Deep reinforcement learning for drone navigation using sensor data

Mobile robots such as unmanned aerial vehicles (drones) can be used for surveillance, monitoring and data collection in buildings, infrastructure and environments. The importance of accurate and multifaceted monitoring is well known to identify problems early and prevent them escalating. This motivates the need for flexible, autonomous and powerful decision-making mobile robots. These systems need to be able to learn through fusing data from multiple sources. Until very recently, they have been task specific. In this paper, we describe a generic navigation algorithm that uses data from sensors on-board the drone to guide the drone to the site of the problem. In hazardous and safety-critical situations, locating problems accurately and rapidly is vital. We use the proximal policy optimisation deep reinforcement learning algorithm coupled with incremental curriculum learning and long short-term memory neural networks to implement our generic and adaptable navigation algorithm. We evaluate different configurations against a heuristic technique to demonstrate its accuracy and efficiency. Finally, we consider how safety of the drone could be assured by assessing how safely the drone would perform using our navigation algorithm in real-world scenarios

Drones, Smartphones and Sensors to Face Natural Disasters

International audienc

Implementing a System Architecture for Data and Multimedia Transmission in a Multi-UAV System

Part 5: Aerial NetworksInternational audienc

Efficient wireless sensor deployment at minimum cost

International audienceWe address the problem of defining a wireless sensor network by deploying sensors with the aim of guaranteeing the coverage of the area and the connectivity among the sensors. The wireless sensor networks are widely studied since they provide several services, e.g., environmental monitoring and target tracking. We consider several typologies of sensors characterized by different sensing and connectivity ranges. A cost is associated with each typology of sensors. In particular, the higher the sensing and connectivity ranges, the higher the cost. We formulate the problem of deploying sensors at minimum cost such that each sensor is connected to a base station with either a one-or a multi-hop and the area is full covered. We present preliminary computational results by solving the proposed mathematical model, on several instances. We provide a simulation-based analysis of the performances of such a deployment from the routing perspective

Extending network tree lifetime with mobile and rechargeable nodes

International audienceIn this paper, we assume network trees consisting of mobile, energy constrained and rechargeable nodes as well as a static sink which collects the monitoring data and it is the root of the tree. Almost exhausted nodes can autonomously move towards a charging point to recharge their battery. However, this action leads to network disconnections and reduced lifetime since one or more predecessor nodes cannot forward their data to the sink. To alleviate this problem and extend network lifetime we examine the feasibility of replacing almost exhausted nodes using nodes with higher remaining energy. Based on this idea we propose a localized algorithm to autonomously replace nodes with high communication burden by the leaves of the tree. Both theoretical and simulation results show a big improvement in terms of network lifetime extension compared to the case where no replacement is performed and to the case where rerouting is considered

Unmanned Aerial Vehicles for Disaster Management

This is the author accepted manuscript. The final version is available from Springer via the DOI in this record.This chapter highlights the communication and network technologies that contribute to UAV disaster management systems, surveys the latest development of UAV-assisted disaster management applications, including early warning system, search and rescue, data gathering, emergency communication, and logistics, and presents our preliminary work to demonstrate the benefits and challenges of UAV systems for emergency communication. Finally, we discuss the characteristics and design challenges of UAV disaster management systems