Reachable Sets for Safe, Real-Time Manipulator Trajectory Design
Mechanical Engineering, University of Michigan（密歇根大学机械工程学院）；Robotics Institute, University of Michigan（密歇根大学机器人研究所）；Mechanical and Industrial Engineering, Louisiana State University（路易斯安那州立大学机械与工业工程学院）；Naval Architecture and Marine Engineering, University of Michigan（密歇根大学船舶与海洋工程学院）；Ford Motor Company（福特汽车公司）；Office of Naval Research（美国海军研究办公室）
Patrick Holmes, Shreyas Kousik, Bohao Zhang, Daphna Raz, Corina Barbalata, Matthew Johnson-Roberson, Ram Vasudevan
For robotic arms to operate in arbitrary environments, especially near people, it is critical to certify the safety of their motion planning algorithms. However, there is often a trade-off between safety and real-time performance; one can either carefully design safe plans, or rapidly generate potentially-unsafe plans. This work presents a receding-horizon, real-time trajectory planner with safety guarantees, called ARMTD (Autonomous Reachability-based Manipulator Trajectory Design). The method first computes (offline) a reachable set of parameterized trajectories for each joint of an arm. Each trajectory includes a fail-safe maneuver (braking to a stop). At runtime, in each receding-horizon planning iteration, ARMTD constructs a parameterized reachable set of the full arm in workspace and intersects it with obstacles to generate sub-differentiable, provably-conservative collision-avoidance constraints on the trajectory parameters. ARMTD then performs trajectory optimization over the parameters, subject to these constraints. On a 6 degree-of-freedom arm, ARMTD outperforms CHOMP in simulation, never crashes, and completes a variety of real-time planning tasks on hardware.
This paper analyses the workspace of the three-revolute orthogonal manipulators that have at least one of their DH parameters equal to zero. These manipulators are classified into different groups with similar kinematic properties. The classification criteria are based on the topology of the workspace. Each group is evaluated according to interesting kinematic properties such as the size of the workspace subregion reachable with four inverse kinematic solutions, the existence and the size of voids, and the size of the regions of feasible paths in the workspace.
INRIA（法国国家信息与自动化研究所）；Institut de Recherche en Cybernétique de Nantes（南特控制论研究所）
The goal of this paper is to define the n-connected regions in the Cartesian workspace of fully-parallel manipulators, i.e. the maximal regions where it is possible to execute point-to-point motions. The manipulators considered in this study may have multiple direct and inverse kinematic solutions. The N-connected regions are characterized by projection, onto the Cartesian workspace, of the connected components of the reachable configuration space defined in the Cartesian product of the Cartesian space by the joint space. Generalized octree models are used for the construction of all spaces. T…
KTH Royal Institute of Technology（KTH皇家理工学院）；Yale University（耶鲁大学）；Örebro University（厄勒布鲁大学）
We address the problem of motion planning for a robotic manipulator with the task to place a grasped object in a cluttered environment. In this task, we need to locate a collision-free pose for the object that a) facilitates the stable placement of the object, b) is reachable by the robot manipulator and c) optimizes a user-given placement objective. Because of the placement objective, this problem is more challenging than classical motion planning where the target pose is defined from the start. To solve this task, we propose an anytime algorithm that integrates sampling-based motion planning…
Institut de recherche en communications et cybernétique de Nantes（南特通信与控制研究所）；CNRS（法国国家科学研究中心）
This paper proposes a method to calculate the largest Regular Dextrous Workspace (RDW) of some types of three-revolute orthogonal manipulators that have at least one of their DH parameters equal to zero. Then a new performance index based on the RDW is introduced, the isocontours of this index are plotted in the parameter space of the interesting types of manipulators and finally an inspection of the domains of the parameter spaces is conducted in order to identify the better manipulator architectures. The RDW is a part of the workspace whose shape is regular (cube, cylinder) and the performan…
IRCCyN, Institut de Recherche en Communications et Cybernetique de Nantes（南特通信与控制研究所（IRCCyN））；McGill University（麦吉尔大学）
This paper proposes a classification of three-revolute orthogonal manipulators that have at least one of their DH parameters equal to zero. This classification is based on the topology of their workspace. The workspace is characterized in a half-cross section by the singular curves. The workspace topology is defined by the number of cusps and nodes that appear on these singular curves. The manipulators are classified into different types with similar kinematic properties. Each type is evaluated according to interesting kinematic properties such as, whether the workspace is fully reachable with…
IIIT-Hyderabad（海得拉巴国际信息技术学院）；TCS Research（塔塔咨询服务研究院）；University of Michigan（密歇根大学）
