Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

Consensus reaching in swarms ruled by a hybrid metric-topological distance

Recent empirical observations of three-dimensional bird flocks and human crowds have challenged the long-prevailing assumption that a metric interaction distance rules swarming behaviors. In some cases, individual agents are found to be engaged in local information exchanges with a fixed number of neighbors, i.e. a topological interaction. However, complex system dynamics based on pure metric or pure topological distances both face physical inconsistencies in low and high density situations. Here, we propose a hybrid metric-topological interaction distance overcoming these issues and enabling a real-life implementation in artificial robotic swarms. We use network- and graph-theoretic approaches combined with a dynamical model of locally interacting self-propelled particles to study the consensus reaching pro- cess for a swarm ruled by this hybrid interaction distance. Specifically, we establish exactly the probability of reaching consensus in the absence of noise. In addition, simulations of swarms of self-propelled particles are carried out to assess the influence of the hybrid distance and noise

Computational Models of Consciousness-Emotion Interactions in Social Robotics: Conceptual Framework

There is a little information on how to design a social robot that effectively executes consciousness-emotion (C-E) interaction in a socially acceptable manner. In fact, development of such socially sophisticated interactions depends on models of human high-level cognition implemented in the robot’s design. Therefore, a fundamental research problem of social robotics in terms of effective C-E interaction processing is to define a computational architecture of the robotic system in which the cognitive-emotional integration occurs and determine cognitive mechanisms underlying consciousness along with its subjective aspect in detecting emotions. Our conceptual framework rests upon assumptions of a computational approach to consciousness, which points out that consciousness and its subjective aspect are specific functions of the human brain that can be implemented into an artificial social robot’s construction. Such research framework of developing C-E addresses a field of machine consciousness that indicates important computational correlates of consciousness in such an artificial system and the possibility to objectively describe such mechanisms with quantitative parameters based on signal-detection and threshold theories

Towards a Cyber-Physical Manufacturing Cloud through Operable Digital Twins and Virtual Production Lines

In last decade, the paradigm of Cyber-Physical Systems (CPS) has integrated industrial manufacturing systems with Cloud Computing technologies for Cloud Manufacturing. Up to 2015, there were many CPS-based manufacturing systems that collected real-time machining data to perform remote monitoring, prognostics and health management, and predictive maintenance. However, these CPS-integrated and network ready machines were not directly connected to the elements of Cloud Manufacturing and required human-in-the-loop. Addressing this gap, we introduced a new paradigm of Cyber-Physical Manufacturing Cloud (CPMC) that bridges a gap between physical machines and virtual space in 2017. CPMC virtualizes machine tools in cloud through web services for direct monitoring and operations through Internet. Fundamentally, CPMC differs with contemporary modern manufacturing paradigms. For instance, CPMC virtualizes machining tools in cloud using remote services and establish direct Internet-based communication, which is overlooked in existing Cloud Manufacturing systems. Another contemporary, namely cyber-physical production systems enable networked access to machining tools. Nevertheless, CPMC virtualizes manufacturing resources in cloud and monitor and operate them over the Internet. This dissertation defines the fundamental concepts of CPMC and expands its horizon in different aspects of cloud-based virtual manufacturing such as Digital Twins and Virtual Production Lines. Digital Twin (DT) is another evolving concept since 2002 that creates as-is replicas of machining tools in cyber space. Up to 2018, many researchers proposed state-of-the-art DTs, which only focused on monitoring production lifecycle management through simulations and data driven analytics. But they overlooked executing manufacturing processes through DTs from virtual space. This dissertation identifies that DTs can be made more productive if they engage directly in direct execution of manufacturing operations besides monitoring. Towards this novel approach, this dissertation proposes a new operable DT model of CPMC that inherits the features of direct monitoring and operations from cloud. This research envisages and opens the door for future manufacturing systems where resources are developed as cloud-based DTs for remote and distributed manufacturing. Proposed concepts and visions of DTs have spawned the following fundamental researches. This dissertation proposes a novel concept of DT based Virtual Production Lines (VPL) in CPMC in 2019. It presents a design of a service-oriented architecture of DTs that virtualizes physical manufacturing resources in CPMC. Proposed DT architecture offers a more compact and integral service-oriented virtual representations of manufacturing resources. To re-configure a VPL, one requirement is to establish DT-to-DT collaborations in manufacturing clouds, which replicates to concurrent resource-to-resource collaborations in shop floors. Satisfying the above requirements, this research designs a novel framework to easily re-configure, monitor and operate VPLs using DTs of CPMC. CPMC publishes individual web services for machining tools, which is a traditional approach in the domain of service computing. But this approach overcrowds service registry databases. This dissertation introduces a novel fundamental service publication and discovery approach in 2020, OpenDT, which publishes DTs with collections of services. Experimental results show easier discovery and remote access of DTs while re-configuring VPLs. Proposed researches in this dissertation have received numerous citations both from industry and academia, clearly proving impacts of research contributions

\u3cem\u3eGRASP News\u3c/em\u3e, Volume 8, Number 1

A report of the General Robotics and Active Sensory Perception (GRASP) Laboratory. Edited by Thomas Lindsay

