This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas.

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A Survey on Aerial Swarm Robotics

Unmanned vehicles (UVs) play a key role in autonomous surveillance scenarios. A major task needed by these UVs in undertaking autonomous patrol missions is to detect the targets and find their locations in real-time. In this paper, a new vision-based target detection and localization system is presented to make use of different capabilities of UVs as a cooperative team. The scenario considered in this paper is a team of an unmanned aerial vehicle (UAV) and multiple unmanned ground vehicles (UGVs) tracking and controlling crowds on a border area. A customized motion detection algorithm is applied to follow the crowd from the moving camera mounted on the UAV. Due to UAVs lower resolution and broader detection range, UGVs with higher resolution and fidelity are used as the individual human detectors, as well as moving landmarks to localize the detected crowds with unknown independently moving patterns at each time point. The UAVs localization algorithm, proposed in this paper, then converts the crowds’ image locations into their real-world positions, using perspective transformation. A rule-of-thumb localization method by a UGV is also presented, which estimates the geographic locations of the detected individuals. Moreover, an agent-based simulation model is developed for system verification, with different parameters, such as flight altitude, number of landmarks, and landmark assignment method. The performance measure considered in this paper is the average Euclidean distance between the estimated locations and simulated geographic waypoints of the crowd. Experimental results demonstrate the effectiveness of the proposed framework for autonomous surveillance by UVs.

Vision-Based Target Detection and Localization via a Team of Cooperative UAV and UGVs

We present a fast and precise vision-based software intended for multiple robot localization. The core component of the software is a novel and efficient algorithm for black and white pattern detection. The method is robust to variable lighting conditions, achieves sub-pixel precision and its computational complexity is independent of the processed image size. With off-the-shelf computational equipment and low-cost cameras, the core algorithm is able to process hundreds of images per second while tracking hundreds of objects with millimeter precision. In addition, we present the method's mathematical model, which allows to estimate the expected localization precision, area of coverage, and processing speed from the camera's intrinsic parameters and hardware's processing capacity. The correctness of the presented model and performance of the algorithm in real-world conditions is verified in several experiments. Apart from the method description, we also make its source code public at http://purl.org/robotics/whycon ; so, it can be used as an enabling technology for various mobile robotic problems.

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A Practical Multirobot Localization System

In this review we argue for the creation of a physics of moving systems -- a locomotion "robophysics" -- which we define as the pursuit of the discovery of principles of self generated motion. Robophysics can provide an important intellectual complement to the discipline of robotics, largely the domain of researchers from engineering and computer science. The essential idea is that we must complement study of complex robots in complex situations with systematic study of simplified robophysical devices in controlled laboratory settings and simplified theoretical models. We must thus use the methods of physics to examine successful and failed locomotion in simplified (abstracted) devices using parameter space exploration, systematic control, and techniques from dynamical systems. Using examples from our and other's research, we will discuss how such robophysical studies have begun to aid engineers in the creation of devices that begin to achieve life-like locomotor abilities on and within complex environments, have inspired interesting physics questions in low dimensional dynamical systems, geometric mechanics and soft matter physics, and have been useful to develop models for biological locomotion in complex terrain. The rapidly decreasing cost of constructing sophisticated robot models with easy access to significant computational power bodes well for scientists and engineers to engage in a discipline which can readily integrate experiment, theory and computation.

A review on locomotion robophysics: the study of movement at the intersection of robotics, soft matter and dynamical systems

A complex system for control of swarms of micro aerial vehicles (MAV), in literature also called as unmanned aerial vehicles (UAV) or unmanned aerial systems (UAS), stabilized via an onboard visual relative localization is described in this paper. The main purpose of this work is to verify the possibility of self-stabilization of multi-MAV groups without an external global positioning system. This approach enables the deployment of MAV swarms outside laboratory conditions, and it may be considered an enabling technique for utilizing fleets of MAVs in real-world scenarios. The proposed visual-based stabilization approach has been designed for numerous different multi-UAV robotic applications (leader-follower UAV formation stabilization, UAV swarm stabilization and deployment in surveillance scenarios, cooperative UAV sensory measurement) in this paper. Deployment of the system in real-world scenarios truthfully verifies its operational constraints, given by limited onboard sensing suites and processing capabilities. The performance of the presented approach (MAV control, motion planning, MAV stabilization, and trajectory planning) in multi-MAV applications has been validated by experimental results in indoor as well as in challenging outdoor environments (e.g., in windy conditions and in a former pit mine).

