Autonomous deployment and repair of a sensor network using an unmanned aerial vehicle

We describe a sensor network deployment method using autonomous flying robots. Such networks are suitable for tasks such as large-scale environmental monitoring or for command and control in emergency situations. We describe in detail the algorithms used for deployment and for measuring network connectivity and provide experimental data we collected from field trials. A particular focus is on determining gaps in connectivity of the deployed network and generating a plan for a second, repair, pass to complete the connectivity. This project is the result of a collaboration between three robotics labs (CSIRO, USC, and Dartmouth.)

Control for Localization and Visibility Maintenance of an Independent Agent using Robotic Teams

Given a non-cooperative agent, we seek to formulate a control strategy to enable a team of robots to localize and track the agent in a complex but known environment while maintaining a continuously optimized line-of-sight communication chain to a fixed base station. We focus on two aspects of the problem. First, we investigate the estimation of the agent\u27s location by using nonlinear sensing modalities, in particular that of range-only sensing, and formulate a control strategy based on improving this estimation using one or more robots working to independently gather information. Second, we develop methods to plan and sequence robot deployments that will establish and maintain line-of-sight chains for communication between the independent agent and the fixed base station using a minimum number of robots. These methods will lead to feedback control laws that can realize this plan and ensure proper navigation and collision avoidance

Energy-Efficient Indoor Search by Swarms of Simulated Flying Robots Without Global Information

Swarms of flying robots are a promising alternative to ground-based robots for search in indoor environments with advantages such as increased speed and the ability to fly above obstacles. However, there are numerous problems that must be surmounted including limitations in available sensory and on-board processing capabilities, and low flight endurance. This paper introduces a novel strategy to coordinate a swarm of flying robots for indoor exploration that significantly increases energy efficiency. The presented algorithm is fully distributed and scalable. It relies solely on local sensing and low-bandwidth communication, and does not require absolute positioning, localisation, or explicit world-models. It assumes that flying robots can temporarily attach to the ceiling, or land on the ground for efficient surveillance over extended periods of time. To further reduce energy consumption, the swarm is incrementally deployed by launching one robot at a time. Extensive simulation experiments demonstrate that increasing the time between consecutive robot launches significantly lowers energy consumption by reducing total swarm flight time, while also decreasing collision probability. As a trade-off, however, the search time increases with increased inter-launch periods. These effects are stronger in more complex environments. The proposed localisation-free strategy provides an energy efficient search behaviour adaptable to different environments or timing constraints

Survey of Deployment Algorithms in Wireless Sensor Networks: Coverage and Connectivity Issues and Challenges

International audienceWireless Sensor Networks (WSNs) have many fields of application, including industrial, environmental, military, health and home domains. Monitoring a given zone is one of the main goals of this technology. This consists in deploying sensor nodes in order to detect any event occurring in the zone of interest considered and report this event to the sink. The monitoring task can vary depending on the application domain concerned. In the industrial domain, the fast and easy deployment of wireless sensor nodes allows a better monitoring of the area of interest in temporary worksites. This deployment must be able to cope with obstacles and be energy efficient in order to maximize the network lifetime. If the deployment is made after a disaster, it will operate in an unfriendly environment that is discovered dynamically. We present a survey that focuses on two major issues in WSNs: coverage and connectivity. We motivate our study by giving different use cases corresponding to different coverage, connectivity, latency and robustness requirements of the applications considered. We present a general and detailed analysis of deployment problems, while highlighting the impacting factors, the common assumptions and models adopted in the literature, as well as performance criteria for evaluation purposes. Different deployment algorithms for area, barrier, and points of interest are studied and classified according to their characteristics and properties. Several recapitulative tables illustrate and summarize our study. The designer in charge of setting up such a network will find some useful recommendations, as well as some pitfalls to avoid. Before concluding, we look at current trends and discuss some open issues

Wireless Sensor Networks for Building Robotic Paths - A Survey of Problems and Restrictions

The conjugation of small nodes with sensing, communication and processing capabilities allows for the creation of wireless sensor networks (WSNs). These networks can be deployed to measure a very wide range of environmental phenomena and send data from remote locations back to users. They offer new and exciting possibilities for applications and research. This paper presents the background of WSNs by firstly exploring the different fields applications, with examples for each of these fields, then the challenges faced by these networks in areas such as energy-efficiency, node localization, node deployment, limited storage and routing. It aims at explaining each issue and giving solutions that have been proposed in the research literature. Finally, the paper proposes a practical scenario of deploying a WSN by autonomous robot path construction. The requirements for such a scenario and the open issues that can be tackled by it are exposed, namely the issues of associated with measuring RSSI, the degree of autonomy of the robot and connectivity restoration.The authors would like to acknowledge the company Inspiring Sci, Lda for the interest and valuable contribution to the successful development of this work.info:eu-repo/semantics/publishedVersio

Distributed and Dynamic Map-less Self-reconfiguration for Microrobot Networks

International audienceMEMS micro robots are low-power and low memory capacity devices that can sense and act. One of the most challenges in MEMS micro robot applications is the self-reconfiguration, especially when the efficiency and the scalability of the algorithm are required. In the literature, if we want a self-reconfiguration of micro robots to a target shape consisting of P positions, each micro robot should have a memory capacity of P positions. Therefore, if P equals to millions, each node should have a memory capacity of millions of positions. Therefore, this is not scalable. In this paper, nodes do not record any position, we present a self-reconfiguration method where a set of micro robots are unaware of their current position and do not have the map of the target shape. In other words, nodes do not store the positions that build the target shape. Consequently, memory usage for each node is reduced to O(1). An algorithm of self-reconfiguration to optimize the communication is deeply studied showing how to manage the dynamicity (wake up and sleep of micro robots) of the network to save energy. Our algorithm is implemented in Meld, a declarative language, and executed in a real environment simulator called DPRSim

Edge Computing for Internet of Things

The Internet-of-Things is becoming an established technology, with devices being deployed in homes, workplaces, and public areas at an increasingly rapid rate. IoT devices are the core technology of smart-homes, smart-cities, intelligent transport systems, and promise to optimise travel, reduce energy usage and improve quality of life. With the IoT prevalence, the problem of how to manage the vast volumes of data, wide variety and type of data generated, and erratic generation patterns is becoming increasingly clear and challenging. This Special Issue focuses on solving this problem through the use of edge computing. Edge computing offers a solution to managing IoT data through the processing of IoT data close to the location where the data is being generated. Edge computing allows computation to be performed locally, thus reducing the volume of data that needs to be transmitted to remote data centres and Cloud storage. It also allows decisions to be made locally without having to wait for Cloud servers to respond