HAC-ER: a disaster response system based on human-agent collectives

This paper proposes a novel disaster management system called HAC-ER that addresses some of the challenges faced by emergency responders by enabling humans and agents, using state-of-the-art algorithms, to collaboratively plan and carry out tasks in teams referred to as human-agent collectives. In particular, HAC-ER utilises crowdsourcing combined with machine learning to extract situational awareness information from large streams of reports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments as well as task planning for responders on the ground. Finally, HAC-ER incorporates a tool for tracking and analysing the provenance of information shared across the entire system. In summary, this paper describes a prototype system, validated by real-world emergency responders, that combines several state-of-the-art techniques for integrating humans and agents, and illustrates, for the first time, how such an approach can enable more effective disaster response operations

An Adaptive Multi-Level Quantization-Based Reinforcement Learning Model for Enhancing UAV Landing on Moving Targets

The autonomous landing of an unmanned aerial vehicle (UAV) on a moving platform is an essential functionality in various UAV-based applications. It can be added to a teleoperation UAV system or part of an autonomous UAV control system. Various robust and predictive control systems based on the traditional control theory are used for operating a UAV. Recently, some attempts were made to land a UAV on a moving target using reinforcement learning (RL). Vision is used as a typical way of sensing and detecting the moving target. Mainly, the related works have deployed a deep-neural network (DNN) for RL, which takes the image as input and provides the optimal navigation action as output. However, the delay of the multi-layer topology of the deep neural network affects the real-time aspect of such control. This paper proposes an adaptive multi-level quantization-based reinforcement learning (AMLQ) model. The AMLQ model quantizes the continuous actions and states to directly incorporate simple Q-learning to resolve the delay issue. This solution makes the training faster and enables simple knowledge representation without needing the DNN. For evaluation, the AMLQ model was compared with state-of-art approaches and was found to be superior in terms of root mean square error (RMSE), which was 8.7052 compared with the proportional-integral-derivative (PID) controller, which achieved an RMSE of 10.0592

Formation of a Wireless Communication System Based on a Swarm of Unmanned Aerial Vehicles

Проблематика. На даний час бурхливо розвивається новий напрямок в техніці рухомих систем, пов'язаний із застосуванням множини/групи рухомих багатофункціональних вузлів, які можуть створювати різні просторово-розподілені структури для різних застосувань: від розважальних шоу, до розвідувальної мережі. Йдеться про техніку малих безпілотних літальних апаратів(БЛА), частіше званих дронами, та їх використання в області побудови телекомунікаційних систем. Мета. Метою роботи є розробка основних принципів і стратегій для формування неоднорідної безпроводової системи зв'язку на базі рою безпілотних літаючих апаратів. Методи. Досліджуються структурно-функціональні методи побудови безпроводової мережі. Результати. Представлені сценарії централізованої і розподіленої побудови безпроводової мережі керування рою БЛА, проведена оцінка ускладнення функціональності вузлів рою в разі розподіленого сценарію. Розроблено схему поетапної реалізації життєвого циклу рою БЛА для послуг зв'язку. Представлений«молекулярний» сценарій просторової самоорганізації дронів-вузлів рою, який може бути реалізований за допомогою процедур«ланцюжка» і«спалаху». Запропоновано побудови деяких стратегій управління роєм: централізоване і децентралізоване з Ведучим, колективне само керування з обміном інформацією, децентралізоване керування з прогнозуванням, самоорганізація без обміну інформацією. Висновки. Розроблено основні принципи і стратегії формування неоднорідної безпроводової системи зв'язку на базі рою безпілотних літаючих апаратів. Розроблено стратегію колективного управління роєм дронів.Background. Currently, a new direction in the technology of mobile systems is rapidly developing, associated with the use of a set / group of mobile multifunctional elements that can create different spatially-distributed structures for various applications: from entertainment shows to intelligence networks. This is a technique of small unmanned aerial vehicles (UAV), often called drones, and their use in the field of building telecommunication systems. Objective. The aim of the work is to develop the basic principles and strategies for the formation of a heterogeneous wireless communication system based on a swarm of unmanned aerial vehicles. Methods. We study the structural and functional methods of building a wireless network. Results. Scenarios of centralized and distributed building of a wireless network of control of a swarm of UAVs are presented, assessment of the complexity of the functionality of swarm nodes inthe case of a distributed scenario is carried out. A schemeof phased implementation of the life cycle of a UAV swarm for communication services has been developed. The “molecular” scenario of spatial self-organization of the swarm-nodes of the swarm is presented, which can beimplemented using the “chain” and “flash” procedures. The proposed construction of some strategies for managing the swarm: centralized and decentralized with the Leader, collective self-management with information sharing,decentralized management withforecasting, self-organization without information sharing. Conclusions. The basic principles and strategies for the formation of a heterogeneous wireless communication system based on a swarm of unmanned aerial vehicles have been developed. A collective management strategy for a swarm of drones was developed. Keywords:swarm of unmanned aerial vehicles; drone swarm; communication system; life cycle; control network.Проблематика. В настоящее время очень бурно развивается новое направление в технике подвижных систем, связанное с применением множества/группы подвижных многофункциональных элементов, которые могут создавать различные пространственно-распределенные структуры для различных применений: от развлекательных шоу, до разведывательной сети. Речь идет о технике малых беспилотных летающих аппаратов(БЛА), чаще называемых дронами, и их использование в области построения телекоммуникационных систем. Цель. Целью работы является разработка основных принципов и стратегий для формирования неоднородной беспроводной системы связи на базе роя беспилотных летающих аппаратов. Методы. Исследуются структурно-функциональные методы построения беспроводной сети. Результаты. Представлены сценарии централизованного и распределенного построения беспроводной сети управления роя БЛА, проведена оценка усложнения функциональности узлов роя в случае распределенного сценария. Разработана схема поэтапной реализации жизненного цикла роя БЛА для услуг связи. Представлен«молекулярный» сценарий пространственной самоорганизации дронов-узлов роя, который может быть реализован посредством процедур

