Approximation algorithms for mobile multi-agent sensing problem

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 산업공학과, 2020. 8. 문일경.Multi-agent systems are generally applicable in a wide diversity of domains, such as robot engineering, computer science, the military, and smart cities. In particular, the mobile multi-agent sensing problem can be defined as a problem of detecting events occurring in a large number of nodes using moving agents. In this thesis, we introduce a mobile multi-agent sensing problem and present a mathematical formulation. The model can be represented as a submodular maximization problem under a partition matroid constraint, which is NP-hard in general. The optimal solution of the model can be considered computationally intractable. Therefore, we propose two approximation algorithms based on the greedy approach, which are global greedy and sequential greedy algorithms, respectively. We present new approximation ratios of the sequential greedy algorithm and prove tightness of the ratios. Moreover, we show that the sequential greedy algorithm is competitive with the global greedy algorithm and has advantages of computation times. Finally, we demonstrate the performances of our results through numerical experiments.다중 에이전트 시스템은 일반적으로 로봇 공학, 컴퓨터 과학, 군사 및 스마트 도시와 같은 다양한 분야에 적용할 수 있다. 특히, 모바일 다중 에이전트 감지 문제는 움직이는 에이전트를 이용해 많은 수의 노드에서 발생하는 이벤트를 감지하는 문제로 정의할 수 있다. 본 논문에서는 모바일 다중 에이전트 감지 문제의 수학적 공식을 제안한다. 이 문제는 일반적으로 NP-난해 문제인 분할 매트로이드 제약 하에서 하위 모듈 함수의 최대화 문제로 표현할 수 있다. 문제의 최적해는 입력 데이터의 크기가 커질수록 보통 합리적인 시간 이내에 계산하기 어렵다. 따라서 본 논문에서는 탐욕적 접근 방식에 기초한 두 가지 근사 알고리즘 (전역 탐욕 알고리즘, 순차 탐욕 알고리즘)을 제안한다. 또한, 순차 탐욕 알고리즘의 새로운 근사 비율을 증명하고 근사 비율에 정확하게 일치하는 인스턴스를 제시한다. 또한, 수치 실험 결과로 순차 탐욕 알고리즘은 효과적인 해를 찾아줄 뿐 아니라, 전역 탐욕 알고리즘과 비교해 계산 시간의 이점을 가지고 있음을 확인한다.Chapter 1 Introduction 1 Chapter 2 Literature Review 4 Chapter 3 Problem statement 7 Chapter 4 Algorithms and approximation ratios 11 Chapter 5 Computational Experiments 22 Chapter 6 Conclusions 30 Bibliography 31 국문초록 40Maste

Coordination of Multirobot Teams and Groups in Constrained Environments: Models, Abstractions, and Control Policies

Robots can augment and even replace humans in dangerous environments, such as search and rescue and reconnaissance missions, yet robots used in these situations are largely tele-operated. In most cases, the robots\u27 performance depends on the operator\u27s ability to control and coordinate the robots, resulting in increased response time and poor situational awareness, and hindering multirobot cooperation. Many factors impede extended autonomy in these situations, including the unique nature of individual tasks, the number of robots needed, the complexity of coordinating heterogeneous robot teams, and the need to operate safely. These factors can be partly addressed by having many inexpensive robots and by control policies that provide guarantees on convergence and safety. In this thesis, we address the problem of synthesizing control policies for navigating teams of robots in constrained environments while providing guarantees on convergence and safety. The approach is as follows. We first model the configuration space of the group (a space in which the robots cannot violate the constraints) as a set of polytopes. For a group with a common goal configuration, we reduce complexity by constructing a configuration space for an abstracted group state. We then construct a discrete representation of the configuration space, on which we search for a path to the goal. Based on this path, we synthesize feedback controllers, decentralized affine controllers for kinematic systems and nonlinear feedback controllers for dynamical systems, on the polytopes, sequentially composing controllers to drive the system to the goal. We demonstrate the use of this method in urban environments and on groups of dynamical systems such as quadrotors. We reduce the complexity of multirobot coordination by using an informed graph search to simultaneously build the configuration space and find a path in its discrete representation to the goal. Furthermore, by using an abstraction on groups of robots we dissociate complexity from the number of robots in the group. Although the controllers are designed for navigation in known environments, they are indeed more versatile, as we demonstrate in a concluding simulation of six robots in a partially unknown environment with evolving communication links, object manipulation, and stigmergic interactions

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The report describes the development of a theoretical framework for coordination and control of combined teams of UAVs and UGVs for coordinated ISR missions. We consider the mission as a composition of an ordered sequence of subtasks, each to be performed by a different team. We design continuous cooperative controllers that enable each team to perform a given subtask and we develop a discrete strategy for interleaving the action of teams on different subtasks. The overall multi-agent coordination architecture is captured by a hybrid automaton, stability is studied using Lyapunov tools, and performance is evaluated through numerical simulations

Negotiation of Target Points for Teams of Heterogeneous Robots: an Application to Exploration

In this paper, we present an application to Search and Rescue of a task negotiation protocol for teams of heterogeneous robots. Self-organization through autonomous negotiations allow the robots to assign themselves a number of target observation points decided by the operator, who is relieved from deciding the optimal assignment. The operator can then focus on monitoring the mission and deciding next actions. The protocol has been tested on both computer simulations and real robots