Fast grasping of unknown objects through automatic determination of the required number of mobile robots

In this paper, we present a strategy for fast grasping of unknown objects by mobile robots through automatic determination of the number of robots. An object handling system consisting of a Gripper robot and a Lifter robot is designed. The Gripper robot moves around an unknown object to acquire partial shape information for determination of grasping points. The object is transported if it can be lifted by the Gripper robot. Otherwise, if all grasping trials fail, a Lifter robot is used. In order to maximize use of the Gripper robot’s payload, the detected grasping points that apply the largest force to the gripper are selected for the Gripper robot when the object is grasped by two mobile robots. The object is measured using odometry and scanned data acquired while the Gripper robot moves around the object. Then, the contact point for calculating the insert position for the Lifter robot can be acquired quickly. Finally, a strategy for fast grasping of known objects by considering the transition between stable states is used to realize grasping of unknown objects. The proposed approach is tested in experiments, which find that a wide variety of objects can be grasped quickly with one or two mobile robots.     

A strategy for fast grasping of unknown objects using partial shape information from range sensors

In this paper, we present a strategy for fast grasping of unknown objects based on the partial shape information from range sensors for a mobile robot with a parallel-jaw gripper. The proposed method can realize fast grasping of an unknown object without needing complete information of the object or learning from grasping experience. Information regarding the shape of the object is acquired by a 2D range sensor installed on the robot at an inclined angle to the ground. Features for determining the maximal contact area are extracted directly from the partial shape information of the unknown object to determine the candidate grasping points. Note that since the shape and mass are unknown before grasping, a successful and stable grasp cannot be in fact guaranteed. Thus, after performing a grasping trial, the mobile robot uses the 2D range sensor to judge whether the object can be lifted. If a grasping trial fails, the mobile robot will quickly find other candidate grasping points for another trial until a successful and stable grasp is realized. The proposed approach has been tested in experiments, which found that a mobile robot with a parallel-jaw gripper can successfully grasp a wide variety of objects using the proposed algorithm. The results illustrate the validity of the proposed algorithm in term of the grasping time.     

Object handling control among two-wheel robots and a human operator: An empirical approach

This article presents object handling control between two-wheel robot manipulators, and a two-wheel robot and a human operator. The two-wheel robot has been built for serving humans in the indoor environment. It has two wheels to maintain balance and is able to make contact with a human operator via an object. A position-based impedance force control method is applied to maintain stable object-handling tasks. As the human operator pushes and pulls the object, the robot also reacts to maintain contact with the object by pulling and pushing against the object to regulate a specified force. Master and slave configuration of two-wheel robots is formed for handling an object, where the master robot or a human leads the slave robot equipped with a force sensor. Switching control from position to force or vice versa is presented. Experimental studies are performed to evaluate the feasibility of the object-handling task between two-wheel mobile robots, and the robot and a human operator.     

双臂搬运机器人结构设计与动态仿真分析

针对棒料搬运和无人机翼下弹药挂装,设计了一种基于双六自由度结构的双臂搬运机器人.对机器人总体构型和各驱动回转及末端执行器等重要部分结构加以阐述.双臂机器人由10个驱动转动环节组成,其中腰部和肩部回转为2个共用关节,双臂各有4个回转关节,组成双六自由度机械臂.机器人通过两臂间的协作可以准确地调整搬运对象的位姿,完成复杂的...     

Magnebike: A magnetic wheeled robot with high mobility for inspecting complex‐shaped structures

This paper describes the Magnebike robot, a compact robot with two magnetic wheels in a motorbike arrangement, which is intended for inspecting the inner casing of ferromagnetic pipes with complex‐shaped structures. The locomotion concept is based on an adapted magnetic wheel unit integrating two lateral lever arms. These arms allow for slight lifting off the wheel in order to locally decrease the magnetic attraction force when passing concave edges, as well as laterally stabilizing the wheel unit. The robot has the main advantage of being compact (180 × 130 × 220 mm) and mechanically simple: it features only five active degrees of freedom (two driven wheels each equipped with an active lifter stabilizer and one steering unit). The paper presents in detail design and implementation issues that are specific to magnetic wheeled robots. Low‐level control functionalities are addressed because they are necessary to control the active system. The paper also focuses on characterizing and analyzing the implemented robot. The high mobility is shown through experimental results: the robot not only can climb vertical walls and follow circumferential paths inside pipe structures but it is also able to pass complex combinations of 90‐deg convex and concave ferromagnetic obstacles with almost any inclination regarding gravity. It requires only limited space to maneuver because turning on the spot around the rear wheel is possible. This high mobility enables the robot to access any location in the specified environment. Finally the paper analyzes the maximum payload for different types of environment complexities because this is a key feature for climbing robots and provides a security factor about the risk of falling and slipping. © 2009 Wiley Periodicals, Inc.     

