Coordinating Aerial Robots and Unattended Ground Sensors for Intelligent Surveillance Systems

Sensor networks are being used to implement different types of sophisticated emerging applications, such as those aimed at supporting ambient intelligence and surveillance systems. This usage is enhanced by employing sensors with different characteristics in terms of sensing, computing and mobility capabilities, working cooperatively in the network. However, the design and deployment of these heterogeneous systems present several issues that have to be handled in order to meet the user expectations. The main problems are related to the nodes‘ interoperability and the overall resource allocation, both inter and intra nodes. The first problem requires a common platform that abstracts the nodes’ heterogeneity and provides a smooth communication, while the second is handled by cooperation mechanisms supported by the platform. Moreover, as the nodes are supposed to be heterogeneous, a customizable platform is required to support both resource rich and poorer nodes. This paper analyses surveillance systems based on a heterogeneous sensor network, which is composed by lowend ground sensor nodes and autonomous aerial robots, i.e. Unmanned Aerial Vehicles (UAVs), carrying different kinds of sensors. The approach proposed in this work tackles the two above mentioned problems by using a customizable hardware platform and a middleware to support interoperability. Experimental results are also provided

Asynchronous displays for multi-UV search tasks

Synchronous video has long been the preferred mode for controlling remote robots with other modes such as asynchronous control only used when unavoidable as in the case of interplanetary robotics. We identify two basic problems for controlling multiple robots using synchronous displays: operator overload and information fusion. Synchronous displays from multiple robots can easily overwhelm an operator who must search video for targets. If targets are plentiful, the operator will likely miss targets that enter and leave unattended views while dealing with others that were noticed. The related fusion problem arises because robots' multiple fields of view may overlap forcing the operator to reconcile different views from different perspectives and form an awareness of the environment by "piecing them together". We have conducted a series of experiments investigating the suitability of asynchronous displays for multi-UV search. Our first experiments involved static panoramas in which operators selected locations at which robots halted and panned their camera to capture a record of what could be seen from that location. A subsequent experiment investigated the hypothesis that the relative performance of the panoramic display would improve as the number of robots was increased causing greater overload and fusion problems. In a subsequent Image Queue system we used automated path planning and also automated the selection of imagery for presentation by choosing a greedy selection of non-overlapping views. A fourth set of experiments used the SUAVE display, an asynchronous variant of the picture-in-picture technique for video from multiple UAVs. The panoramic displays which addressed only the overload problem led to performance similar to synchronous video while the Image Queue and SUAVE displays which addressed fusion as well led to improved performance on a number of measures. In this paper we will review our experiences in designing and testing asynchronous displays and discuss challenges to their use including tracking dynamic targets. © 2012 by the American Institute of Aeronautics and Astronautics, Inc

Editorial – Special Issue on Collaboration Support Systems (CSS)

Recent and emerging advances in computer and information science and technology have realized a powerful computing and communication environment. It enables effective interactions and collaboration among groups of people and systems (and systems-of-systems) beyond traditional restrictions of time and space. The evolution in hardware (e.g., pervasive computing devices, wireless sensor networks, nano-electronics) and software (e.g., multi-agent systems, workflow and information integration, interaction models and protocols) technology, and their flexible teaming have further enabled diverse forms of collaboration approaches. It has been observed during the last few decades that numerous collaboration methodologies, tools and applications in various domains have emerged to provide better quality services, helping to solve domain-specific, highly complex problems. The development of collaboration tools and methodologies has increased the domain knowledge that can be discovered and shared by individuals, and the level and intensity of interactions and collaboration that can dramatically decrease problem complexity and increase solution quality. At the same time, inefficient interactions, task and information overloads, and ineffective collaboration are prevalent

On the role and opportunities in teamwork design for advanced multi-robot search systems

Intelligent robotic systems are becoming ever more present in our lives across a multitude of domains such as industry, transportation, agriculture, security, healthcare and even education. Such systems enable humans to focus on the interesting and sophisticated tasks while robots accomplish tasks that are either too tedious, routine or potentially dangerous for humans to do. Recent advances in perception technologies and accompanying hardware, mainly attributed to rapid advancements in the deep-learning ecosystem, enable the deployment of robotic systems equipped with onboard sensors as well as the computational power to perform autonomous reasoning and decision making online. While there has been significant progress in expanding the capabilities of single and multi-robot systems during the last decades across a multitude of domains and applications, there are still many promising areas for research that can advance the state of cooperative searching systems that employ multiple robots. In this article, several prospective avenues of research in teamwork cooperation with considerable potential for advancement of multi-robot search systems will be visited and discussed. In previous works we have shown that multi-agent search tasks can greatly benefit from intelligent cooperation between team members and can achieve performance close to the theoretical optimum. The techniques applied can be used in a variety of domains including planning against adversarial opponents, control of forest fires and coordinating search-and-rescue missions. The state-of-the-art on methods of multi-robot search across several selected domains of application is explained, highlighting the pros and cons of each method, providing an up-to-date view on the current state of the domains and their future challenges

Multi-Robot Auction Based Coordination

This dissertation studied the coordination problem for a Task Initiator (TI) with multiple ground stations (GSs). Each GS has a team of unmanned aerial vehicles (UAVs) that frequently collected data from a set of unattended ground sensors (UGSs) and delivered it to the source ground station (GS)