This paper proposes a novel controller framework that provides trajectory tracking for an Aerial Manipulator (AM) while ensuring the safe operation of the system under unknown bounded disturbances. The AM considered here is a 2-DOF (degrees-of-freedom) manipulator rigidly attached to a UAV. Our proposed controller structure follows the conventional inner loop PID control for attitude dynamics and an outer loop controller for tracking a reference trajectory. The outer loop control is based on the Model Predictive Control (MPC) with constraints derived using the Barrier Lyapunov Function (BLF) f…
RINGO: Real-time Navigation with a Guiding Trajectory for Aerial Manipulators in Unknown Environments
Institute of Robotics and Automatic Information System（机器人与自动信息系统研究所）；College of Artificial Intelligence（人工智能学院）；Tianjin Key Laboratory of Intelligent Robotics（天津市智能机器人重点实验室）；Nankai University（南开大学）；Institute of Intelligence Technology and Robotic Systems（智能技术与机器人系统研究所）；Shenzhen Research Institute of Nankai University（南开大学深圳研究院）
QuadHand: A Compact Quadrotor Aerial Manipulator with MRC-SDF-Based Whole-Body Motion Planning
Nanyang Technological University（南洋理工大学）；NTU–VinUni Joint Research Laboratory for Embodied AI and Robotics（南洋理工大学-维纳大学具身智能与机器人联合研究实验室）；VinUniversity（维纳大学）
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…
University of Cassino and Southern Lazio（卡西诺和南拉齐奥大学）
In this paper we present a framework that allows the motion control of a robotic arm automatically handling different kinds of safety-related tasks. The developed controller is based on a Task-Priority Inverse Kinematics algorithm that allows the manipulator's motion while respecting constraints defined either in the joint or in the operational space in the form of equality-based or set-based tasks. This gives the possibility to define, among the others, tasks as joint-limits, obstacle avoidance or limiting the workspace in the operational space. Additionally, an algorithm for the real-time co…
Robust Convex Model Predictive Control with collision avoidance guarantees for robot manipulators
ABB robotics（ABB机器人公司）；Department of Mechanical Engineering, Imperial College London（帝国理工学院机械工程系）；Department of Information Technology, Uppsala University（乌普萨拉大学信息科技系）
Fast and Safe Trajectory Optimization for Mobile Manipulators With Neural Configuration Space Distance Field
Robotics and Autonomous Systems Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学机器人与自主系统研究组）；Department of Electrical and Computer Engineering, National University of Singapore（新加坡国立大学电子与计算机工程系）；Idiap Research Institute（Idiap研究所）；Institute of Data Science, The University of Hong Kong（香港大学数据科学研究所）
Evaluating distance to collision for robot manipulators is useful for assessing the feasibility of a robot configuration or for defining safe robot motion in unpredictable environments. However, distance estimation is a timeconsuming operation, and the sensors involved in measuring the distance are always noisy. A challenge thus exists in evaluating the expected distance to collision for safer robot control and planning. In this work, we propose the use of Gaussian process (GP) regression and the forward kinematics (FK) kernel (a similarity function for robot manipulators) to efficiently and a…
Massachusetts Institute of Technology（麻省理工学院）；Ford Motor Company（福特汽车公司）
Density of the reachable states can help understand the risk of safety-critical systems, especially in situations when worst-case reachability is too conservative. Recent work provides a data-driven approach to compute the density distribution of autonomous systems' forward reachable states online. In this paper, we study the use of such approach in combination with model predictive control for verifiable safe path planning under uncertainties. We first use the learned density distribution to compute the risk of collision online. If such risk exceeds the acceptable threshold, our method will p…
A Switching Nonlinear Model Predictive Control Strategy for Safe Collision Handling by an Underwater Vehicle-Manipulator System
D. Guggenheim School of Aerospace Engineering, Georgia Institute of Technology（德·古根海姆航空航天工程学院，佐治亚理工学院）；faculty of Electrical Engineering, Engineering Division, New York University Abu Dhabi（电气工程系，工程分校，纽约大学阿布扎克分校）
A study on a Real-Time VR-Based Teleoperation Framework for Manipulator in Dynamic Environment
Department of Robotics, Hanyang University（韩世大学机器人系）；Department of Smart Construction Engineering, Hanyang University（韩世大学智能建造工程系）；Department of Interdisciplinary Robot Engineering Systems, Hanyang University（韩世大学跨学科机器人工程系统系）；School of Smart Convergence Engineering, Hanyang University, Ansan（韩世大学智能融合工程学院，安山）
Admittance-Based Motion Planning with Vision-Guided Initialization for Robotic Manipulators in Self-Driving Laboratories
Department of Electronics Systems, Aalborg University（电子系统系，奥尔堡大学）；Department of Information Technology and Electrical Engineering, Università degli Studi di Napoli Federico II（信息科技与电气工程系，那不勒斯费迪里奇二世大学）
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；ETH Zürich（苏黎世联邦理工学院）；University of Stuttgart（斯图加特大学）
Fast feedback control and safety guarantees are essential in modern robotics. We present an approach that achieves both by combining novel robust model predictive control (MPC) with function approximation via (deep) neural networks (NNs). The result is a new approach for complex tasks with nonlinear, uncertain, and constrained dynamics as are common in robotics. Specifically, we leverage recent results in MPC research to propose a new robust setpoint tracking MPC algorithm, which achieves reliable and safe tracking of a dynamic setpoint while guaranteeing stability and constraint satisfaction.…
