Adoption of vehicular ad hoc networking protocols by networked robots

This paper focuses on the utilization of wireless networking in the robotics domain. Many researchers have already equipped their robots with wireless communication capabilities, stimulated by the observation that multi-robot systems tend to have several advantages over their single-robot counterparts. Typically, this integration of wireless communication is tackled in a quite pragmatic manner, only a few authors presented novel Robotic Ad Hoc Network (RANET) protocols that were designed specifically with robotic use cases in mind. This is in sharp contrast with the domain of vehicular ad hoc networks (VANET). This observation is the starting point of this paper. If the results of previous efforts focusing on VANET protocols could be reused in the RANET domain, this could lead to rapid progress in the field of networked robots. To investigate this possibility, this paper provides a thorough overview of the related work in the domain of robotic and vehicular ad hoc networks. Based on this information, an exhaustive list of requirements is defined for both types. It is concluded that the most significant difference lies in the fact that VANET protocols are oriented towards low throughput messaging, while RANET protocols have to support high throughput media streaming as well. Although not always with equal importance, all other defined requirements are valid for both protocols. This leads to the conclusion that cross-fertilization between them is an appealing approach for future RANET research. To support such developments, this paper concludes with the definition of an appropriate working plan

Deployment and navigation of aerial drones for sensing and interacting applications

Existing research recognises the critical role played by Unmanned Aerial Vehicles (UAVs) (also referred to as drones) to numerous civilian applications. Typical drone applications include surveillance, wireless communication, agriculture, among many others. One of the biggest challenges is to determine the deployment and navigation of the drones to benefit the most for different applications. Many research questions have been raised about this topic. For example, drone-enabled wildlife monitoring has received much attention in recent years. Unfortunately, this approach results in significant disturbance to different species of wild animals. Moreover, with the capability of rapidly moving communication supply towards demand when required, the drone equipped with a base station, i.e., drone-cell, is becoming a promising solution for providing cellular networks to victims and rescue teams in disaster-affected areas. However, few studies have investigated the optimal deployments of multiple drone-cells with limited backhaul communication distances. In addition, the use of autonomous drones as flying interactors for many real-life applications has not been sufficiently discussed. With superior maneuverability, drone-enabled autonomous aerial interacting can potentially be used on shark attack prevention and animal herding. Nevertheless, previous studies of autonomous drones have not dealt with such applications in much detail. This thesis explores the solutions to all the mentioned research questions, with a particular focus on the deployment and navigation of the drones. First, we provide one of the first investigations into reducing the negative impacts of wildlife monitoring drones by navigation control. Second, we study the optimal placement of a group of drone-cells with limited backhaul communication ranges, aims to maximise the number of served users. Third, we propose a novel method named ‘drone shark shield’, which uses communicating autonomous drones to intervene and prevent shark attacks for protecting swimmers and surfers. Lastly, we introduce one of the first autonomous drone herding systems for mustering a large number of farm animals efficiently. Simulations have been conducted to verify the effectiveness of the proposed approaches. We believe that our findings in this thesis shed new light on the fundamental benefits of autonomous civilian drones

