In this paper, we address efficiently and robustly collecting objects stored in different trays using a mobile manipulator. A resolution complete method, based on precomputed reachability database, is proposed to explore collision-free inverse kinematics (IK) solutions and then a resolution complete set of feasible base positions can be determined. This method approximates a set of representative IK solutions that are especially helpful when solving IK and checking collision are treated separately. For real world applications, we take into account the base positioning uncertainty and plan a sequence of base positions that reduce the number of necessary base movements for collecting the target objects, the base sequence is robust in that the mobile manipulator is able to complete the part-supply task even there is certain deviation from the planned base positions. Our experiments demonstrate both the efficiency compared to regular base sequence and the feasibility in real world applications.
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…
University College London（伦敦大学学院）；University of Ioannina（约阿尼纳大学）
Many robotic path planning problems are continuous, stochastic, and high-dimensional. The ability of a mobile manipulator to coordinate its base and manipulator in order to control its whole-body online is particularly challenging when self and environment collision avoidance is required. Reinforcement Learning techniques have the potential to solve such problems through their ability to generalise over environments. We study joint penalties and joint limits of a state-of-the-art mobile manipulator whole-body controller that uses LIDAR sensing for obstacle collision avoidance. We propose direc…
This work addresses the problem of kinematic trajectory planning for mobile manipulators with non-holonomic constraints, and holonomic operational-space tracking constraints. We obtain whole-body trajectories and time-varying kinematic feedback controllers by solving a Constrained Sequential Linear Quadratic Optimal Control problem. The employed algorithm features high efficiency through a continuous-time formulation that benefits from adaptive step-size integrators and through linear complexity in the number of integration steps. In a first application example, we solve kinematic trajectory p…
University of Modena and Reggio Emilia（摩德纳-雷焦艾米利亚大学）
Human-robot collaborative tasks foresee interactions between humans and robots with various degrees of complexity. Specifically, for tasks which involve physical contact among the agents, challenges arise in the modelling and control of such interaction. In this paper we propose a control architecture capable of ensuring a flexible and robustly stable physical human-robot interaction, focusing on a collaborative transportation task. The architecture is deployed onto a mobile manipulator, modelled as a whole-body structure, which aids the operator during the transportation of an unwieldy load. …
IIIT Hyderabad（国际信息技术研究所海得拉巴分校）；University of Genoa（热那亚大学）；University of Tübingen（图宾根大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；IIT Jodhpur（焦特布尔印度理工学院）
In this paper, a kinematic motion planning algorithm for cooperative spatial payload manipulation is presented. A hierarchical approach is introduced to compute real-time collision-free motion plans for a formation of mobile manipulator robots. Initially, collision-free configurations of a deformable 2-D virtual bounding box are identified, over a planning horizon, to define a convex workspace for the entire system. Then, 3-D payload configurations whose projections lie within the defined convex workspace are computed. Finally, a convex decentralized model-predictive controller is formulated t…
Autonomous mobile manipulation offers a dual advantage of mobility provided by a mobile platform and dexterity afforded by the manipulator. In this paper, we present a whole-body optimal control framework to jointly solve the problems of manipulation, balancing and interaction as one optimization problem for an inherently unstable robot. The optimization is performed using a Model Predictive Control (MPC) approach; the optimal control problem is transcribed at the end-effector space, treating the position and orientation tasks in the MPC planner, and skillfully planning for end-effector contac…
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）
Deep reinforcement learning produces robust locomotion policies for legged robots over challenging terrains. To date, few studies have leveraged model-based methods to combine these locomotion skills with the precise control of manipulators. Here, we incorporate external dynamics plans into learning-based locomotion policies for mobile manipulation. We train the base policy by applying a random wrench sequence on the robot base in simulation and adding the noisified wrench sequence prediction to the policy observations. The policy then learns to counteract the partially-known future disturbanc…
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）
Italian Institute of Technology（意大利技术研究院）；University of Genoa（热那亚大学）
This paper presents a simplified model-based trajectory optimization (TO) formulation for motion planning on quadruped mobile manipulators that carry heavy payload of known mass. The proposed payload-aware formulation simultaneously plans locomotion, payload manipulation and considers both robot and payload model dynamics while remaining computationally efficient. At the presence of heavy payload, the approach exhibits reduced leg outstretching (thus increased manipulability) in kinematically demanding motions due to the contribution of payload manipulation in the optimization. The framework's…
Ecole Centrale de Lille（里尔中央理工学院）；Beihang University（北京航空航天大学）
A high redundant non-holonomic humanoid mobile dual-arm manipulator system is presented in this paper where the motion planning to realize "human-like" autonomous navigation and manipulation tasks is studied. Firstly, an improved MaxiMin NSGA-II algorithm, which optimizes five objective functions to solve the problems of singularity, redundancy, and coupling between mobile base and manipulator simultaneously, is proposed to design the optimal pose to manipulate the target object. Then, in order to link the initial pose and that optimal pose, an off-line motion planning algorithm is designed. I…
