Over the decades, kinematic controllers have proven to be practically useful for applications like set-point and trajectory tracking in robotic systems. To this end, we formulate a novel safety-critical paradigm for kinematic control in this paper. In particular, we extend the methodology of control barrier functions (CBFs) to kinematic equations governing robotic systems. We demonstrate a purely kinematic implementation of a velocity-based CBF, and subsequently introduce a formulation that guarantees safety at the level of dynamics. This is achieved through a new form CBFs that incorporate kinetic energy with the classical forms, thereby minimizing model dependence and conservativeness. The approach is then extended to underactuated systems. This method and the purely kinematic implementation are demonstrated in simulation on two robotic platforms: a 6-DOF robotic manipulator, and a cart-pole system.
Digital Twin technology is playing a pivotal role in the modern industrial evolution. Especially, with the technological progress in the Internet-of-Things (IoT) and the increasing trend in autonomy, multi-sensor equipped robotics can create practical digital twin, which is particularly useful in the industrial applications for operations, maintenance and safety. Herein, we demonstrate a real-world digital twin of a safety-critical robotics applications with a Franka-Emika-Panda robotic arm. We develop and showcase an edge-assisted collaborative digital twin for dynamic obstacle avoidance whic…
University of Southern California（南加州大学）；University of California, Berkeley（加州大学伯克利分校）
We present a novel method of optimal robust control through quadratic programs that offers tracking stability while subject to input and state-based constraints as well as safety-critical constraints for nonlinear dynamical robotic systems in the presence of model uncertainty. The proposed method formulates robust control Lyapunov and barrier functions to provide guarantees of stability and safety in the presence of model uncertainty. We evaluate our proposed control design on dynamic walking of a five-link planar bipedal robot subject to contact force constraints as well as safety-critical pr…
Safety-aware Goal-oriented Semantic Sensing, Communication, and Control for Robotics
Department of Engineering, King’s College London（工程系，伦敦国王学院）；Department of Intelligent Systems, KTH Royal Institute of Technology（智能系统系，皇家理工学院）；Department of Automation Technology, ABB Corporate Research Sweden（自动化技术系，ABB瑞典公司）；Institute for Digital Communications, FriedrichAlexander-Universität Erlangen-Nürnberg（数字通信研究所，埃尔兰根-纽伦堡弗里德里希-亚历山大大学）
This paper presents a framework for the safety-critical control of robotic systems, when safety is defined on safe regions in the configuration space. To maintain safety, we synthesize a safe velocity based on control barrier function theory without relying on a -- potentially complicated -- high-fidelity dynamical model of the robot. Then, we track the safe velocity with a tracking controller. This culminates in model-free safety critical control. We prove theoretical safety guarantees for the proposed method. Finally, we demonstrate that this approach is application-agnostic. We execute an o…
École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）
Humans coordinate the abundant degrees of freedom (DoFs) of hands to dexterously perform tasks in everyday life. We imitate human strategies to advance the dexterity of multi-DoF robotic hands. Specifically, we enable a robot hand to grasp multiple objects by exploiting its kinematic redundancy, referring to all its controllable DoFs. We propose a human-like grasp synthesis algorithm to generate grasps using pairwise contacts on arbitrary opposing hand surface regions, no longer limited to fingertips or hand inner surface. To model the available space of the hand for grasp, we construct a reac…
University of Wollongong（伍伦贡大学）；Lebanese American University（黎巴嫩美国大学）
This paper proposes a new variable stiffness soft gripper that enables high-performance grasping tasks in industrial applications. The design of the proposed monolithic soft gripper includes a middle bellow and two side bellows (i.e., fingers). The positions of the fingers are regulated by adjusting the negative pressure in the middle bellow actuator via an on-off controller. The stiffness of the soft gripper is modulated by controlling the positive pressure in the fingers through the use of a proportional air-pressure regulator. It is experimentally shown that the proposed soft gripper can mo…
A General Safety Framework for Learning-Based Control in Uncertain Robotic Systems