Intelligent robotic gripper with adaptive grasping force

The on-off control robot gripper is widely employed in pick-and-place operations in Cartesian space for handling hard objects between two positions. Without contact force monitoring, it can not be applied in fragile or soft objects handling. Although, an appropriate grasping force or gripper opening for each target could be searched by trial-and-error process, it needs expensive force/torque sensor or an accurate gripper position controller. It has too expensive and complex control strategy disadvantages for most of industrial applications. In addition, it can not overcome the target slip problem due to mass uncertainty and dynamic factor. Here, an intelligent gripper is designed with embedded distributed control structure for overcoming the uncertainty of object’s mass and soft/hard features. A communication signal is specified to integrate both robot arm and gripper control kernels for executing the robotic position control and gripper force control functions in sequence. An efficient model-free intelligent fuzzy sliding mode control strategy is employed to design the position and force controllers of gripper, respectively. Experimental results of pick-and-place soft and hard objects with grasping force auto-tuning and anti-slip control strategy are shown by pictures to verify the dynamic performance of this distributed control system. The position and force tracking errors are less than 1 mm and 0.1 N, respectively.

     

A sensor for dynamic tactile information with applications in human–robot interaction and object exploration

We present a novel tactile sensor, which is applied for dextrous grasping with a simple robot gripper. The hardware novelty consists of an array of capacitive sensors, which couple to the object by means of little brushes of fibers. These sensor elements are very sensitive (with a threshold of about 5 mN) but robust enough not to be damaged during grasping. They yield two types of dynamical tactile information corresponding roughly to two types of tactile sensor in the human skin. The complete sensor consists of a foil-based static force sensor, which yields the total force and the center of the two-dimensional force distribution and is surrounded by an array of the dynamical sensor elements. One such sensor has been mounted on each of the two gripper jaws of our humanoid robot and equipped with the necessary read-out electronics and a CAN bus interface. We describe applications to guiding a robot arm on a desired trajectory with negligible force, reflective grip improvement, and tactile exploration of objects to create a shape representation and find stable grips, which are applied autonomously on the basis of visual recognition.     

A decentralized control system for cooperative transportation by multiple non-holonomic mobile robots

In this paper, we propose a decentralized control system for transporting a single object by multiple non-holonomic mobile robots. Each agent used in the proposed system has two arms, which can steer around a joint offset from the centre point between two driving wheels. One of these mobile robots acts as a leader, who is assumed to be able to plan and to manipulate the omnidirectional motion of the object by using a resolved velocity control. Other robots, referred to as followers, cooperatively transport the object by keeping a constant relative position with the object using a simple PI control. Different from conventional leader–follower type systems that transport an object by multiple robots in coordination, the present followers can plan an action based on their local coordinate and need no absolute positional information. In addition, as a special case, a system consisting of only two robots is introduced, in which the follower robot not only has an arm length controller to follow the leader but also has a fuzzy controller as an avoidance controller to avoid obstacles or a posture controller to keep a desired posture of the object. Simulation results are given to demonstrate the good performance of the proposed systems.     

Solving function distribution and behavior design problem for cooperative object handling by multiple mobile robots

This paper addresses the function distribution and behavior design problem for a multirobot system which incorporates a behavior-based dynamic cooperation strategy for object handling. The proposed multiple robot system is composed of a managing robot and homogeneous behavior-based robots. The cooperation strategy in this system is realized in two steps: designing the distributed robot's cooperative behavioral attributes according to the robot's abilities, and organizing these behavioral attributes so that team cooperation is realized. For indicating an incremental style of local behavior construction, an advanced design of cooperative behavior for coping with unknown disturbance is addressed. Additionally, two extended cooperation strategies designed for a path tracking task are described. These three strategies are based on the same concept on performing manipulation in coordination. Therefore, by considering the function distribution among the managing robot and worker robots, and considering behavior design of each worker robot, the proposed system is able to achieve the object handling task with different performances according to the task requirement, such as with or without path tracking and with or without contact with the environment. Experimental results demonstrate the applicability of the proposed system.     

