Multi-agent persistent surveillance under temporal logic constraints

This thesis proposes algorithms for the deployment of multiple autonomous agents for persistent surveillance missions requiring repeated, periodic visits to regions of interest. Such problems arise in a variety of domains, such as monitoring ocean conditions like temperature and algae content, performing crowd security during public events, tracking wildlife in remote or dangerous areas, or watching traffic patterns and road conditions. Using robots for surveillance is an attractive solution to scenarios in which fixed sensors are not sufficient to maintain situational awareness. Multi-agent solutions are particularly promising, because they allow for improved spatial and temporal resolution of sensor information. In this work, we consider persistent monitoring by teams of agents that are tasked with satisfying missions specified using temporal logic formulas. Such formulas allow rich, complex tasks to be specified, such as "visit regions A and B infinitely often, and if region C is visited then go to region D, and always avoid obstacles." The agents must determine how to satisfy such missions according to fuel, communication, and other constraints. Such problems are inherently difficult due to the typically infinite horizon, state space explosion from planning for multiple agents, communication constraints, and other issues. Therefore, computing an optimal solution to these problems is often infeasible. Instead, a balance must be struck between computational complexity and optimality. This thesis describes solution methods for two main classes of multi-agent persistent surveillance problems. First, it considers the class of problems in which persistent surveillance goals are captured entirely by TL constraints. Such problems require agents to repeatedly visit a set of surveillance regions in order to satisfy their mission. We present results for agents solving such missions with charging constraints, with noisy observations, and in the presence of adversaries. The second class of problems include an additional optimality criterion, such as minimizing uncertainty about the location of a target or maximizing sensor information among the team of agents. We present solution methods and results for such missions with a variety of optimality criteria based on information metrics. For both classes of problems, the proposed algorithms are implemented and evaluated via simulation, experiments with robots in a motion capture environment, or both

A Survey on Aerial Swarm Robotics

The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas

A Novel Battery Management & Charging Solution for Autonomous UAV Systems

abstract: Currently, one of the biggest limiting factors for long-term deployment of autonomous systems is the power constraints of a platform. In particular, for aerial robots such as unmanned aerial vehicles (UAVs), the energy resource is the main driver of mission planning and operation definitions, as everything revolved around flight time. The focus of this work is to develop a new method of energy storage and charging for autonomous UAV systems, for use during long-term deployments in a constrained environment. We developed a charging solution that allows pre-equipped UAV system to land on top of designated charging pads and rapidly replenish their battery reserves, using a contact charging point. This system is designed to work with all types of rechargeable batteries, focusing on Lithium Polymer (LiPo) packs, that incorporate a battery management system for increased reliability. The project also explores optimization methods for fleets of UAV systems, to increase charging efficiency and extend battery lifespans. Each component of this project was first designed and tested in computer simulation. Following positive feedback and results, prototypes for each part of this system were developed and rigorously tested. Results show that the contact charging method is able to charge LiPo batteries at a 1-C rate, which is the industry standard rate, maintaining the same safety and efficiency standards as modern day direct connection chargers. Control software for these base stations was also created, to be integrated with a fleet management system, and optimizes UAV charge levels and distribution to extend LiPo battery lifetimes while still meeting expected mission demand. Each component of this project (hardware/software) was designed for manufacturing and implementation using industry standard tools, making it ideal for large-scale implementations. This system has been successfully tested with a fleet of UAV systems at Arizona State University, and is currently being integrated into an Arizona smart city environment for deployment.Dissertation/ThesisMasters Thesis Computer Engineering 201

