Beyond Reynolds: A Constraint-Driven Approach to Cluster Flocking

In this paper, we present an original set of flocking rules using an ecologically-inspired paradigm for control of multi-robot systems. We translate these rules into a constraint-driven optimal control problem where the agents minimize energy consumption subject to safety and task constraints. We prove several properties about the feasible space of the optimal control problem and show that velocity consensus is an optimal solution. We also motivate the inclusion of slack variables in constraint-driven problems when the global state is only partially observable by each agent. Finally, we analyze the case where the communication topology is fixed and connected, and prove that our proposed flocking rules achieve velocity consensus.Comment: 6 page

모션 프리머티브를 이용한 복잡한 로봇 임무 학습 및 일반화 기법

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 항공우주공학과, 2020. 8. 김현진.Learning from demonstrations (LfD) is a promising approach that enables robots to perform a specific movement. As robotic manipulations are substituting a variety of tasks, LfD algorithms are widely used and studied for specifying the robot configurations for the various types of movements. This dissertation presents an approach based on parametric dynamic movement primitives (PDMP) as a motion representation algorithm which is one of relevant LfD techniques. Unlike existing motion representation algorithms, this work not only represents a prescribed motion but also computes the new behavior through a generalization of multiple demonstrations in the actual environment. The generalization process uses Gaussian process regression (GPR) by representing the nonlinear relationship between the PDMP parameters that determine motion and the corresponding environmental variables. The proposed algorithm shows that it serves as a powerful optimal and real-time motion planner among the existing planning algorithms when optimal demonstrations are provided as dataset. In this dissertation, the safety of motion is also considered. Here, safety refers to keeping the system away from certain configurations that are unsafe. The safety criterion of the PDMP internal parameters are computed to check the safety. This safety criterion reflects the new behavior computed through the generalization process, as well as the individual motion safety of the demonstration set. The demonstrations causing unsafe movement are identified and removed. Also, the demolished demonstrations are replaced by proven demonstrations upon this criterion. This work also presents an extension approach reducing the number of required demonstrations for the PDMP framework. This approach is effective where a single mission consists of multiple sub-tasks and requires numerous demonstrations in generalizing them. The whole trajectories in provided demonstrations are segmented into multiple sub-tasks representing unit motions. Then, multiple PDMPs are formed independently for correlated-segments. The phase-decision process determines which sub-task and associated PDMPs to be executed online, allowing multiple PDMPs to be autonomously configured within an integrated framework. GPR formulations are applied to obtain execution time and regional goal configuration for each sub-task. Finally, the proposed approach and its extension are validated with the actual experiments of mobile manipulators. The first two scenarios regarding cooperative aerial transportation demonstrate the excellence of the proposed technique in terms of quick computation, generation of efficient movement, and safety assurance. The last scenario deals with two mobile manipulations using ground vehicles and shows the effectiveness of the proposed extension in executing complex missions.