144 research outputs found

    Towards the development of a smart flying sensor: illustration in the field of precision agriculture

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    Sensing is an important element to quantify productivity, product quality and to make decisions. Applications, such as mapping, surveillance, exploration and precision agriculture, require a reliable platform for remote sensing. This paper presents the first steps towards the development of a smart flying sensor based on an unmanned aerial vehicle (UAV). The concept of smart remote sensing is illustrated and its performance tested for the task of mapping the volume of grain inside a trailer during forage harvesting. Novelty lies in: (1) the development of a position-estimation method with time delay compensation based on inertial measurement unit (IMU) sensors and image processing; (2) a method to build a 3D map using information obtained from a regular camera; and (3) the design and implementation of a path-following control algorithm using model predictive control (MPC). Experimental results on a lab-scale system validate the effectiveness of the proposed methodology

    Detection and estimation of moving obstacles for a UAV

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    In recent years, research interest in Unmanned Aerial Vehicles (UAVs) has been grown rapidly because of their potential use for a wide range of applications. In this paper, we proposed a vision-based detection and position/velocity estimation of moving obstacle for a UAV. The knowledge of a moving obstacle's state, i.e., position, velocity, is essential to achieve better performance for an intelligent UAV system specially in autonomous navigation and landing tasks. The novelties are: (1) the design and implementation of a localization method using sensor fusion methodology which fuses Inertial Measurement Unit (IMU) signals and Pozyx signals; (2) The development of detection and estimation of moving obstacles method based on on-board vision system. Experimental results validate the effectiveness of the proposed approach. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved

    The development of an autonomous navigation system with optimal control of an UAV in partly unknown indoor environment

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    This paper presents an autonomous methodology for a low-cost commercial AR.Drone 2.0 in partly unknown indoor flight using only on-board visual and internal sensing. Novelty lies in: (i) the development of a position estimation method using sensor fusion in a structured environment. This localization method presents how to get the UAV localization states (position and orientation), through a sensor fusion scheme, dealing with data provided by an optical sensor and an inertial measurement unit (IMU). Such a data fusion scheme takes also in to account the time delay present in the camera signal due to the communication protocols; (ii) improved potential field method which is capable of performing obstacle avoiding in an unknown environment and solving the non reachable goal problem; and (iii) the design and implementation of an optimal proportional - integral - derivative (PID) controller based on a novel multi-objective particle swarm optimization with an accelerated update methodology tracking such reference trajectories, thus characterizing a cascade controller. Experimental results validate the effectiveness of the proposed approach

    Autonomous wireless self-charging for multi-rotor unmanned aerial vehicles

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    Rotary-wing unmanned aerial vehicles (UAVs) have the ability to operate in confined spaces and to hover over point of interest, but they have limited flight time and endurance. Conventional contact-based charging system for UAVs has been used, but it requires high landing accuracy for proper docking. Instead of the conventional system, autonomous wireless battery charging system for UAVs in outdoor conditions is proposed in this paper. UAVs can be wirelessly charged using the proposed charging system, regardless of yaw angle between UAVs and wireless charging pad, which can further reduce their control complexity for autonomous landing. The increased overall mission time eventually relaxes the limitations on payload and flight time. In this paper, a cost effective automatic recharging solution for UAVs in outdoor environments is proposed using wireless power transfer (WPT). This research proposes a global positioning system (GPS) and vision-based closed-loop target detection and a tracking system for precise landing of quadcopters in outdoor environments. The system uses the onboard camera to detect the shape, color and position of the defined target in image frame. Based on the offset of the target from the center of the image frame, control commands are generated to track and maintain the center position. Commercially available AR.Drone. was used to demonstrate the proposed concept which is equppied with bottom camera and GPS. Experiments and analyses showed good performance, and about 75% average WPT efficiency was achieved in this research

    A survey on fractional order control techniques for unmanned aerial and ground vehicles

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    In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade

    Robust Intelligent Sensing and Control Multi Agent Analysis Platform for Research and Education

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    The aim of this thesis is the development and implementation of a controlled testing platform for the Robust Intelligent Sensing and Controls (RISC) Lab at Utah State University (USU). This will be an open source adaptable expandable robotics platform usable for both education and research. This differs from the many other platforms developed in that the entire platform software will be made open source. This open source software will encourage collaboration among other universities and enable researchers to essentially pick up where others have left off without the necessity of replicating months or even years of work. The expected results of this research will create a foundation for diverse robotics investigation at USU as well as enable attempts at novel methods of control, estimation and optimization. This will also contribute a complete software testbed setup to the already vibrant robotics open source research community. This thesis first outlines the platform setup and novel developments therein. The second stage provides an example of how this has been used in education, providing an example curriculum implementing modern control techniques. The third section provides some exploratory research in trajectory control and state estimation of the tip of an inverted pendulum atop a small unmanned aerial vehicle as well as bearing-only cooperative localization experimentation. Finally, a conclusion and future work is discussed

    Model predictive cooperative localization control of multiple UAVs using potential function sensor constraints: a workflow to create sensor constraint based potential functions for the control of cooperative localization scenarios with mobile robots.

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    The global localization of multiple mobile robots can be achieved cost efficiently by localizing one robot globally and the others in relation to it using local sensor data. However, the drawback of this cooperative localization is the requirement of continuous sensor information. Due to a limited sensor perception space, the tracking task to continuously maintain this sensor information is challenging. To address this problem, this contribution is presenting a model predictive control (MPC) approach for such cooperative localization scenarios. In particular, the present work shows a novel workflow to describe sensor limitations with the help of potential functions. In addition, a compact motion model for multi-rotor drones is introduced to achieve MPC real-time capability. The effectiveness of the presented approach is demonstrated in a numerical simulation, an experimental indoor scenario with two quadrotors as well as multiple indoor scenarios of a quadrotor obstacle evasion maneuver
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