Motion planning and control: a formal methods approach

Control of complex systems satisfying rich temporal specification has become an increasingly important research area in fields such as robotics, control, automotive, and manufacturing. Popular specification languages include temporal logics, such as Linear Temporal Logic (LTL) and Computational Tree Logic (CTL), which extend propositional logic to capture the temporal sequencing of system properties. The focus of this dissertation is on the control of high-dimensional systems and on timed specifications that impose explicit time bounds on the satisfaction of tasks. This work proposes and evaluates methods and algorithms for synthesizing provably correct control policies that deal with the scalability problems. Ideas and tools from formal verification, graph theory, and incremental computing are used to synthesize satisfying control strategies. Finite abstractions of the systems are generated, and then composed with automata encoding the specifications. The first part of this dissertation introduces a sampling-based motion planning algorithm that combines long-term temporal logic goals with short-term reactive requirements. The specification has two parts: (1) a global specification given as an LTL formula over a set of static service requests that occur at the regions of a known environment, and (2) a local specification that requires servicing a set of dynamic requests that can be sensed locally during the execution. The proposed computational framework consists of two main ingredients: (a) an off-line sampling-based algorithm for the construction of a global transition system that contains a path satisfying the LTL formula, and (b) an on-line sampling-based algorithm to generate paths that service the local requests, while making sure that the satisfaction of the global specification is not affected. The second part of the dissertation focuses on stochastic systems with temporal and uncertainty constraints. A specification language called Gaussian Distribution Temporal Logic is introduced as an extension of Boolean logic that incorporates temporal evolution and noise mitigation directly into the task specifications. A sampling-based algorithm to synthesize control policies is presented that generates a transition system in the belief space and uses local feedback controllers to break the curse of history associated with belief space planning. Switching control policies are then computed using a product Markov Decision Process between the transition system and the Rabin automaton encoding the specification.The approach is evaluated in experiments using a camera network and ground robot. The third part of this dissertation focuses on control of multi-vehicle systems with timed specifications and charging constraints. A rich expressivity language called Time Window Temporal Logic (TWTL) that describes time bounded specifications is introduced. The temporal relaxation of TWTL formulae with respect to the deadlines of tasks is also discussed. The key ingredient of the solution is an algorithm to translate a TWTL formula to an annotated finite state automaton that encodes all possible temporal relaxations of the given formula. The annotated automata are composed with transition systems encoding the motion of all vehicles, and with charging models to produce control strategies for all vehicles such that the overall system satisfies the mission specification. The methods are evaluated in simulation and experimental trials with quadrotors and charging stations

Multi-Agent Coverage Control with Energy Depletion and Repletion

We develop a hybrid system model to describe the behavior of multiple agents cooperatively solving an optimal coverage problem under energy depletion and repletion constraints. The model captures the controlled switching of agents between coverage (when energy is depleted) and battery charging (when energy is replenished) modes. It guarantees the feasibility of the coverage problem by defining a guard function on each agent's battery level to prevent it from dying on its way to a charging station. The charging station plays the role of a centralized scheduler to solve the contention problem of agents competing for the only charging resource in the mission space. The optimal coverage problem is transformed into a parametric optimization problem to determine an optimal recharging policy. This problem is solved through the use of Infinitesimal Perturbation Analysis (IPA), with simulation results showing that a full recharging policy is optimal

Двухуровневый эволюционный подход к маршрутизации группы подводных роботов в условиях периодической ротации состава

Применение скоординированных групп автономных подводных роботов представляется наиболее перспективной и многообещающей технологией, обеспечивающей решение самого широкого спектра океанографических задач. Групповое выполнение комплексных широкомасштабных миссий, как правило, связано с длительным пребыванием роботов в заданной акватории, что в условиях ограниченной энергоемкости аккумуляторных батарей возможно только при наличии специализированных док-станций для ее пополнения. С целью обеспечения высокого уровня работоспособности действующей группировки возникают две параллельные задачи: эффективно распределить задания миссии между членами группы и определить порядок подзарядки роботов на длительном промежутке времени. При этом необходимо учитывать, что реальные робототехнические системы функционируют в динамической подводной среде, а значит, могут подвергаться влиянию непредвиденных событий и различного рода неполадок. В данной статье предлагается двухуровневый подход к динамическому планированию групповой стратегии, основанный на декомпозиции миссии на последовательность рабочих периодов с обязательным сбором действующей группировки по окончанию каждого из них. Задача планировщика на верхнем уровне заключается в составлении такого расписания циклов зарядки для всех аппаратов в группе, которое обеспечивало бы своевременное пополнение батарей при недопущении одновременной зарядки большого количества роботов. На основе выбранного расписания осуществляется декомпозиция миссии таким образом, чтобы каждый сбор группы сопровождался либо выходом робота из группы для осуществления подзарядки, либо возвращением в группу уже заряженного аппарата. Такая схема позволяет отслеживать статус группы и осуществлять оперативное перепланирование при изменении ее состава. Маршрутизация группы на каждом рабочем периоде осуществляется низкоуровневым планировщиком, работающим на графе целей и учитывающим технические возможности всех аппаратов в группе, а также все действующие ограничения и требования к выполнению конкретных задач. В статье предлагается эволюционный подход к децентрализованной реализации обоих планировщиков с применением специализированных эвристик, процедур улучшения решений и оригинальных схем кодирования и оценки решений; приводятся результаты вычислительных экспериментов