Multi-objective Decentralised Coordination for Teams of Robotic Agents

This thesis introduces two novel coordination mechanisms for a team of multiple autonomous decision makers, represented as autonomous robotic agents. Such techniques aim to improve the capabilities of robotic agents, such as unmanned aerial or ground vehicles (UAVs and UGVs), when deployed in real world operations. In particular, the work reported in this thesis focuses on improving the decision making of teams of such robotic agents when deployed in an unknown, and dynamically changing, environment to perform search and rescue operations for lost targets. This problem is well known and studied within both academia and industry and coordination mechanisms for controlling such teams have been studied in both the robotics and the multi-agent systems communities. Within this setting, our first contribution aims at solves a canonical target search problem, in which a team of UAVs is deployed in an environment to search for a lost target. Specifically, we present a novel decentralised coordination approach for teams of UAVs, based on the max-sum algorithm. In more detail, we represent each agent as a UAV, and study the applicability of the max-sum algorithm, a decentralised approximate message passing algorithm, to coordinate a team of multiple UAVs for target search. We benchmark our approach against three state-of-the-art approaches within a simulation environment. The results show that coordination with the max-sum algorithm out-performs a best response algorithm, which represents the state of the art in the coordination of UAVs for search, by up to 26%, an implicitly coordinated approach, where the coordination arises from the agents making decisions based on a common belief, by up to 34% and finally a non-coordinated approach by up to 68%. These results indicate that the max-sum algorithm has the potential to be applied in complex systems operating in dynamic environments. We then move on to tackle coordination in which the team has more than one objective to achieve (e.g. maximise the covered space of the search area, whilst minimising the amount of energy consumed by each UAV). To achieve this shortcoming, we present, as our second contribution, an extension of the max-sum algorithm to compute bounded solutions for problems involving multiple objectives. More precisely, we develop the bounded multi-objective max-sum algorithm (B-MOMS), a novel decentralised coordination algorithm able to solve problems involving multiple objectives while providing guarantees on the solution it recovers. B-MOMS extends the standard max-sum algorithm to compute bounded approximate solutions to multi-objective decentralised constraint optimisation problems (MO-DCOPs). Moreover, we prove the optimality of B-MOMS in acyclic constraint graphs, and derive problem dependent bounds on its approximation ratio when these graphs contain cycles. Finally, we empirically evaluate its performance on a multi-objective extension of the canonical graph colouring problem. In so doing, we demonstrate that, for the settings we consider, the approximation ratio never exceeds $2$, and is typically less than $1.5$ for less-constrained graphs. Moreover, the runtime required by B-MOMS on the problem instances we considered never exceeds $30$ minutes, even for maximally constrained graphs with one hundred agents