We consider the problem of online path planning for joint detection and tracking of multiple unknown radio-tagged objects. This is a necessary task for gathering spatio-temporal information using UAVs with on-board sensors in a range of monitoring applications. In this paper, we propose an online path planning algorithm with joint detection and tracking because signal measurements from these objects are inherently noisy. We derive a partially observable Markov decision process with a random finite set track-before-detect (TBD) multi-object filter, which also maintains a safe distance between the UAV and the objects of interest using a void probability constraint. We show that, in practice, the multi-object likelihood function of raw signals received by the UAV in the time-frequency domain is separable and results in a numerically efficient multi-object TBD filter. We derive a TBD filter with a jump Markov system to accommodate maneuvering objects capable of switching between different dynamic modes. Our evaluations demonstrate the capability of the proposed approach to handle multiple radio-tagged objects subject to birth, death, and motion modes. Moreover, this online planning method with the TBD-based filter outperforms its detection-based counterparts in detection and tracking, especially in low signal-to-noise ratio environments.
This paper gives a novel formula (Copenhagen Limit) to calculate/estimate the dynamic state error of a system without a feedforward signal. Copenhagen Limit is in the form of a limit and finds the dynamic error in an analytical solution. It can be used to design the controller in a tracking control problem. A numerical example is displayed to illustrate the accuracy of the Copenhagen Limit. Besides, the controller of a UAV (quadrotor) is designed using the Copenhagen Limit in a trajectory-tracking problem. The result of it is also demonstrated and analyzed.
National Yang Ming Chiao Tung University（台湾阳明交通大学）
A tracking controller for unmanned aerial vehicles (UAVs) is developed to track moving targets undergoing unknown translational and rotational motions. The main challenges are to control both the relative positions and angles between the target and the UAVs to within desired values, and to guarantee that the generated control inputs to the UAVs are feasible (i.e., within their motion capabilities). Moreover, the UAVs are controlled to ensure that the target always remains within the fields of view of their onboard cameras. To the best of our knowledge, this is the first work to apply multiple …
This paper proposes an adaptive dynamic programming-based adaptive-gain sliding mode control (ADP-ASMC) scheme for a fixed-wing unmanned aerial vehicle (UAV) with matched and unmatched disturbances. Starting from the dynamic of fixed-wing UAV, the control-oriented model composed of attitude subsystem and airspeed subsystem is established. According to the different issues in two subsystems, two novel adaptive-gain generalized super-twisting (AGST) algorithms are developed to eliminate the effects of disturbances in two subsystems and make the system trajectories tend to the designed integral s…
Beam Tracking for UAV Mounted SatCom on-the-Move with Massive Antenna Array
Tsinghua National Laboratory for Information Science and Technology（清华大学信息科学技术国家重点实验室）；High-Tech Institute of Xi¡¯an（西安高技术研究所）；Department of Electronics and Information Engineering, Nanjing University of Aeronautics and Astronautics（南京航空航天大学电子与信息工程学院）；National Communications Research Laboratory, Southeast University（东南大学国家通信工程研究中心）；College of Photonic and Electronic Engineering, Fujian Normal University（福建师范大学光电与信息工程学院）
Trajectory Tracking for UAVs: An Interpolating Control Approach
New Technologies for the Information Society Research Center（信息技术新技术研究中心）；Department of Cybernetics（控制论系）；Faculty of Applied Sciences（应用科学学院）；University of West Bohemia（西波希米亚大学）
IIIT-Delhi（印度德里信息技术学院）；Indian Institute of Science Education and Research, Bhopal（印度博帕尔科学教育与研究学院）
Persistent target tracking in urban environments using UAV is a difficult task due to the limited field of view, visibility obstruction from obstacles and uncertain target motion. The vehicle needs to plan intelligently in 3D such that the target visibility is maximized. In this paper, we introduce Target Following DQN (TF-DQN), a deep reinforcement learning technique based on Deep Q-Networks with a curriculum training framework for the UAV to persistently track the target in the presence of obstacles and target motion uncertainty. The algorithm is evaluated through several simulation experime…
Over the last few years, the many uses of Unmanned Aerial Vehicles (UAVs) have captured the interest of both the scientific and the industrial communities. A typical scenario consists in the use of UAVs for surveillance or target-search missions over a wide geographical area. In this case, it is fundamental for the command center to accurately estimate and track the trajectories of the UAVs by exploiting their periodic state reports. In this work, we design an ad hoc tracking system that exploits the Long Range Wide Area Network (LoRaWAN) standard for communication and an extended version of t…