시연 학습 기법(Learning from demonstrations, LfD)은 로봇이 특정 동작을 수행할 수 있도록 하는 유망한 동작 생성 기법이다. 로봇 조작기가 인간 사회에서 다양한 업무를 대체해 감에 따라, 다양한 임무를 수행하는 로봇의 동작을 생성하기 위해 LfD 알고리즘들은 널리 연구되고, 사용되고 있다. 본 논문은 LfD 기법 중 모션 프리머티브 기반의 동작 재생성 알고리즘인 Parametric dynamic movement primitives(PDMP)에 기초한 알고리즘을 제시하며, 이를 통해 다양한 임무를 수행하는 모바일 조작기의 궤적을 생성한다. 기존의 동작 재생성 알고리즘과 달리, 이 연구는 제공된 시연에서 표현된 동작을 단순히 재생성하는 것에 그치지 않고, 새로운 환경에 맞게 일반화 하는 과정을 포함한다. 이 논문에서 제시하는 일반화 과정은 PDMPs의 내부 파라미터 값인 스타일 파라미터와 환경 변수 사이의 비선형 관계를 가우스 회귀 기법 (Gaussian process regression, GPR)을 이용하여 수식적으로 표현한다. 제안된 기법은 또한 최적 시연를 학습하는 방식을 통해 강력한 최적 실시간 경로 계획 기법으로도 응용될 수 있다. 본 논문에서는 또한 로봇의 구동 안전성도 고려한다. 기존 연구들에서 다루어진 시연 관리 기술이 로봇의 구동 효율성을 개선하는 방향으로 제시된 것과 달리, 이 연구는 강한 구속조건으로 로봇의 구동 안전성을 확보하는 시연 관리 기술을 통해 안정성을 고려하는 새로운 방식을 제시한다. 제안된 방식은 스타일 파라미터 값 상에서 안전성 기준을 계산하며, 이 안전 기준을 통해 시연을 제거하는 일련의 작업을 수행한다. 또한, 제거된 시위를 안전 기준에 따라 입증된 시위로 대체하여 일반화 성능을 저하시키지 않도록 시위를 관리한다. 이를 통해 다수의 시연 각각 개별 동작 안전성 뿐 아니라 온라인 동작의 안전성까지 고려할 수 있으며, 실시간 로봇 조작기 운용시 안전성이 확보될 수 있다. 제안된 안정성을 고려한 시연 관리 기술은 또한 환경의 정적 설정이 변경되어 모든 시연을 교체해야 할 수 있는 상황에서 사용할 수 있는 시연들을 판별하고, 효율적으로 재사용하는 데 응용할 수 있다. 또한 본 논문은 복잡한 임무에서 적용될 수 있는 PDMPs의 확장 기법인 seg-PDMPs를 제시한다. 이 접근방식은 복잡한 임무가 일반적으로 복수개의 간단한 하위 작업으로 구성된다고 가정한다. 기존 PDMPs와 달리 seg-PDMPs는 전체 궤적을 하위 작업을 나타내는 여러 개의 단위 동작으로 분할하고, 각 단위동작에 대해 여러개의 PDMPs를 구성한다. 각 단위 동작 별로 생성된 PDMPs는 통합된 프레임워크내에서 단계 결정 프로세스를 통해 자동적으로 호출된다. 각 단계 별로 단위 동작을 수행하기 위한 시간 및 하위 목표점은 가우스 공정 회귀(GPR)를 이용한 환경변수와의의 관계식을 통해 얻는다. 결과적으로, 이 연구는 전체적으로 요구되는 시연의 수를 효과적으로 줄일 뿐 아니라, 각 단위동작의 표현 성능을 개선한다. 제안된 알고리즘은 협동 모바일 로봇 조작기 실험을 통하여 검증된다. 세 가지의 시나리오가 본 논문에서 다루어지며, 항공 운송과 관련된 첫 두 가지 시나리오는 PDMPs 기법이 로봇 조작기에서 빠른 적응성, 임무 효율성과 안전성 모두 만족하는 것을 입증한다. 마지막 시나리오는 지상 차량을 이용한 두 개의 로봇 조작기에 대한 실험으로 복잡한 임무 수행을 하기 위해 확장된 기법인 seg-PDMPs가 효과적으로 변화하는 환경에서 일반화된 동작을 생성함을 검증한다.1 Introduction 1 1.1 Motivations 1 1.2 Literature Survey 3 1.2.1 Conventional Motion Planning in Mobile Manipulations 3 1.2.2 Motion Representation Algorithms 5 1.2.3 Safety-guaranteed Motion Representation Algorithms 7 1.3 Research Objectives and Contributions 7 1.3.1 Motion Generalization in Motion Representation Algorithm 9 1.3.2 Motion Generalization with Safety Guarantee 9 1.3.3 Motion Generalization for Complex Missions 10 1.4 Thesis Organization 11 2 Background 12 2.1 DMPs 12 2.2 Mobile Manipulation Systems 13 2.2.1 Single Mobile Manipulation 14 2.2.2 Cooperative Mobile Manipulations 14 2.3 Experimental Setup 17 2.3.1 Test-beds for Aerial Manipulators 17 2.3.2 Test-beds for Robot Manipulators with Ground Vehicles 17 3 Motion Generalization in Motion Representation Algorithm 22 3.1 Parametric Dynamic Movement Primitives 22 3.2 Generalization Process in PDMPs 26 3.2.1 Environmental Parameters 26 3.2.2 Mapping Function 26 3.3 Simulation Results 29 3.3.1 Two-dimensional Hurdling Motion 29 3.3.2 Cooperative Aerial Transportation 30 4 Motion Generalization with Safety Guarantee 36 4.1 Safety Criterion in Style Parameter 36 4.2 Demonstration Management 39 4.3 Simulation Validation 42 4.3.1 Two-dimensional Hurdling Motion 46 4.3.2 Cooperative Aerial Transportation 47 5 Motion Generalization for Complex Missions 51 5.1 Overall Structure of Seg-PDMPs 51 5.2 Motion Segments 53 5.3 Phase-decision Process 54 5.4 Seg-PDMPs for Single Phase 54 5.5 Simulation Results 55 5.5.1 Initial/terminal Offsets 56 5.5.2 Style Generalization 59 5.5.3 Recombination 61 6 Experimental Validation and Results 63 6.1 Cooperative Aerial Transportation 63 6.2 Cooperative Mobile Hang-dry Mission 70 6.2.1 Demonstrations 70 6.2.2 Simulation Validation 72 6.2.3 Experimental Results 78 7 Conclusions 82 Abstract (in Korean) 93Docto