The COVID-19 pandemic has demonstrated the need for a more effective and efficient disinfection approach to combat infectious diseases. Ultraviolet germicidal irradiation (UVGI) is a proven mean for disinfection and sterilization and has been integrated into handheld devices and autonomous mobile robots. Existing UVGI robots which are commonly equipped with uncovered lamps that emit intense ultraviolet radiation suffer from: inability to be used in human presence, shadowing of objects, and long disinfection time. These robots also have a high operational cost. This paper introduces a cost-effe…
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）
Keep it Upright: Model Predictive Control for Nonprehensile Object Transportation with Obstacle Avoidance on a Mobile Manipulator
Technical University of Munich（慕尼黑工业大学）；University of Toronto Institute for Aerospace Studies（多伦多大学航空航天研究所）；University of Toronto Robotics Institute（多伦多大学机器人研究所）；Munich Institute of Robotics and Machine Intelligence（慕尼黑机器人与机器智能研究所）；Vector Institute for Artificial Intelligence（矢量人工智能研究所）
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）
Mobile manipulation is usually achieved by sequentially executing base and manipulator movements. This simplification, however, leads to a loss in efficiency and in some cases a reduction of workspace size. Even though different methods have been proposed to solve Whole-Body Control (WBC) online, they are either limited by a kinematic model or do not allow for reactive, online obstacle avoidance. In order to overcome these drawbacks, in this work, we propose an end-to-end Reinforcement Learning (RL) approach to WBC. We compared our learned controller against a state-of-the-art sampling-based m…
Automation applications are pushing the deployment of many high DoF manipulators in warehouse and manufacturing environments. This has motivated many efforts on optimizing manipulation tasks involving a single arm. Coordinating multiple arms for manipulation, however, introduces additional computational challenges arising from the increased DoFs, as well as the combinatorial increase in the available operations that many manipulators can perform, including handoffs between arms. The focus here is on the case of pick-and-place tasks, which require a sequence of handoffs to be executed, so as to…
Motion control of mobile manipulators (a robotic arm mounted on a mobile base) can be challenging for complex tasks such as material and package handling. In this paper, a task-space stabilization controller based on Nonlinear Model Predictive Control (NMPC) is designed and implemented to a 10 Degrees of Freedom (DOF) mobile manipulator which consists of a 7-DOF robotic arm and a 3-DOF mobile base. The system model is based on kinematic models where the end-effector orientation is parameterized directly by a rotation matrix. The state and control constraints as well as singularity constraints …
University of Tartu（塔尔图大学）；Tampere University（坦佩雷大学）；Johannes Kepler University（约翰内斯·开普勒大学）
In this paper, we propose a novel trajectory optimization algorithm for mobile manipulators under end-effector path, collision avoidance and various kinematic constraints. Our key contribution lies in showing how this highly non-linear and non-convex problem can be solved as a sequence of convex unconstrained quadratic programs (QPs). This is achieved by reformulating the non-linear constraints that arise out of manipulator kinematics and its coupling with the mobile base in a multi-affine form. We then use techniques from Alternating Direction Method of Multipliers (ADMM) to formulate and sol…
The aim of this paper is the kinematic study of a 3-RPR planar parallel manipulator where the three fixed revolute joints are actuated. The direct and inverse kinematic problem as well as the singular configuration is characterized. On parallel singular configurations, the motion produce by the mobile platform can be compared to the Reuleaux straight-line mechanism.
Istituto Italiano di Tecnologia（意大利技术研究院）；Università Cattolica del Sacro Cuore（圣心天主教大学）
Mobile manipulators that combine mobility and manipulability, are increasingly being used for various unstructured application scenarios in the field, e.g. vineyards. Therefore, the coordinated motion of the mobile base and manipulator is an essential feature of the overall performance. In this paper, we explore a whole-body motion controller of a robot which is composed of a 2-DoFs non-holonomic wheeled mobile base with a 7-DoFs manipulator (non-holonomic wheeled mobile manipulator, NWMM) This robotic platform is designed to efficiently undertake complex grapevine pruning tasks. In the contro…
From Pixels to Shelf: An Integrated Robotic System for Autonomous Supermarket Stocking with a Mobile Manipulator
Department of Information Engineering, University of Padova（帕多瓦大学信息工程系）；Division of Decision and Control Systems, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology（皇家理工学院电气工程与计算机科学学院决策与控制系统系）；Animum AB（Animum公司）
Motion Planning of Cooperative Nonholonomic Mobile Manipulators
Department of Mechanical Engineering, Indian Institute of Technology Bombay（印度理工学院班加罗尔机械工程系）；Center for Systems and Control, Indian Institute of Technology Bombay（印度理工学院班加罗尔系统控制中心）
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…
This paper describes the vision based robotic picking system that was developed by our team, Team Applied Robotics, for the Amazon Picking Challenge 2016. This competition challenged teams to develop a robotic system that is able to pick a large variety of products from a shelve or a tote. We discuss the design considerations and our strategy, the high resolution 3D vision system, the use of a combination of texture and shape-based object detection algorithms, the robot path planning and object manipulators that were developed.
In this paper, we study the implementation of a model predictive controller (MPC) for the task of object manipulation in a highly uncertain environment (e.g., picking objects from a semi-flexible array of densely packed bins). As a real-time perception-driven feedback controller, MPC is robust to the uncertainties in this environment. However, our experiment shows MPC cannot control a robot to complete a sequence of motions in a heavily occluded environment due to its myopic nature. It will benefit from adding a high-level policy that adaptively adjusts the optimization problem for MPC.
