Low-cost mini-drones with advanced sensing and maneuverability enable a new class of intelligent sensing systems. This trend motivated several research efforts to employ drones as standalone systems or to assist legacy deployments. However, several fundamental challenges remain unsolved including: 1) Adequate coverage of sizable targets; 2) Target orientation that render coverage effective only from certain view points; 3) Occlusion by elements in the environment, including other targets. In this paper, we present Argus, a system that provides coverage of wide and oriented targets, using camera-mounted drones, taking into account the challenges stated above. Argus relies on a geometric model that captures both target shapes and coverage constraints. With drones being the scarcest resource in Argus, we study the problem of minimizing the number of drones required to cover a set of such targets and derive a best-possible approximation algorithm. Furthermore, we present a sampling heuristic that yields a comparable performance, yet is up to 100x faster than the approximation algorithm. We build a prototype of Argus to demonstrate and evaluate the proposed coverage algorithms as part of a complete autonomous surveillance system. We evaluate the proposed algorithms using simulations to compare their performance at scale under various conditions. Finally, we present extensions and discuss open problems related to the studied problem.
Recent advancements in manufacturing low-cost wireless battery operated cameras has made their application in Wireless Video Sensor Networks (WVSN) increasingly more feasible and affordable. The application of robotic sensing agents equipped with cameras in WVSNs, seems particularly promising in performing coverage tasks for ad hoc surveillance. Their application in this context can be specifically useful for surveying areas with little to no available or affordable infrastructure, or where quick deployment is necessary. In this paper, we address the target coverage problem for finding the minimum number of cameras, their placement, and orientation to cover arbitrarily located targets in an area of interest. We propose a computationally light-weight heuristic, where the number of used mobile cameras is close to those found by near-optimal algorithms. Specifically, we address this problem for non-uniform target distributions that naturally form clusters. Having light-weight heuristics will be particularly useful when the application is required to adapt to target mobility and or is implemented in embedded systems. Our simulation study shows that when clusters are sufficiently separated, the required number of cameras found by our proposed method is very close to those acquired by the near-optimal algorithm, whereas the computational complexity of our algorithm is about ten times less. We also deploy our algorithm on a drone testbed using off-the-shelf components to verify its feasibility. The deployment of multiple robots for achieving a common goal helps to improve the performance, efficiency, and or robustness in a variety of tasks. In particular, the observation of moving targets is an important multirobot application that still exhibits numerous open challenges, including the effective coordination of the robots. This paper reviews control techniques for cooperative mobile robots monitoring multiple targets. The simultaneous movement of robots and targets makes this problem particularly interesting, and our review systematically addresses this cooperative multirobot problem for the first time. We classify and critically discuss the control techniques: cooperative multirobot observation of multiple moving targets, cooperative search, acquisition, and track, cooperative tracking, and multirobot pursuit evasion. We also identify the five major elements that characterize this problem, namely, the coordination method, the environment, the target, the robot and its sensor(s). These elements are used to systematically analyze the control techniques. The majority of the studied work is based on simulation and laboratory studies, which may not accurately reflect real-world operational conditions. Importantly, while our systematic analysis is focused on multitarget observation, our proposed classification is useful also for related multirobot applications. Drones, as known as unmanned aerial vehicles (UAV), are aircrafts which can perform autonomous pilot. They can easily reach locations which are too difficult to reach or dangerous for human beings and collect images from bird's-eye view through aerial photography. Enabling drones to identify people on the ground is important for a variety of applications, such as surveillance, people search, and remote monitoring. Since faces are part of inherent identities of people, how well face recognition technologies can be used by drones becomes essential for future development of the above applications. In this paper, we conduct empirical studies to evaluate several factors that may influence the performance of face detection and recognition techniques on drones. Our findings show that the current face recognition technologies are capable of recognizing faces on drones with some limits in distance and angle, especially when drones take pictures in high altitudes and the face image is taken from a long distance and with a large angle of depression. We also find that augmenting face models with 3D information may help to boost recognition performance in the case of large angles of depression. The use of miniature Unmanned Aerial Vehicles (UAVs), e.g. quadrocopters, has gained great popularity over the last years. Some complex application scenarios for micro UAVs call for the formation of swarms of multiple drones. In this paper a platform for the creation of such swarms is presented. It consists of commercial quadrocopters enhanced with on-board processing and communication units enabling autonomy of individual drones. Furthermore, a generic ground control station has been realized. Different co-operation strategies for teams of UAVs are currently evaluated with an agent based simulation tool. Finally, complex application scenarios for multiple micro UAVs are presented. Wireless sensor and actuator networks have been extensively deployed for enhancing industrial control processes and supply-chains, and many forms of surveillance and environmental monitoring. The availability of low-cost mobile robots equipped with a variety of sensors in addition to communication and computational capabilities makes them particularly promising in target coverage tasks for ad hoc surveillance, where quick, low-cost or non-lasting visual sensing solutions are required, e.g. in border protection and disaster recovery. In this paper, we consider the problem of low complexity placement and orientation of mobile cameras to cover arbitrary targets. We tackle this problem by clustering proximal targets, while calculating estimating the camera location direction for each cluster separately through our cover-set coverage method. Our proposed solutions provide extremely computationally efficient heuristics with only a small increase in number of cameras used, and a small decrease in number of covered targets. We study the problem of tracking mobile targets using a team of aerial robots. Each robot carries a camera to detect targets moving on the ground. The overall goal is to plan for the trajectories of the robots in order to track the most number of targets, and accurately estimate the target locations using the images. The two objectives can conflict since a robot may fly to a higher altitude and potentially cover a larger number of targets at the expense of accuracy. We start by showing that k ≥ 3 robots may not be able to track all n targets while maintaining a constant factor approximation of the optimal quality of tracking at all times. Next, we study the problem of choosing robot trajectories to maximize either the number of targets tracked or the quality of tracking. We formulate this problem as the weighted version of a combinatorial optimization problem known as the Maximum Group Coverage (MGC) problem. A greedy algorithm yields a 1 2 approximation for the weighted MGC problem. Finally, we evaluate the algorithm and the sensing model through simulations and preliminary experiments. We present a system to capture video footage of human subjects in the real world. Our system leverages a quadrotor camera to automatically capture well-composed video of two subjects. Subjects are tracked in a large-scale outdoor environment using RTK GPS and IMU sensors. Then, given the tracked state of our subjects, our system automatically computes static shots based on well-established visual composition principles and canonical shots from cinematography literature. To transition between these static shots, we calculate feasible, safe, and visually pleasing transitions using a novel real-time trajectory planning algorithm. We evaluate the performance of our tracking system, and experimentally show that RTK GPS significantly outperforms conventional GPS in capturing a variety of canonical shots. Lastly, we demonstrate our system guiding a consumer quadrotor camera autonomously capturing footage of two subjects in a variety of use cases. This is the first end-to-end system that enables people to leverage the mobility of quadrotors, as well as the knowledge of expert filmmakers, to autonomously capture high-quality footage of people in the real world. 