Towards adaptive multi-robot systems: self-organization and self-adaptation

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible

Applications of Biological Cell Models in Robotics

In this paper I present some of the most representative biological models applied to robotics. In particular, this work represents a survey of some models inspired, or making use of concepts, by gene regulatory networks (GRNs): these networks describe the complex interactions that affect gene expression and, consequently, cell behaviour

Engineering evolutionary control for real-world robotic systems

Evolutionary Robotics (ER) is the field of study concerned with the application of evolutionary computation to the design of robotic systems. Two main issues have prevented ER from being applied to real-world tasks, namely scaling to complex tasks and the transfer of control to real-robot systems. Finding solutions to complex tasks is challenging for evolutionary approaches due to the bootstrap problem and deception. When the task goal is too difficult, the evolutionary process will drift in regions of the search space with equally low levels of performance and therefore fail to bootstrap. Furthermore, the search space tends to get rugged (deceptive) as task complexity increases, which can lead to premature convergence. Another prominent issue in ER is the reality gap. Behavioral control is typically evolved in simulation and then only transferred to the real robotic hardware when a good solution has been found. Since simulation is an abstraction of the real world, the accuracy of the robot model and its interactions with the environment is limited. As a result, control evolved in a simulator tends to display a lower performance in reality than in simulation. In this thesis, we present a hierarchical control synthesis approach that enables the use of ER techniques for complex tasks in real robotic hardware by mitigating the bootstrap problem, deception, and the reality gap. We recursively decompose a task into sub-tasks, and synthesize control for each sub-task. The individual behaviors are then composed hierarchically. The possibility of incrementally transferring control as the controller is composed allows transferability issues to be addressed locally in the controller hierarchy. Our approach features hybridity, allowing different control synthesis techniques to be combined. We demonstrate our approach in a series of tasks that go beyond the complexity of tasks where ER has been successfully applied. We further show that hierarchical control can be applied in single-robot systems and in multirobot systems. Given our long-term goal of enabling the application of ER techniques to real-world tasks, we systematically validate our approach in real robotic hardware. For one of the demonstrations in this thesis, we have designed and built a swarm robotic platform, and we show the first successful transfer of evolved and hierarchical control to a swarm of robots outside of controlled laboratory conditions.A Robótica Evolutiva (RE) é a área de investigação que estuda a aplicação de computação evolutiva na conceção de sistemas robóticos. Dois principais desafios têm impedido a aplicação da RE em tarefas do mundo real: a dificuldade em solucionar tarefas complexas e a transferência de controladores evoluídos para sistemas robóticos reais. Encontrar soluções para tarefas complexas é desafiante para as técnicas evolutivas devido ao bootstrap problem e à deception. Quando o objetivo é demasiado difícil, o processo evolutivo tende a permanecer em regiões do espaço de procura com níveis de desempenho igualmente baixos, e consequentemente não consegue inicializar. Por outro lado, o espaço de procura tende a enrugar à medida que a complexidade da tarefa aumenta, o que pode resultar numa convergência prematura. Outro desafio na RE é a reality gap. O controlo robótico é tipicamente evoluído em simulação, e só é transferido para o sistema robótico real quando uma boa solução tiver sido encontrada. Como a simulação é uma abstração da realidade, a precisão do modelo do robô e das suas interações com o ambiente é limitada, podendo resultar em controladores com um menor desempenho no mundo real. Nesta tese, apresentamos uma abordagem de síntese de controlo hierárquica que permite o uso de técnicas de RE em tarefas complexas com hardware robótico real, mitigando o bootstrap problem, a deception e a reality gap. Decompomos recursivamente uma tarefa em sub-tarefas, e sintetizamos controlo para cada subtarefa. Os comportamentos individuais são então compostos hierarquicamente. A possibilidade de transferir o controlo incrementalmente à medida que o controlador é composto permite que problemas de transferibilidade possam ser endereçados localmente na hierarquia do controlador. A nossa abordagem permite o uso de diferentes técnicas de síntese de controlo, resultando em controladores híbridos. Demonstramos a nossa abordagem em várias tarefas que vão para além da complexidade das tarefas onde a RE foi aplicada. Também mostramos que o controlo hierárquico pode ser aplicado em sistemas de um robô ou sistemas multirobô. Dado o nosso objetivo de longo prazo de permitir o uso de técnicas de RE em tarefas no mundo real, concebemos e desenvolvemos uma plataforma de robótica de enxame, e mostramos a primeira transferência de controlo evoluído e hierárquico para um exame de robôs fora de condições controladas de laboratório.This work has been supported by the Portuguese Foundation for Science and Technology (Fundação para a Ciência e Tecnologia) under the grants SFRH/BD/76438/2011, EXPL/EEI-AUT/0329/2013, and by Instituto de Telecomunicações under the grant UID/EEA/50008/2013