Navigation and Control of Mobile Robots

The rapid development of robotics has benefited by more and more people putting their attention to it. In the 1920s, ‘Robota’, a similar concept, was first known to the world. It is proposed in Karel Capek’ s drama, Rossum’ s Universal Robots (RUR). From then on, numbers of automatic machines were created all over the world, which are known as the robots of the early periods. Gradually, the demand for robots is growing for the purpose of fulfilling tasks instead of humans. From industrial uses, to the military, to education and entertainment, di↵erent kinds of robots began to serve humans in various scenarios. Based on this, how to control the robot better is becoming a hot topic. For the topic of navigating and controlling mobile robots, number of related problems have been carried out. Obstacle avoidance, path planning, cooperative work of multi-robots. In this thesis, we focus on the first two problems, and mention the last one as a future direction in the last part. For obstacle avoidance, we proposed algorithms for both 2D planar environ- ments and 3D space environments. The example cases we raise are those that need to be addressed but have always been ignored. To be specific, the motion of the obstacles are not fixed, the shape of the obstacles are changeable, and the sensors that could be deployed for underwater environments are limited. We even put those problems together to solve them. The methods we proposed are based on the biologically inspired algorithm and Back Propagation Neural network (BPNN). In addition, we put e↵orts into trajectory planning for robots. The two scenarios we set are self-driving cars on the road and reconnaissance and surveillance of drones. The methods we deployed are the Convolutional Neural Network (CNN) method and the two-phase strategy, respectively. When we proposed the strategies, we gave a detailed description of the robot systems, the proposed algorithms. We showed the performance with simulation results to demonstrate the solutions proposed are feasible. For future expectations, there are some possible directions. When applying traditional navigation algorithms, for example, biologically inspired algorithms, we have to pay attention to the limitations of the environment. However, high-tech algorithms sometimes are not computationally friendly. How to combine them together so as to fulfill the tasks perfectly while the computational e ciency is not too high is a worthy topic. In addition, extending the obstacle avoidance al- gorithms to more competitive situations, such as applying to autonomous UAVs, is also being considered. Moreover, for cooperation among multi robots, which could be regarded as Network Control System (NCS), the issues, such as how to complete their respective tasks, how to choose the optimal routes for them are worth attention by researchers. All in all, there is still a long way to go for the development of navigation and control of mobile robots. Despite this, we believe we do not need to wait for too long time to see the revolution of robots

Joint Estimation and Control for Multi-Target Passive Monitoring with an Autonomous UAV Agent

This work considers the problem of passively monitoring multiple moving targets with a single unmanned aerial vehicle (UAV) agent equipped with a direction-finding radar. This is in general a challenging problem due to the unobservability of the target states, and the highly non-linear measurement process. In addition to these challenges, in this work we also consider: a) environments with multiple obstacles where the targets need to be tracked as they manoeuvre through the obstacles, and b) multiple false-alarm measurements caused by the cluttered environment. To address these challenges we first design a model predictive guidance controller which is used to plan hypothetical target trajectories over a rolling finite planning horizon. We then formulate a joint estimation and control problem where the trajectory of the UAV agent is optimized to achieve optimal multi-target monitoring

Data collection of mobile sensor networks by drones

Data collection by autonomous mobile sensor arrays can be coupled with the use of drones which provide a low-cost, easily deployable backhauling solution. These means of collection can be used to organize temporary events (sporting or cultural) or to carry out operations in difficult or hostile terrain. The aim of this thesis is to propose effective solutions for communication between both mobile sensors on the ground and on the edge-to-ground link. For this purpose, we are interested in scheduling communications, routing and access control on the sensor / drone link, the mobile collector. We propose an architecture that meets the constraints of the network. The main ones are the intermittence of the links and therefore the lack of connectivity for which solutions adapted to the networks tolerant to the deadlines are adopted. Given the limited opportunities for communication with the drone and the significant variation in the physical data rate, we proposed scheduling solutions that take account of both the contact time and the physical flow rate. Opportunistic routing is also based on these two criteria both for the selection of relay nodes and for the management of queues. We wanted to limit the overhead and propose efficient and fair solutions between mobile sensors on the ground. The proposed solutions have proved superior to conventional scheduling and routing solutions. Finally, we proposed a method of access combining a random access with contention as well as an access with reservation taking into account the aforementioned criteria. This flexible solution allows a network of dense mobile sensors to get closer to the performance obtained in an oracle mode. The proposed solutions can be implemented and applied in different application contexts for which the ground nodes are mobile or easily adapted to the case where the nodes are static

An efficient distributed area division method for cooperative monitoring applications with multiple uavs

This article addresses the area division problem in a distributed manner providing a solution for cooperative monitoring missions with multiple UAVs. Starting from a sub-optimal area division, a distributed online algorithm is presented to accelerate the convergence of the system to the optimal solution, following a frequency-based approach. Based on the “coordination variables” concept and on a strict neighborhood relation to share information (left, right, above and below neighbors), this technique defines a distributed division protocol to determine coherently the size and shape of the sub-area assigned to each UAV. Theoretically, the convergence time of the proposed solution depends linearly on the number of UAVs. Validation results, comparing the proposed approach with other distributed techniques, are provided to evaluate and analyze its performance following a convergence time criterion.European Union’s Horizon 2020 AERIAL-CORE Project Grant 871479CDTI (sPAIN) “Red Cervera” Programme iMOV3D Spanish R&D projec