Unmanned aerial vehicle (UAV) swarm control has applications including target tracking, surveillance, terrain mapping, and precision agriculture. Decentralized control methods are particularly useful when the swarm is large, as centralized methods (a single command center controlling the UAVs) suffer from exponential computational complexity, i.e., the computing time to obtain the optimal control for the UAVs grow exponentially with the number of UAVs in the swarm in centralized approaches. Although many centralized control methods exist, literature lacks decentralized control frameworks with …
The demand for accurate and fast trajectory tracking for multirotor Unmanned Aerial Vehicles (UAVs) have grown recently due to advances in UAV avionics technology and application domains. In many applications, the multirotor UAV is required to accurately perform aggressive maneuvers in challenging scenarios like the presence of external wind disturbances or in-flight payload changes. In this paper, we propose a systematic controller tuning approach based on identification results obtained by a recently developed Deep Neural Networks with the Modified Relay Feedback Test (DNN-MRFT) algorithm. W…
A leader-follower system is developed for cooperative transportation. To the best of our knowledge, this is the first work that inter-UAV communication is not required and the reference trajectory of the payload can be modified in real time, so that it can be applied to a dynamically changing environment. To track the modified reference trajectory in real time under the communication-free condition, the leader-follower system is considered as a nonholonomic system in which a controller is developed for the leader to achieve asymptotic tracking of the payload. To eliminate the need to install f…
An innovative method of detecting Unmanned Aerial Vehicles (UAVs) is presented. The goal of this study is to develop a robust setup for an autonomous multi-rotor hunter UAV, capable of visually detecting and tracking the intruder UAVs for real-time motion planning. The system consists of two parts: object detection using a stereo camera to generate 3D point cloud data and video tracking applying a Kalman filter for UAV motion modeling. After detection, the hunter can aim and shoot a tethered net at the intruder to neutralize it. The computer vision, motion tracking, and planning algorithms can…
In recent years, teams of robot and Unmanned Aerial Vehicles (UAVs) have been commissioned by researchers to enable accurate, online wildfire coverage and tracking. While the majority of prior work focuses on the coordination and control of such multi-robot systems, to date, these UAV teams have not been given the ability to reason about a fire's track (i.e., location and propagation dynamics) to provide performance guarantee over a time horizon. Motivated by the problem of aerial wildfire monitoring, we propose a predictive framework which enables cooperation in multi-UAV teams towards collab…
We study a trajectory tracking problem for a multi-rotor in the presence of modeling error and external disturbances. The desired trajectory is unknown and generated from a reference system with unknown or partially known dynamics. We assume that only position and orientation measurements for the multi-rotor and position measurements for the reference system can be accessed. We adopt an extended high-gain observer (EHGO) estimation framework to estimate the feed-forward term required for trajectory tracking, the multi-rotor states, as well as modeling error and external disturbances. We design…
Guidance Control and Decision Systems Laboratory Department of Aerospace Engineering Indian institute of Science（印度科学研究所航空航天工程系制导控制与决策系统实验室）；Robert Bosch Center for Cyber Physical Systems Indian Institute of Science（印度科学研究所罗伯特·博世网络物理系统中心）
This work details the problem of aerial target capture using multiple UAVs. This problem is motivated from the challenge 1 of Mohammed Bin Zayed International Robotic Challenge 2020. The UAVs utilise visual feedback to autonomously detect target, approach it and capture without disturbing the vehicle which carries the target. Multi-UAV collaboration improves the efficiency of the system and increases the chance of capturing the ball robustly in short span of time. In this paper, the proposed architecture is validated through simulation in ROS-Gazebo environment and is further implemented on ha…
Collaborative target-tracking control using multiple autonomous fixed-wing UAVs with constant speeds
Department of Electrical Engineering, Eindhoven University of Technology（埃因霍温理工大学电子工程系）；Universidad Complutense de Madrid（马德里complutense大学）；Research School of Electrical, Energy and Material Engineering, Australian National University（澳大利亚国立大学电气、能源和材料工程研究学校）；Optus-Curtin Centre of Excellence in Artificial Intelligence, Curtin University（Curtin大学人工智能卓越中心）