Feasibility Study to Determine the Economic and Operational Benefits of Utilizing Unmanned Aerial Vehicles (UAVs)

This project explored the feasibility of using Unmanned Aerial Systems (UASs) in Georgia Department of Transportation (GDOT) operations. The research team conducted 24 interviews with personnel in four GDOT divisions. Interviews focused on (1) the basic goals of the operators in each division, (2) their major decisions for accomplishing those goals, and (3) the information requirements for each decision. Following an interview validation process, a set of UASs design characteristics that fulfill user requirements of each previously identified division was developed. A “House of Quality” viewgraph was chosen to capture the relationships between GDOT tasks and potential UAS aiding those operations. As a result, five reference systems are proposed. The UAS was broken into three components: vehicle, control station, and system. This study introduces a variety of UAS applications in traffic management, transportation and construction disciplines related to DOTs, such as the ability to get real time, digital photographs/videos of traffic scenes, providing a "bird’s eye view" that was previously only available with the assistance of a manned aircraft, integrating aerial data into GDOT drawing software programs, and dealing with restricted or complicated access issues when terrain, area, or the investigated object make it difficult for GDOT personnel to conduct a task. The results of this study could lead to further research on design, development, and field-testing of UAVs for applications identified as beneficial to the Department.Georgia Department of Transportatio

Autonomous, Long-Range, Sensor Emplacement Using Unmanned Aircraft Systems

Automated, in-ground sensor emplacement can significantly improve remote, terrestrial, data collection capabilities. Utilizing a multicopter, unmanned aircraft system (UAS) for this purpose allows sensor insertion with minimal disturbance to the target site or surrounding area. However, developing an emplacement mechanism for a small multicopter, autonomy to manage the target selection and implantation process, as well as long-range deployment are challenging to address. We have developed an autonomous, multicopter UAS that can implant subsurface sensor devices. We enhanced the UAS autopilot with autonomy for target and landing zone selection, as well as ensuring the sensor is implanted properly in the ground. The multicopter UAS, limited by onboard energy, can be carried by a transport aircraft to within 1 km of the desired sensor location site and deployed by a novel parachuting-canister system. Through a comprehensive set of field trials and testing, we assess the effectiveness of each subsystem. We evaluate our system on missions covering distances up to 25 km away in mountains 1 km above the takeoff location

Distributed Control for Collective Behaviour in Micro-unmanned Aerial Vehicles

Full version unavailable due to 3rd party copyright restrictions.The work presented herein focuses on the design of distributed autonomous controllers for collective behaviour of Micro-unmanned Aerial Vehicles (MAVs). Two alternative approaches to this topic are introduced: one based upon the Evolutionary Robotics (ER) paradigm, the other one upon flocking principles. Three computer simulators have been developed in order to carry out the required experiments, all of them having their focus on the modelling of fixed-wing aircraft flight dynamics. The employment of fixed-wing aircraft rather than the omni-directional robots typically employed in collective robotics significantly increases the complexity of the challenges that an autonomous controller has to face. This is mostly due to the strict motion constraints associated with fixed-wing platforms, that require a high degree of accuracy by the controller. Concerning the ER approach, the experimental setups elaborated have resulted in controllers that have been evolved in simulation with the following capabilities: (1) navigation across unknown environments, (2) obstacle avoidance, (3) tracking of a moving target, and (4) execution of cooperative and coordinated behaviours based on implicit communication strategies. The design methodology based upon flocking principles has involved tests on computer simulations and subsequent experimentation on real-world robotic platforms. A customised implementation of Reynolds’ flocking algorithm has been developed and successfully validated through flight tests performed with the swinglet MAV. It has been notably demonstrated how the Evolutionary Robotics approach could be successfully extended to the domain of fixed-wing aerial robotics, which has never received a great deal of attention in the past. The investigations performed have also shown that complex and real physics-based computer simulators are not a compulsory requirement when approaching the domain of aerial robotics, as long as proper autopilot systems (taking care of the ”reality gap” issue) are used on the real robots.EOARD (European Office of Aerospace Research & Development), euCognitio

A Localized Autonomous Control Algorithm For Robots With Heterogeneous Capabilities In A Multi-Tier Architecture

This dissertation makes two contributions to the use of the Blackboard Architecture for command. The use of boundary nodes for data abstraction is introduced and the use of a solver-based blackboard system with pruning is proposed. It also makes contributions advancing the engineering design process in the area of command system selection for heterogeneous robotic systems. It presents and analyzes data informing decision making between centralized and distributed command systems and also characterizes the efficacy of pruning across different experimental scenarios, demonstrating when it is effective or not. Finally, it demonstrates the operations of the system, raising the technology readiness level (TRL) of the technology towards a level suitable for actual mission use. The context for this work is a multi-tier mission architecture, based on prior work by Fink on a “tier scalable” architecture. This work took a top-down approach where the superior tiers (in terms of scope of visibility) send specific commands to craft in lower tiers. While benefitting from the use of a large centralized processing center, this approach is limited in responding to failures and interference. The work presented herein has involved developing and comparatively characterizing centralized and decentralized (where superior nodes provide information and goals to the lower-level craft, but decisions are made locally) Blackboard Architecture based command systems. Blackboard Architecture advancements (a solver, pruning, boundary nodes) have been made and tested under multiple experimental conditions