Planning and control for microassembly of structures composed of stress-engineered MEMS microrobots

We present control strategies that implement planar microassembly using groups of stress-engineered MEMS microrobots (MicroStressBots) controlled through a single global control signal. The global control signal couples the motion of the devices, causing the system to be highly underactuated. In order for the robots to assemble into arbitrary planar shapes despite the high degree of underactuation, it is desirable that each robot be independently maneuverable (independently controllable). To achieve independent control, we fabricated robots that behave (move) differently from one another in response to the same global control signal. We harnessed this differentiation to develop assembly control strategies, where the assembly goal is a desired geometric shape that can be obtained by connecting the chassis of individual robots. We derived and experimentally tested assembly plans that command some of the robots to make progress toward the goal, while other robots are constrained to remain in small circular trajectories (orbits) until it is their turn to move into the goal shape. Our control strategies were tested on systems of fabricated MicroStressBots. The robots are 240–280 µm × 60 µm × 7–20 µm in size and move simultaneously within a single operating environment. We demonstrated the feasibility of our control scheme by accurately assembling five different types of planar microstructures

The Problem of Signal and Symbol Integration: A Study of Cooperative Mobile Autonomous Agent Behaviors

This paper explores and reasons about the interplay between symbolic and continuous representations. We first provide some historical perspective on signal and symbol integration as viewed by the Artificial Intelligence (AI), Robotics and Computer Vision communities. The domain of autonomous robotic agents residing in dynamically changing environments anchors well different aspects of this integration and allows us to look at the problem in its entirety. Models of reasoning, sensing and control actions of such agents determine three different dimensions for discretization of the agent-world behavioral state space. The design and modeling of robotic agents, where these three aspects have to be closely tied together, provide a good experimental platform for addressing the signal-to-symbol transformation problem. We present some experimental results from the domain of cooperating mobile agents involved in tasks of navigation and manipulation

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Cybernetic automata: An approach for the realization of economical cognition for multi-robot systems

The multi-agent robotics paradigm has attracted much attention due to the variety of pertinent applications that are well-served by the use of a multiplicity of agents (including space robotics, search and rescue, and mobile sensor networks). The use of this paradigm for most applications, however, demands economical, lightweight agent designs for reasons of longer operational life, lower economic cost, faster and easily-verified designs, etc. An important contributing factor to an agent’s cost is its control architecture. Due to the emergence of novel implementation technologies carrying the promise of economical implementation, we consider the development of a technology-independent specification for computational machinery. To that end, the use of cybernetics toolsets (control and dynamical systems theory) is appropriate, enabling a principled specifi- cation of robotic control architectures in mathematical terms that could be mapped directly to diverse implementation substrates. This dissertation, hence, addresses the problem of developing a technologyindependent specification for lightweight control architectures to enable robotic agents to serve in a multi-agent scheme. We present the principled design of static and dynamical regulators that elicit useful behaviors, and integrate these within an overall architecture for both single and multi-agent control. Since the use of control theory can be limited in unstructured environments, a major focus of the work is on the engineering of emergent behavior. The proposed scheme is highly decentralized, requiring only local sensing and no inter-agent communication. Beyond several simulation-based studies, we provide experimental results for a two-agent system, based on a custom implementation employing field-programmable gate arrays

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