Department of Mechanical and Process Engineering, ETH Zurich（瑞士苏黎世联邦理工学院机械与过程工程系）；Clearpath Robotics（Clearpath机器人公司）；Electronic Frontier Foundation（电子前沿基金会）
Robust Adaptive Safe Robotic Grasping with Tactile Sensing
Maersk Mc-Kinney Moller Institute, University of Southern Denmark（马士基麦金尼默勒研究所，丹麦南方大学）；Departments of Mechanical and Civil Engineering and Computing and Mathematical Sciences , California Institute of Technology（机械与土木工程系和计算与数学科学系，加州理工学院）
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Beckman Institute for Advanced Science and Technology（贝克曼高等科学技术研究所）；University of Michigan（密歇根大学）
Many soft-body organisms found in nature flourish underwater. Similarly, soft robots are potentially well-suited for underwater environments partly because the problematic effects of gravity, friction, and harmonic oscillations are less severe underwater. However, it remains a challenge to design, fabricate, waterproof, model, and control underwater soft robotic systems. Furthermore, submersible robots usually do not have configurable components because of the need for sealed electronics and mechanical elements. This work presents the development of a modular and submersible soft robotic arm d…
SpineWave: Harnessing Fish Rigid-Flexible Spinal Kinematics for Enhancing Biomimetic Robotic Locomotion
Zhejiang University（浙江大学）；Research Center for Industries of the Future, Westlake University（西湖大学未来产业研究中心）；Key Laboratory of Coastal Environment and Resources of Zhejiang Province, School of Engineering, Westlake University（西湖大学工学院浙江省海岸环境与资源重点实验室）；Institute of Advanced Technology, Westlake Institute for Advanced Study（西湖大学高等研究院先进技术研究所）；Hangzhou Dianzi University（杭州电子科技大学）；China University of Geosciences（中国地质大学）；Massachusetts Institute of Technology（麻省理工学院）
Bayesian Optimization with Safety Constraints: Safe and Automatic Parameter Tuning in Robotics
1 Learning \& Adaptive Systems Group, Department of Computer Science, ETH Zurich, Switzerland. 2 Dynamic Systems Lab, Institute for Aerospace Studies, University of Toronto, Canada.
Safety-Ensured Robotic Control Framework for Cutting Task Automation in Endoscopic Submucosal Dissection
Division of Mechanical System Engineering, Myongji University, Yongin-si, Gyeonggi-do, Republic of Korea（机械系统工程系，明州大学，韩国京畿道 Yongin市）；SDU Robotics, the Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark（SDU机器人，马士克麦金尼莫勒研究所，丹麦南部大学，Odense）；Department of Mechanical Engineering, Myongji University, Yongin-si, Gyeonggi-do, Republic of Korea（机械工程系，明州大学，韩国京畿道 Yongin市）
Task-Space Consensus of Networked Robotic Systems: Separation and Manipulability
Beijing Institute of Control Engineering（北京控制工程研究所）；Science and Technology on Space Intelligent Control Laboratory, Beijing Institute of Control Engineering（空间智能控制重点实验室，北京控制工程研究所）
Shanghai Jiao Tong University（上海交通大学）；University of Oxford（牛津大学）；Tencent Robotics X Lab（腾讯机器人X实验室）
Designing safety-critical control for robotic manipulators is challenging, especially in a cluttered environment. First, the actual trajectory of a manipulator might deviate from the planned one due to the complex collision environments and non-trivial dynamics, leading to collision; Second, the feasible space for the manipulator is hard to obtain since the explicit distance functions between collision meshes are unknown. By analyzing the relationship between the safe set and the controlled invariant set, this paper proposes a data-driven control barrier function (CBF) construction method, whi…
On the Robotic Uncertainty of Fully Autonomous Traffic: From Stochastic Car-Following to Mobility-Safety Trade-Offs
organization= Intelligent Transportation Thrust, Systems Hub, The Hong Kong University of Science；organization= Department of Civil \& Environmental Engineering, University of Wisconsin-Madison , city= Madison , postcode= 53706 , state= Wisconsin , country= United States
A Verification Methodology for Safety Assurance of Robotic Autonomous Systems
Faculty of Science and Technology, Norwegian University Of Life Sciences (NMBU)（科学与技术学院，挪威生命科学大学）；Department of Mechatronics, University of Agder (UiA)（机械电子系，阿格德大学）；Department of Computer Science, University Of York, UK（计算机科学系，约克大学（英国））
Control Barrier Functions (CBF) are widely used to enforce the safety-critical constraints on nonlinear systems. Recently, these functions are being incorporated into a path planning framework to design safety-critical path planners. However, these methods fall short of providing a realistic path considering both the algorithm's run-time complexity and enforcement of the safety-critical constraints. This paper proposes a novel motion planning approach using the well-known Rapidly Exploring Random Trees (RRT) algorithm that enforces both CBF and the robot Kinodynamic constraints to generate a s…