Двухуровневый эволюционный подход к маршрутизации группы подводных роботов в условиях периодической ротации состава

Currently, the coordinated use of autonomous underwater vehicles groups seems to be the most promising and ambitious technology to provide a solution to the whole range of oceanographic problems. Complex and large-scale underwater operations usually involve long stay activities of robotic groups under the limited vehicle’s battery capacity. In this context, available charging station within the operational area is required for long-term mission implementation. In order to ensure a high level of group performance capability, two following problems have to be handled simultaneously and accurately – to allocate all tasks between vehicles in the group and to determine the recharging order over the extended period of time. While doing this, it should be taken into account, that the real world underwater vehicle systems are partially self-contained and could be subjected to any malfunctions and unforeseen events. The article is devoted to the suggested two-level dynamic mission planner based on the rendezvous point selection scheme. The idea is to divide a mission on a series of time-limited operating periods with the whole group rendezvous at the end of each period. The high-level planner’s objective here is to construct the recharging schedule for all vehicles in the group ensuring well-timed energy replenishment while preventing the simultaneous charging of a plenitude of robots. Based on this schedule, mission is decomposed to assign group rendezvous to each regrouping event (robot leaving the group for recharging or joining the group after recharging). This scheme of periodic rendezvous allows group to keep up its status regularly and to re-plan current strategy, if needed, almost on-the-fly. Low-level planner, in return, performs detailed group routing on the graph-like terrain for each operating period under vehicle’s technical restrictions and task’s spatiotemporal requirements. In this paper, we propose the evolutionary approach to decentralized implementation of both path planners using specialized heuristics, solution improvement techniques, and original chromosome-coding scheme. Both algorithm options for group mission planner are analyzed in the paper; the results of computational experiments are given.Применение скоординированных групп автономных подводных роботов представляется наиболее перспективной и многообещающей технологией, обеспечивающей решение самого широкого спектра океанографических задач. Групповое выполнение комплексных широкомасштабных миссий, как правило, связано с длительным пребыванием роботов в заданной акватории, что в условиях ограниченной энергоемкости аккумуляторных батарей возможно только при наличии специализированных док-станций для ее пополнения. С целью обеспечения высокого уровня работоспособности действующей группировки возникают две параллельные задачи: эффективно распределить задания миссии между членами группы и определить порядок подзарядки роботов на длительном промежутке времени. При этом необходимо учитывать, что реальные робототехнические системы функционируют в динамической подводной среде, а значит, могут подвергаться влиянию непредвиденных событий и различного рода неполадок. В данной статье предлагается двухуровневый подход к динамическому планированию групповой стратегии, основанный на декомпозиции миссии на последовательность рабочих периодов с обязательным сбором действующей группировки по окончанию каждого из них. Задача планировщика на верхнем уровне заключается в составлении такого расписания циклов зарядки для всех аппаратов в группе, которое обеспечивало бы своевременное пополнение батарей при недопущении одновременной зарядки большого количества роботов. На основе выбранного расписания осуществляется декомпозиция миссии таким образом, чтобы каждый сбор группы сопровождался либо выходом робота из группы для осуществления подзарядки, либо возвращением в группу уже заряженного аппарата. Такая схема позволяет отслеживать статус группы и осуществлять оперативное перепланирование при изменении ее состава. Маршрутизация группы на каждом рабочем периоде осуществляется низкоуровневым планировщиком, работающим на графе целей и учитывающим технические возможности всех аппаратов в группе, а также все действующие ограничения и требования к выполнению конкретных задач. В статье предлагается эволюционный подход к децентрализованной реализации обоих планировщиков с применением специализированных эвристик, процедур улучшения решений и оригинальных схем кодирования и оценки решений; приводятся результаты вычислительных экспериментов