Fast multipole networks

Two prerequisites for robotic multiagent systems are mobility and communication. Fast multipole networks (FMNs) enable both ends within a unified framework. FMNs can be organized very efficiently in a distributed way from local information and are ideally suited for motion planning using artificial potentials. We compare FMNs to conventional communication topologies, and find that FMNs offer competitive communication performance (including higher network efficiency per edge at marginal energy cost) in addition to advantages for mobility

Simultaneous Deployment and Tracking Multi-Robot Strategies with Connectivity Maintenance

Multi robot teams composed by ground and aerial vehicles have gained attention during the last years. We present a scenario where both types of robots must monitor the same area from different view points. In this paper we propose two Lloyd-based tracking strategies to allow the ground robots (agents) follow the aerial ones (targets), keeping the connectivity between the agents. The first strategy establishes density functions on the environment so that the targets acquire more importance than other zones, while the second one iteratively modifies the virtual limits of the working area depending on the positions of the targets. We consider the connectivity maintenance due to the fact that coverage tasks tend to spread the agents as much as possible, which is addressed by restricting their motions so that they keep the links of a Minimum Spanning Tree of the communication graph. We provide a thorough parametric study of the performance of the proposed strategies under several simulated scenarios. In addition, the methods are implemented and tested using realistic robotic simulation environments and real experiments

Simultaneous deployment and tracking multi-robot strategies with connectivity maintenance

Multi-robot teams composed of ground and aerial vehicles have gained attention during the last few years. We present a scenario where both types of robots must monitor the same area from different view points. In this paper, we propose two Lloyd-based tracking strategies to allow the ground robots (agents) to follow the aerial ones (targets), keeping the connectivity between the agents. The first strategy establishes density functions on the environment so that the targets acquire more importance than other zones, while the second one iteratively modifies the virtual limits of the working area depending on the positions of the targets. We consider the connectivity maintenance due to the fact that coverage tasks tend to spread the agents as much as possible, which is addressed by restricting their motions so that they keep the links of a minimum spanning tree of the communication graph. We provide a thorough parametric study of the performance of the proposed strategies under several simulated scenarios. In addition, the methods are implemented and tested using realistic robotic simulation environments and real experiments

Path Planning and Communication Strategies to Enable Connectivity in Robotic Systems

There has been considerable interest in the area of communication-aware robotics in recent years, where the sensing, communication and motion objectives of robotic systems are jointly optimized. One particular open problem in this area is that of exploiting the mobility of unmanned vehicles in order to improve or satisfy communication objectives in realistic communication environments. Progress in this field could not only affect robust networked operation of unmanned vehicles but also would improve communication systems of the future (e.g. 5G), thus contributing to both areas of robotics and communications. This mobility-enabled connectivity and communication is the main area of interest in this dissertation.This dissertation is focused on path planning and communication strategies for robotic systems seeking to satisfy certain communication objectives in realistic communication environments experiencing path loss, shadowing and multipath fading. We consider realistic communication environments by leveraging and incorporating a probabilistic channel prediction framework that allows the robots to predict the channel quality at unvisited locations. This thesis then contributes to the area of mobility and connectivity through three main topics 1) energy-optimal distributed beamforming, 2) finding the statistics of the distance traveled until connectivity, and 3) path planning for connectivity. First, in energy-optimal distributed beamforming, we utilize the motion of a group of initially unconnected mobile robots to enable new forms of connectivity. More specifically, we co-optimize their locations and transmission powers to cooperatively enable connectivity through distributed beamforming. We further bring a foundational theoretical understanding to robotic distributed beamforming. Next, in finding the statistics of the distance traveled until connectivity, we analytically characterize the probability density function of the distance traveled by an initially unconnected robot until it gets connected to a remote node as it moves along a given path. We utilize tools from the stochastic differential equation literature to develop this characterization. Finally, in path planning for connectivity, we actively plan the path of a mobile robot such that it finds a connected spot with a minimum expected traveled distance (i.e., energy). The scenario considered in this part is in fact a more general one, and tackles the problem of path planning on a graph to minimize the expected cost incurred until the successful completion of a task. This framework has applications beyond path planning for connectivity, in areas such as celestial body imaging, human-robot collaboration, and search scenarios. We bring a foundational understanding to this problem. We show how this problem is inherently hard to solve (NP-complete) and also propose a path planner, based on a game-theoretic framework, that provides an asymptotic optimality guarantee.Overall, this thesis proposes novel strategies for utilizing the mobility of unmanned vehicles and enabling connectivity while considering the underlying energy constraints. We also provide a rigorous theoretical analysis of the aforementioned problems using a wide range of tools from communications theory, game theory, optimal control and time series literature. Moreover, through extensive realistic numerical studies using real channel parameters/data, we show the efficiency and performance of our proposed approaches

Mobile sensors management algorithms for environmental monitoring

Recent progresses in automatic mobile robot platforms make the development of mobile sensors possible. When different kinds of sensors are installed on different mobile agents platforms, we can deploy the mobile sensor system to monitor large scale and dynamically changing environmental fields. Comparing to traditional fixed sensor networks, teams of mobile sensors have the advantage in mobility and flexibility. They are also easier and cheaper than fixed sensor networks to maintain. To deploy mobile sensors to efficiently complete monitoring or tracking tasks, management algorithms are in need. Practical mobile sensor management algorithms should be able to be implemented in real time. Moreover, algorithms with theoretical performance guarantees will have more credits. In this thesis, we investigate in problems related to mobile sensor management algorithms. We start from giving an overview of the current developments of mobile sensor management algorithms. Taking the application of flood monitoring as a concrete example, we first analyze the necessary components in mobile sensor management algorithms. Through this example, we point out that algorithms which can efficiently assign tasks among multi-agent systems and schedule sequences of tasks for mobile sensors are in need. This thesis then study the two topics separately. We first proposed two algorithms that can efficiently assign tasks among multi-agent systems. These two task assignment algorithms can all be implemented in fully decentralized style with reasonable computational complexity. We proved the suboptimality guarantee when the objective function are submodular functions. In the following chapter, we proposed an online scheme to schedule tasks for mobile agents with the goal to seek for extremum values in a field. The proposed online scheme is inspired by the Upper Confidence Bound (UCB) algorithm in bandits problems. In order to allow each agent to compute the UCB efficiently, we proposed a new concept called Dummy Upper Confidence Bound (D-UCB). With a compromise in regret loss, the online scheme can be implemented in real-time using D-UCB. Finally, we consider the case where there are unknown disturbances present in the environment. The online scheme is adapted to detect those disturbances. We theoretically show that both two online schemes will guarantee mobile sensors to track extremum values in the field. All the algorithms proposed in this thesis are validated through numerical experiments. In the end, we show how these proposed algorithms can be combined together to manage mobile sensors efficiently. We also pointed out future directions to continue developing practical mobile sensor management algorithms.Civil, Architectural, and Environmental Engineerin