensors [2]. Although some of these algorithms can be extended to three-dimensional (3-D) problems, their computational load is prohibitively large and/or they do not scale up well to problems in 3-D space. From a practical perspective, UAVs, due to their faster speed, tend to pose more challenges than the ground robots or unmanned underwater vehicles (UUVs) [7] in terms of speed and accuracy in obstacle sensing and evasion. In addition to payload limitation, during the development stage, typical trial-and-error approaches are not usually possible for UAVs because failures to avoid obstacles can lead to costly and dangerous outcomes.Model predictive control (MPC) has been found effective to solve control problems on dynamic systems with input and state constraints [9], [10] in an explicit manner. The online optimization [3] over a finite horizon allows for a control system more perceptive to future variations in the system dynamics and operating environment. In [10], it is proposed to solve for a plausible trajectory using mixedinteger linear programming (MILP) with constraints such as obstacle avoidance. In [8], obstacle-free trajectories in urban environments are computed using a nonlinear MPC technique. However, their algorithm requires a priori information about environments, and the collision-free air space is explicitly represented by convex cones between known urban structures. Alternatively, in [4], it is shown that the MPC algorithm can be formulated to solve the stabilization and tracking problem of a nonlinear kinodynamic equation with control input saturation, state constraints, and some behavioral constraints such as collision avoidance and pursuit-evasion games. In this article, for numerical tractability and improved reliability, we propose a hierarchical flight control system that enables conflict-free navigation by tracking the trajectories generated by the real-time MPC optimization module...