Full-Body Dynamic Safety for Robot Manipulators: 3D Poisson Safety Functions for CBF-Based Safety Filters
Computing and Mathematical Sciences, California Institute of Technology（加州理工学院计算与数学科学系）；Mechanical and Civil Engineering, California Institute of Technology（加州理工学院机械与土木工程系）
Meg Wilkinson, Gilbert Bahati, Ryan M. Bena, Emily Fourney, Joel W. Burdick, Aaron D. Ames
Collision avoidance for robotic manipulators requires enforcing full-body safety constraints in high-dimensional configuration spaces. Control Barrier Function (CBF) based safety filters have proven effective in enabling safe behaviors, but enforcing the high number of constraints needed for safe manipulation leads to theoretic and computational challenges. This work presents a framework for full-body collision avoidance for manipulators in dynamic environments by leveraging 3D Poisson Safety Functions (PSFs). In particular, given environmental occupancy data, we sample the manipulator surface at a prescribed resolution and shrink free space via a Pontryagin difference according to this resolution. On this buffered domain, we synthesize a globally smooth CBF by solving Poisson's equation, yielding a single safety function for the entire environment. This safety function, evaluated at each sampled point, yields task-space CBF constraints enforced by a real-time safety filter via a multi-constraint quadratic program. We prove that keeping the sample points safe in the buffered region guarantees collision avoidance for the entire continuous robot surface. The framework is validated on a 7-degree-of-freedom manipulator in dynamic environments.
A Closed-Form CLF-CBF Controller for Whole-Body Continuum Soft Robot Collision Avoidance
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology（计算机科学与人工智能实验室，麻省理工学院）；Robotics Engineering Department, Worcester Polytechnic Institute（机器人工程系，沃斯堡理工学院）
In this paper we present the implementation of a Control Barrier Function (CBF) using a quadratic program (QP) formulation that provides obstacle avoidance for a robotic manipulator arm system. CBF is a control technique that has emerged and developed over the past decade and has been extensively explored in the literature on its mathematical foundations, proof of set invariance and potential applications for a variety of safety-critical control systems. In this work we will look at the design of CBF for the robotic manipulator obstacle avoidance, discuss the selection of the CBF parameters an…
Safe Expeditious Whole-Body Control of Mobile Manipulators for Collision Avoidance
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；School of Advanced Manufacturing, Sun Yat-Sen University, Sun Yat-Sen University（中山大学先进制造学院）；School of Electrical and Automation Engineering, Hefei University of Technology（合肥工业大学电气与自动化工程学院）
Artificial potential fields (APFs) and their variants have been a staple for collision avoidance of mobile robots and manipulators for almost 40 years. Its model-independent nature, ease of implementation, and real-time performance have played a large role in its continued success over the years. Control barrier functions (CBFs), on the other hand, are a more recent development, commonly used to guarantee safety for nonlinear systems in real-time in the form of a filter on a nominal controller. In this paper, we address the connections between APFs and CBFs. At a theoretic level, we prove that…
California Institute of Technology（加州理工学院）；Miso Robotics（味噌机器人公司）
Recent advances allow for the automation of food preparation in high-throughput environments, yet the successful deployment of these robots requires the planning and execution of quick, robust, and ultimately collision-free behaviors. In this work, we showcase a novel framework for modifying previously generated trajectories of robotic manipulators in highly detailed and dynamic collision environments using Control Barrier Functions (CBFs). This method dynamically re-plans previously validated behaviors in the presence of changing environments -- and does so in a computationally efficient mann…
Gaussian Process Implicit Surfaces as Control Barrier Functions for Safe Robot Navigation
School of Electrical and Computer Engineering, Georgia Institute of Technology（电子与计算机工程学院，佐治亚理工学院）；Department of Electronics and Bioinformatics, School of Science and Technology, Meiji University（电子与生物信息学系，明治大学）
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…
Learning-based Adaptive Safety-Critical Control With Evolving Unsafe Regions
Department of Automation, and the Institute for Embodied Intelligence and Robotics, Tsinghua University（自动化系，以及 embodied intelligence and robotics 研究所，清华大学）；Department of Computer Science, Brunel University London（计算机科学系，布鲁内尔大学伦敦分校）；State Key Laboratory of Multimodal Artificial Intelligence Systems, Beijing Engineering Research Center of Intelligent Systems and Technology, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，北京智能系统与技术工程研究中心，中国科学院自动化研究所）
Safe Navigation under State Uncertainty: Online Adaptation for Robust Control Barrier Functions
Department of Mechanical and Civil Engineering, California Institute of Technology（机械与土木工程系，加州理工学院）；Electrical and Computer Engineering Department, University of California at Los Angeles（电气与计算机工程系，加州大学洛杉矶分校）
Task-Space Singularity Avoidance for Control Affine Systems Using Control Barrier Functions
Department of Mechanical Engineering, University of Maryland, College Park（马里兰大学机械工程系，College Park分校）；Division of Magnetic Manipulation and Particle Research, Weinberg Medical Physics, Inc.（Weinberg医学物理公司磁操控与粒子研究部）；Department of Mechanical Engineering, Johns Hopkins University（约翰霍普金斯大学机械工程系）
D-SafeMPC: Diffusion-Driven Safe Model Predictive Control with Discrete-Time Control Barrier Functions
Paderborn University（帕德博恩大学）；Illinois Institute of Technology（伊利诺伊理工学院）；California Institute of Technology（加州理工学院）；Technical University of Munich（慕尼黑工业大学）