Applying autonomy to distributed satellite systems: Trends, challenges, and future prospects

While monolithic satellite missions still pose significant advantages in terms of accuracy and operations, novel distributed architectures are promising improved flexibility, responsiveness, and adaptability to structural and functional changes. Large satellite swarms, opportunistic satellite networks or heterogeneous constellations hybridizing small-spacecraft nodes with highperformance satellites are becoming feasible and advantageous alternatives requiring the adoption of new operation paradigms that enhance their autonomy. While autonomy is a notion that is gaining acceptance in monolithic satellite missions, it can also be deemed an integral characteristic in Distributed Satellite Systems (DSS). In this context, this paper focuses on the motivations for system-level autonomy in DSS and justifies its need as an enabler of system qualities. Autonomy is also presented as a necessary feature to bring new distributed Earth observation functions (which require coordination and collaboration mechanisms) and to allow for novel structural functions (e.g., opportunistic coalitions, exchange of resources, or in-orbit data services). Mission Planning and Scheduling (MPS) frameworks are then presented as a key component to implement autonomous operations in satellite missions. An exhaustive knowledge classification explores the design aspects of MPS for DSS, and conceptually groups them into: components and organizational paradigms; problem modeling and representation; optimization techniques and metaheuristics; execution and runtime characteristics and the notions of tasks, resources, and constraints. This paper concludes by proposing future strands of work devoted to study the trade-offs of autonomy in large-scale, highly dynamic and heterogeneous networks through frameworks that consider some of the limitations of small spacecraft technologies.Postprint (author's final draft

A new model for solution of complex distributed constrained problems

In this paper we describe an original computational model for solving different types of Distributed Constraint Satisfaction Problems (DCSP). The proposed model is called Controller-Agents for Constraints Solving (CACS). This model is intended to be used which is an emerged field from the integration between two paradigms of different nature: Multi-Agent Systems (MAS) and the Constraint Satisfaction Problem paradigm (CSP) where all constraints are treated in central manner as a black-box. This model allows grouping constraints to form a subset that will be treated together as a local problem inside the controller. Using this model allows also handling non-binary constraints easily and directly so that no translating of constraints into binary ones is needed. This paper presents the implementation outlines of a prototype of DCSP solver, its usage methodology and overview of the CACS application for timetabling problems