TopAY: Efficient Trajectory Planning for Differential Drive Mobile Manipulators via Topological Paths Search and Arc Length-Yaw Parameterization
State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China（浙江大学工业控制技术状态重点实验室）；Huzhou Institute of Zhejiang University, Huzhou 313000, China（浙江大学湖州研究院）
HEATS: A Hierarchical Framework for Efficient Autonomous Target Search with Mobile Manipulators
School of Mechanical Engineering and Automation, Harbin Institute of Technology Shenzhen（哈尔滨工业大学（深圳）机械工程与自动化学院）；University of Science and Technology of China（中国科学技术大学）
Robust Dynamic Control Barrier Function Based Trajectory Planning for Mobile Manipulator
School of Mechanical Engineering and Automation, Harbin Institute of Technology Shenzhen（哈尔滨工业大学（深圳）机械工程与自动化学院）；Graduated School of Information Science and Technology, Osaka University（大阪大学信息科学与技术研究生院）
Australian Centre for Robotic Vision (ACRV)（澳大利亚机器人视觉中心（ACRV））；Queensland University of Technology (QUT)（昆士兰科技大学（QUT））；University of Adelaide（阿德莱德大学）；Australian National University (ANU)（澳大利亚国立大学（ANU））
Robotic manipulation and grasping in cluttered and unstructured environments is a current challenge for robotics. Enabling robots to operate in these challenging environments have direct applications from automating warehouses to harvesting fruit in agriculture. One of the main challenges associated with these difficult robotic manipulation tasks is the motion planning and control problem for multi-DoF (Degree of Freedom) manipulators. This paper presents the design and performance evaluation of a low-cost Cartesian manipulator, Cartman who took first place in the Amazon Robotics Challenge 201…
Harbin Institute of Technology（哈尔滨工业大学）；University of Maryland at College Park（马里兰大学帕克分校）
We present a novel algorithm for collision-free kinematics of multiple manipulators in a shared workspace with moving obstacles. Our optimization-based approach simultaneously handles collision-free constraints based on reciprocal velocity obstacles and inverse kinematics constraints for high-DOF manipulators. We present an efficient method based on particle swarm optimization that can generate collision-free configurations for each redundant manipulator. Furthermore, our approach can be used to compute safe and oscillation-free trajectories in a few milli-seconds. We highlight the real-time p…
RTD-RAX: Fast, Safe Trajectory Planning for Systems under Unknown Disturbances
Georgia Institute of Technology（佐治亚理工学院）；School of Electrical and Computer Engineering（电子与计算机工程学院）；School of Mechanical Engineering（机械工程学院）；School of Civil and Environmental Engineering（土木与环境工程学院）
We develop a new framework for trajectory planning on predefined paths, for general N-link manipulators. Different from previous approaches generating open-loop minimum time controllers or pre-tuned motion profiles by time-scaling, we establish analytic algorithms that recover all initial conditions that can be driven to the desirable target set while adhering to environment constraints. More technologically relevant, we characterise families of corresponding safe state-feedback controllers with several desirable properties. A key enabler in our framework is the introduction of a state feedbac…
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）
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）
In multirotor systems, guaranteeing safety while considering unknown disturbances is essential for robust trajectory planning. The Forward reachable set (FRS), the set of feasible states subject to bounded disturbances, can be utilized to identify robust and collision-free trajectories by checking the intersections with obstacles. However, in many cases, the FRS is not calculated in real time and is too conservative to be used in actual applications. In this paper, we address these issues by introducing a nonlinear disturbance observer (NDOB) and an adaptive controller to the multirotor system…
Maximal Controlled Invariant-MPC: Enhancing Feasibility and Reducing Conservatism through Terminal CBF Constraint in Safety-Critical Control
Graduate Student, Daniel Guggenheim School of Aerospace Engineering（丹尼尔·古吉恩航空航天工程学院研究生）；Assistant Professor, Director, Aerospace Robotics Lab, Daniel Guggenheim School of Aerospace Engineering（丹尼尔·古吉恩航空航天工程学院助理教授、主任、航空航天机器人实验室）
The University of Tokyo（东京大学）；Matsuo Institute（松尾研究所）
Tidying up a household environment using a mobile manipulator poses various challenges in robotics, such as adaptation to large real-world environmental variations, and safe and robust deployment in the presence of humans.The Partner Robot Challenge in World Robot Challenge (WRC) 2020, a global competition held in September 2021, benchmarked tidying tasks in the real home environments, and importantly, tested for full system performances.For this challenge, we developed an entire household service robot system, which leverages a data-driven approach to adapt to numerous edge cases that occur d…
Safe Obstacle-Free Guidance of Space Manipulators in Debris Removal Missions via Deep Reinforcement Learning
Embodied Learning and Intelligence for eXploration and Innovative soft Robotics (ELIXIR) Lab, Toronto Metropolitan University, Toronto, ON, Canada（体现学习与智能探索与创新软机器人（ELIXIR）实验室，多伦多 Metropolitan 大学，多伦多，ON，加拿大）