학습 기반 자율시스템의 리스크를 고려하는 분포적 강인 최적화

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2020. 8. 양인순.In this thesis, a risk-aware motion control scheme is considered for autonomous systems to avoid randomly moving obstacles when the true probability distribution of uncertainty is unknown. We propose a novel model predictive control (MPC) method for motion planning and decision-making that systematically adjusts the safety and conservativeness in an environment with randomly moving obstacles. The key component is the Conditional Value-at-Risk (CVaR), employed to limit the safety risk in the MPC problem. Having the empirical distribution obtained using a limited amount of sample data, Sample Average Approximation (SAA) is applied to compute the safety risk. Furthermore, we propose a method, which limits the risk of unsafety even when the true distribution of the obstacles movements deviates, within an ambiguity set, from the empirical one. By choosing the ambiguity set as a statistical ball with its radius measured by the Wasserstein metric, we achieve a probabilistic guarantee of the out-of-sample risk, evaluated using new sample data generated independently of the training data. A set of reformulations are applied on both SAA-based MPC (SAA-MPC) and Wasserstein Distributionally Robust MPC (DR-MPC) to make them tractable. In addition, we combine the DR-MPC method with Gaussian Process (GP) to predict the future motion of the obstacles from past observations of the environment. The performance of the proposed methods is demonstrated and analyzed through simulation studies using a nonlinear vehicle model and a linearized quadrotor model.본 연구에서 자율 시스템이 알려지지 않은 확률 분포로 랜덤하게 움직이는 장애물을 피하기 위한 위험 인식을 고려하는 모션 제어 기법을 개발한다. 따라서 본 논문에서 안전성과 보수성을 체계적으로 조절하는 새로운 Model Predictive Control (MPC) 방법을 제안한다. 본 방벙의 핵심 요소는 MPC 문제의 안전성 리스크를 제한하는 Conditional Value-at-Risk (CVaR)라는 리스크 척도이다. 안전성 리스크를 계산하기 위해 제한된 양의 표본 데이터를 이용하여 얻어진 경험적 분포를 사용하는 Sample Average Approximation (SAA)을 적용한다. 또한, 경험적 분포로부터 실제 분포가 Ambiguity Set라는 집합 내에서 벗어나도 리스크를 제한하는 방법을 제안한다. Ambiguity Set를 Wasserstein 거리로 측정된 반지름을 가진 통계적 공으로 선택함으로써 훈련 데이터와 독립적으로 생성된 새로운 샘플 데이터를 사용하여 평가한 out-of-sample risk에 대한 확률적 보장을 달성한다. 본 논문에서 SAA기반 MPC (SAA-MPC)와 Wasserstein Distributionally Robust MPC (DR-MPC)를 여러 과정을 통하여 다루기 쉬운 프로그램으로 재편성한다. 또한, 환경의 과거 관측으로부터 장애물의 미래 움직임을 예측하기 위해 Distributionally Robust MPC 방법을 Gaussian Process (GP)와 결합한다. 본 연구에서 개발되는 기법들의 성능을 비선형 자동차 모델과 선형화된 쿼드로터 모델을 이용한 시뮬레이션 연구를 통하여 분석한다.1 BACKGROUND AND OBJECTIVES 1 1.1 Motivation and Objectives 1 1.2 Research Contributions 2 1.3 Thesis Organization 3 2 RISK-AWARE MOTION PLANNING AND CONTROL USING CVAR-CONSTRAINED OPTIMIZATION 5 2.1 Introduction 5 2.2 System and Obstacle Models 8 2.3 CVaR-constrained Motion Planning and Control 10 2.3.1 Reference Trajectory Planning 10 2.3.2 Safety Risk 11 2.3.3 Risk-Constrained Model Predictive Control 13 2.3.4 Linearly Constrained Mixed Integer Convex Program 18 2.4 Numerical Experiments 20 2.4.1 Effect of Confidence Level 21 2.4.2 Effect of Sample Size 23 2.5 Conclusions 24 3 WASSERSTEIN DISTRIBUTIONALLY ROBUST MPC 28 3.1 Introduction 28 3.2 System and Obstacle Models 31 3.3 Wasserstein Distributionally Robust MPC 33 3.3.1 Distance to the Safe Region 36 3.3.2 Reformulation of Distributionally Robust Risk Constraint 38 3.3.3 Reformulation of the Wasserstein DR-MPC Problem 43 3.4 Out-of-Sample Performance Guarantee 45 3.5 Numerical Experiments 47 3.5.1 Nonlinear Car-Like Vehicle Model 48 3.5.2 Linearized Quadrotor Model 53 3.6 Conclusions 57 4 LEARNING-BASED DISTRIBUTIONALLY ROBUST MPC 58 4.1 Introduction 58 4.2 Learning the Movement of Obstacles Using Gaussian Processes 60 4.2.1 Obstacle Model 60 4.2.2 Gaussian Process Regression 61 4.2.3 Prediction of the Obstacle's Motion 63 4.3 Gaussian Process based Wasserstein DR-MPC 65 4.4 Numerical Experiments 70 4.5 Conclusions 74 5 CONCLUSIONS AND FUTURE WORK 75 Abstract (In Korean) 87Maste