협업 로봇을 위한 서비스 기반과 모델 기반의 소프트웨어 개발 방법론

학위논문(박사)--서울대학교 대학원 :공과대학 전기·컴퓨터공학부,2020. 2. 하순회.가까운 미래에는 다양한 로봇이 다양한 분야에서 하나의 임무를 협력하여 수행하는 모습은 흔히 볼 수 있게 될 것이다. 그러나 실제로 이러한 모습이 실현되기에는 두 가지의 어려움이 있다. 먼저 로봇을 운용하기 위한 소프트웨어를 명세하는 기존 연구들은 대부분 개발자가 로봇의 하드웨어와 소프트웨어에 대한 지식을 알고 있는 것을 가정하고 있다. 그래서 로봇이나 컴퓨터에 대한 지식이 없는 사용자들이 여러 대의 로봇이 협력하는 시나리오를 작성하기는 쉽지 않다. 또한, 로봇의 소프트웨어를 개발할 때 로봇의 하드웨어의 특성과 관련이 깊어서, 다양한 로봇의 소프트웨어를 개발하는 것도 간단하지 않다. 본 논문에서는 상위 수준의 미션 명세와 로봇의 행위 프로그래밍으로 나누어 새로운 소프트웨어 개발 프레임워크를 제안한다. 또한, 본 프레임워크는 크기가 작은 로봇부터 계산 능력이 충분한 로봇들이 서로 군집을 이루어 미션을 수행할 수 있도록 지원한다. 본 연구에서는 로봇의 하드웨어나 소프트웨어에 대한 지식이 부족한 사용자도 로봇의 동작을 상위 수준에서 명세할 수 있는 스크립트 언어를 제안한다. 제안하는 언어는 기존의 스크립트 언어에서는 지원하지 않는 네 가지의 기능인 팀의 구성, 각 팀의 서비스 기반 프로그래밍, 동적으로 모드 변경, 다중 작업(멀티 태스킹)을 지원한다. 우선 로봇은 팀으로 그룹 지을 수 있고, 로봇이 수행할 수 있는 기능을 서비스 단위로 추상화하여 새로운 복합 서비스를 명세할 수 있다. 또한 로봇의 멀티 태스킹을 위해 '플랜' 이라는 개념을 도입하였고, 복합 서비스 내에서 이벤트를 발생시켜서 동적으로 모드가 변환할 수 있도록 하였다. 나아가 여러 로봇의 협력이 더욱 견고하고, 유연하고, 확장성을 높이기 위해, 군집 로봇을 운용할 때 로봇이 임무를 수행하는 도중에 문제가 생길 수 있으며, 상황에 따라 로봇을 동적으로 다른 행위를 수행할 수 있다고 가정한다. 이를 위해 동적으로도 팀을 구성할 수 있고, 여러 대의 로봇이 하나의 서비스를 수행하는 그룹 서비스를 지원하고, 일대 다 통신과 같은 새로운 기능을 스크립트 언어에 반영하였다. 따라서 확장된 상위 수준의 스크립트 언어는 비전문가도 다양한 유형의 협력 임무를 쉽게 명세할 수 있다. 로봇의 행위를 프로그래밍하기 위해 다양한 소프트웨어 개발 프레임워크가 연구되고 있다. 특히 재사용성과 확장성을 중점으로 둔 연구들이 최근 많이 사용되고 있지만, 대부분의 이들 연구는 리눅스 운영체제와 같이 많은 하드웨어 자원을 필요로 하는 운영체제를 가정하고 있다. 또한, 프로그램의 분석 및 성능 예측 등을 고려하지 않기 때문에, 자원 제약이 심한 크기가 작은 로봇의 소프트웨어를 개발하기에는 어렵다. 그래서 본 연구에서는 임베디드 소프트웨어를 설계할 때 쓰이는 정형적인 모델을 이용한다. 이 모델은 정적 분석과 성능 예측이 가능하지만, 로봇의 행위를 표현하기에는 제약이 있다. 그래서 본 논문에서 외부의 이벤트에 의해 수행 중간에 행위를 변경하는 로봇을 위해 유한 상태 머신 모델과 데이터 플로우 모델이 결합하여 동적 행위를 명세할 수 있는 확장된 모델을 적용한다. 그리고 딥러닝과 같이 계산량을 많이 필요로 하는 응용을 분석하기 위해, 루프 구조를 명시적으로 표현할 수 있는 모델을 제안한다. 마지막으로 여러 로봇의 협업 운용을 위해 로봇 사이에 공유되는 정보를 나타내기 위해 두 가지 모델을 사용한다. 먼저 중앙에서 공유 정보를 관리하기 위해 라이브러리 태스크라는 특별한 태스크를 통해 공유 정보를 나타낸다. 또한, 로봇이 자신의 정보를 가까운 로봇들과 공유하기 위해 멀티캐스팅을 위한 새로운 포트를 추가한다. 이렇게 확장된 정형적인 모델은 실제 로봇 코드로 자동 생성되어, 소프트웨어 설계 생산성 및 개발 효율성에 이점을 가진다. 비전문가가 명세한 스크립트 언어는 정형적인 태스크 모델로 변환하기 위해 중간 단계인 전략 단계를 추가하였다. 제안하는 방법론의 타당성을 검증하기 위해, 시뮬레이션과 여러 대의 실제 로봇을 이용한 협업하는 시나리오에 대해 실험을 진행하였다.In the near future, it will be common that a variety of robots are cooperating to perform a mission in various fields. There are two software challenges when deploying collaborative robots: how to specify a cooperative mission and how to program each robot to accomplish its mission. In this paper, we propose a novel software development framework that separates mission specification and robot behavior programming, which is called service-oriented and model-based (SeMo) framework. Also, it can support distributed robot systems, swarm robots, and their hybrid. For mission specification, a novel scripting language is proposed with the expression capability. It involves team composition and service-oriented behavior specification of each team, allowing dynamic mode change of operation and multi-tasking. Robots are grouped into teams, and the behavior of each team is defined with a composite service. The internal behavior of a composite service is defined by a sequence of services that the robots will perform. The notion of plan is applied to express multi-tasking. And the robot may have various operating modes, so mode change is triggered by events generated in a composite service. Moreover, to improve the robustness, scalability, and flexibility of robot collaboration, the high-level mission scripting language is extended with new features such as team hierarchy, group service, one-to-many communication. We assume that any robot fails during the execution of scenarios, and the grouping of robots can be made at run-time dynamically. Therefore, the extended mission specification enables a casual user to specify various types of cooperative missions easily. For robot behavior programming, an extended