A Study of Institutional Factors Affecting Water Resource Development in the Lower Rio Grande Valley, Texas

Despite numerous studies of and plans for the use of land and water resources of the lower Rio Grande Valley for efficient agricultural production, development has lagged and the production potential has not been realized. Institutional factors--political, legal, economic and cultural--have often been obstacles to the construction of needed water facilities and good management of lands in irrigation. Change in some of these institutions and the introduction of new, more appropriate institutional arrangements can facilitate land and water development and use so that greater efficiency in productive operations is achieved. A very important legal institution is the water right, yet there has existed considerable confusion about rights in the Valley Water rights need to be clarified as to origin, extent and legality. Certainty in this right is necessary to optimum levels of development of irrigation. This can be accomplished by completion of court action which has proceeded through this decade. To achieve efficiency in water use, rights should be made negotiable. Some trading or leasing of rights is practiced now on an informal basis. A change or clarification of water law to permit purchase and sale of rights would facilitate exchange so that water would be used in higher value uses. To achieve better management of water in irrigation, it is recommended that rehabilitation of irrigation systems be continued on an accelerated basis. This would include reconstruction of many canals and ditches to include concrete linings, construction of storage areas off the river where feasible, and certainly installation of water meters at points of delivery to users. To provide for more orderly and efficient planning for and further development of irrigation systems, it is recommended that some consolidation of special districts be accomplished. It seems possible that a single master district might be a logical goal for the many irrigation districts. Drainage problems could be attacked by a single or small number of irrigation districts that would take on this responsibility, or one or more special drainage districts could be organized for this purpose. These and other recommendations are the product of this study

Deep reinforcement learning for drone navigation using sensor data

Mobile robots such as unmanned aerial vehicles (drones) can be used for surveillance, monitoring and data collection in buildings, infrastructure and environments. The importance of accurate and multifaceted monitoring is well known to identify problems early and prevent them escalating. This motivates the need for flexible, autonomous and powerful decision-making mobile robots. These systems need to be able to learn through fusing data from multiple sources. Until very recently, they have been task specific. In this paper, we describe a generic navigation algorithm that uses data from sensors on-board the drone to guide the drone to the site of the problem. In hazardous and safety-critical situations, locating problems accurately and rapidly is vital. We use the proximal policy optimisation deep reinforcement learning algorithm coupled with incremental curriculum learning and long short-term memory neural networks to implement our generic and adaptable navigation algorithm. We evaluate different configurations against a heuristic technique to demonstrate its accuracy and efficiency. Finally, we consider how safety of the drone could be assured by assessing how safely the drone would perform using our navigation algorithm in real-world scenarios

An unmanned aerial vehicle-based system for large scale metrology applications

International audienceDifferent manufacturing environments, such as aerospace, automotive, shipbuilding, and railway, reveal their interest in Large Scale Metrology (LSM) instruments, to provide a support in assembly, alignment, inspection, and robot tracking tasks. Notwithstanding different levels of portability granted by existing systems, the overall measurement procedure actually involves a direct interaction between the measuring equipment and the operator, as well as a strong dependence on human skills. This paper presents a novel metrological system, scaling down this interaction to a mission management task and aimed at uniforming system performance. The proposed architecture entrusts an unmanned aerial vehicle (UAV) with the task of carrying the sensor equipment and of moving the measuring probe. The design process of the proposed system is detailed and faced with a two-level approach. The first level is directed to identify the available start-up technology, its limitations and sensitivity to the design parameters, while the second level is focused on the experimental testing of a preliminary test-bed to investigate the overall system performance. An actual implementation of the proposed architecture is herein discussed, focusing on system feasibility and presenting some preliminary experimental results

On the Design and Use of a Micro Air Vehicle to Track and Avoid Adversaries

The MAV ’08 competition focused on the problem of using air and ground vehicles to locate and rescue hostages being held in a remote building. To execute this mission, a number of technical challenges were addressed, including designing the micro air vehicle (MAV), using the MAV to geo-locate ground targets, and planning the motion of ground vehicles to reach the hostage location without detection. In this paper, we describe the complete system designed for the MAV ’08 competition, and present our solutions to three technical challenges that were addressed within this system. First, we summarize the design of our micro air vehicle, focusing on the navigation and sensing payload. Second, we describe the vision and state estimation algorithms used to track ground features, including stationary obstacles and moving adversaries, from a sequence of images collected by the MAV. Third, we describe the planning algorithm used to generate motion plans for the ground vehicles to approach the hostage building undetected by adversaries; these adversaries are tracked by the MAV from the air. We examine different variants of a search algorithm and describe their performance under different conditions. Finally, we provide results of our system’s performance during the mission execution.United States. Army Research Office (MAST CTA)Singapore. Armed ForcesUnited States. Air Force Office of Scientific Research (contract # F9550-06-C-0088)Aurora Flight Sciences Corp.Boeing CompanyNational Energy Research Scientific Computing Center (U.S.)National Science Foundation (U.S.). Division of Information and Intelligent Systems (grant # 0546467)Massachusetts Institute of Technology. Air Vehicle Research Center (MAVRC