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System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

In this paper, we present a small, light-weight, low-cost, fast and reliable system designed to satisfy requirements of relative localization within a swarm of micro aerial vehicles. The core of the proposed solution is based on off-the-shelf components consisting of the Caspa camera module and Gumstix Overo board accompanied by a developed efficient image processing method for detecting black and white circular patterns. Although the idea of the roundel recognition is simple, the developed system exhibits reliable and fast estimation of the relative position of the pattern up to 30 fps using the full resolution of the Caspa camera. Thus, the system is suited to meet requirements for a vision based stabilization of the robotic swarm. The intent of this paper is to present the developed system as an enabling technology for various robotic tasks.

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Low-cost embedded system for relative localization in robotic swarms

The task of cooperative surveillance of pre-selected Areas of Interest (AoI) in outdoor environments by groups of closely cooperating Micro Aerial Vehicles (MAVs) is tackled in this paper. In the cooperative surveillance mission, finding distributions of the MAVs in the environment to properly cover the AoIs and finding feasible trajectories to reach the obtained surveillance locations from the initial depot are crucial tasks that have to be fulfilled. In addition, motion constraints of the employed MAVs, environment constraints (e.g. non-fly zones), and constraints imposed by localization of members of the groups need to be satisfied in the planning process. We formulate the task of cooperative surveillance as a single high-dimensional optimization problem to be able to integrate all these requirements. Due to numerous constraints that have to be satisfied, we propose to solve the problem using an evolutionary-based optimization technique. An important aspect of the proposed method is that the cooperating MAVs are localized relatively to each other, rather than using a global localization system. This increases robustness of the system and its deploy-ability in scenarios, in which compact shapes of the MAV group with short relative distances are required.

Swarm Distribution and Deployment for Cooperative Surveillance by Micro-Aerial Vehicles

E-learning is a modern and effective approach for training in various areas and at different levels of education. This paper gives an overview of SyRoTek, an e-learning platform for mobile robotics, artificial intelligence, control engineering, and related domains. SyRoTek provides remote access to a set of fully autonomous mobile robots placed in a restricted area with dynamically reconfigurable obstacles, which enables solving a huge variety of problems. A user is able to control the robots in real time by their own developed algorithms as well as being able to analyze gathered data and observe activity of the robots by provided interfaces. The system is currently used for education at the Czech Technical University in Prague, Prague, Czech Republic, and at the University of Buenos Aires, Buenos, Aires, Argentina, and it is freely accessible to other institutions. In addition to the system overview, this paper presents the experience gained from the actual deployment of the system in teaching activities.

SyRoTek—Distance Teaching of Mobile Robotics

An algorithm for autonomous deployment of groups of Micro Aerial Vehicles (MAVs) in the cooperative surveillance task is presented in this paper. The algorithm enables to find a proper distributions of all MAVs in surveillance locations together with feasible and collision free trajectories from their initial position. The solution of the MAV-group deployment satisfies motion constraints of MAVs, environment constraints (non-fly zones) and constraints imposed by a visual onboard relative localization. The onboard relative localization, which is used for stabilization of the group flying in a compact formation, acts as an enabling technique for utilization of MAVs in situations where an external local system is not available or lacks the sufficient precision.

Jan Chudoba

Papers

System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

Low-cost embedded system for relative localization in robotic swarms

Swarm Distribution and Deployment for Cooperative Surveillance by Micro-Aerial Vehicles

SyRoTek—Distance Teaching of Mobile Robotics

Autonomous deployment of swarms of micro-aerial vehicles in cooperative surveillance