dataflow model is used for task-level behavior specification that does not depend on the robot hardware platform. To specify the dynamic behavior of the robot, we apply an extended task model that supports a hybrid specification of dataflow and finite state machine models. Furthermore, we propose a novel extension to allow the explicit specification of loop structures. This extension helps the compute-intensive application, which contains a lot of loop structures, to specify explicitly and analyze at compile time. Two types of information sharing, global information sharing and local knowledge sharing, are supported for robot collaboration in the dataflow graph. For global information, we use the library task, which supports shared resource management and server-client interaction. On the other hand, to share information locally with near robots, we add another type of port for multicasting and use the knowledge sharing technique. The actual robot code per robot is automatically generated from the associated task graph, which minimizes the human efforts in low-level robot programming and improves the software design productivity significantly. By abstracting the tasks or algorithms as services and adding the strategy description layer in the design flow, the mission specification is refined into task-graph specification automatically. The viability of the proposed methodology is verified with preliminary experiments with three cooperative mission scenarios with heterogeneous robot platforms and robot simulator.Chapter 1. Introduction 1 1.1 Motivation 1 1.2 Contribution 7 1.3 Dissertation Organization 9 Chapter 2. Background and Existing Research 11 2.1 Terminologies 11 2.2 Robot Software Development Frameworks 25 2.3 Parallel Embedded Software Development Framework 31 Chapter 3. Overview of the SeMo Framework 41 3.1 Motivational Examples 45 Chapter 4. Robot Behavior Programming 47 4.1 Related works 48 4.2 Model-based Task Graph Specification for Individual Robots 56 4.3 Model-based Task Graph Specification for Cooperating Robots 70 4.4 Automatic Code Generation 74 4.5 Experiments 78 Chapter 5. High-level Mission Specification 81 5.1 Service-oriented Mission Specification 82 5.2 Strategy Description 93 5.3 Automatic Task Graph Generation 96 5.4 Related works 99 5.5 Experiments 104 Chapter 6. Conclusion 114 6.1 Future Research 116 Bibliography 118 Appendices 133 요약 158Docto

BEHAVIORAL COMPOSITION FOR HETEROGENEOUS SWARMS

Research into swarm robotics has produced a robust library of swarm behaviors that excel at defined tasks such as flocking and area search, many of which have potential for application to a wide range of military problems. However, to be successfully applied to an operational environment, swarms must be flexible enough to achieve a wide array of specific objectives and usable enough to be configured and employed by lay operators. This research explored the use of the Mission-based Architecture for Swarm Composability (MASC) to develop mission-specific tactics as compositions of more general, reusable plays for use with the Advanced Robotic Systems Engineering Laboratory (ARSENL) swarm system. Three tactics were developed to conduct autonomous search of a geographic area and investigation of generated contacts of interest. The tactics were tested in live-flight and virtual environment experiments and compared to a preexisting monolithic behavior implementation completing the same task. Measures of performance were defined and observed that verified the effectiveness of solutions and confirmed the advantages that composition provides with respect to reusability and rapid development of increasingly complex behaviors.Lieutenant Commander, United States NavyApproved for public release. Distribution is unlimited