Team NimbRo at MBZIRC 2017: Fast landing on a moving target and treasure hunting with a team of micro aerial vehicles

The Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017 has defined ambitious new benchmarks to advance the state‐of‐the‐art in autonomous operation of ground‐based and flying robots. This study covers our approaches to solve the two challenges that involved micro aerial vehicles (MAV). Challenge 1 required reliable target perception, fast trajectory planning, and stable control of an MAV to land on a moving vehicle. Challenge 3 demanded a team of MAVs to perform a search and transportation task, coined “Treasure Hunt,” which required mission planning and multirobot coordination as well as adaptive control to account for the additional object weight. We describe our base MAV setup and the challenge‐specific extensions, cover the camera‐based perception, explain control and trajectory‐planning in detail, and elaborate on mission planning and team coordination. We evaluated our systems in simulation as well as with real‐robot experiments during the competition in Abu Dhabi. With our system, we—as part of the larger team NimbRo—won the MBZIRC Grand Challenge and achieved a third place in both subchallenges involving flying robots.     

Position-Based Fuzzy Force Control for Dual Industrial Robots

In this paper, a fuzzy force control framework is proposed for dual-industrial robot systems. The master/slave control method is used in dual-robot systems. Two MITSUBISHI MELFA RV-M1 industrial robots, one is equipped with an BL Force/Torque sensor and the other is not, are utilized for implementing the dual-arm system. In order to adapt various stiffness of the holding object, an adaptable fuzzy force control scheme has been proposed to improve the performance. The ability of the adaptable force control system is achieved by tuning the scaling factor of the fuzzy logic controller. Successful experiments are carried out for the dual-robot system handling an object.     

Development of a search and rescue robot system for the underground building environment

The underground building environment plays an increasingly important role in the construction of modern cities. To deal with possible fires, collapses, and so on, in underground building space, it is a general trend to use rescue robots to replace humans. This paper proposes a dual-robot system solution for search and rescue in an underground building environment. To speed up rescue and search, the two robots focus on different tasks. However, the environmental perception information and location of them are shared. The primary robot is used to quickly explore the environment in a wide range, identify objects, cross difficult obstacles, and so on. The secondary robot is responsible for grabbing, carrying items, clearing obstacles, and so on. In response to the difficulty of rescue caused by unknown scenes, the Lidar, inertial measurement unit and multiview cameras are integrated for large-scale 3D environment mapping. The depth camera detects the objects to be rescued and locate them on the map. A six-degree-of-freedom manipulator with a two-finger gripper is equipped to open doors and clear roadblocks during the rescue. To solve the problem of severe signal attenuation caused by reinforced concrete walls, corners and long-distance transmission, a wireless multinode networking solution is adopted. In the case of a weak wireless signal, the primary robot uses autonomous exploration for environmental perception. Experimental results show the robots' system has high reliability in over-the-horizon maneuvering, teleoperation of the door opening and grasping, object searching, and environmental perception, and can be well applied to underground search and rescue.     

Neural Network Force Control for Industrial Robots

In this paper, we present a hierarchical force control framework consisting of a high level control system based on neural network and the existing motion control system of a manipulator in the low level. Inputs of the neural network are the contact force error and estimated stiffness of the contacted environment. The output of the neural network is the position command for the position controller of industrial robots. A MITSUBISHI MELFA RV-M1 industrial robot equipped with a BL Force/Torque sensor is utilized for implementing the hierarchical neural network force control system. Successful experiments for various contact motions are carried out. Additionally, the proposed neural network force controller together with the master/slave control method are used in dual-industrial robot systems. Successful experiments are carried out for the dual-robot system handling an object.     

Virtual Force/Tactile Sensors for Interactive Machines Using the User's Biological Signals

This study proposes a new approach to virtual realization of force/tactile sensors in machines equipped with no real sensors. The key of our approach is that a machine exploits the user's biological signals. Therefore, this approach is not dependent on controlled objects and is expected to be widely applicable for a variety of machines including robots. This article describes an example robotic system comprised of an industrial robot manipulator, a motion capture system and a surface electromyogram (EMG) measurement apparatus. By monitoring/recording the user's surface EMG and postural information in real-time, we show that a robot equipped with no force/tactile sensors behaved similarly to one possessing sensors over its body. Another advantage of our approach is demonstrated by a task in which a robot and a user cooperatively hold and move a heavy load.     

Gradual spatial pattern formation of homogeneous robot group

This paper proposes a gradual formation of a spatial pattern for a homogeneous robot group. The autonomous formation of spatial pattern is one of key technologies for the advancement of cooperative robotic systems because a pattern formation can be regarded as function differentiation of a multi-agent system. When multiple autonomous robots without a given local task cooperatively work for a global objective, the function differentiation is the first and indispensable step. For example, each member of cooperative insects or animals can autonomously recognize own local tasks through mutual communication with local members. There were a lot of papers that reported a spatial pattern formation of multiple robots, but the global information was supposed to be available in their approaches. It is however almost impractical assumption for a small robot to be equipped with an advanced sensing system for global localization due to robot’s scale and sensor size. The local information-based algorithm for the pattern formation is desired even if each robot is not equipped with a global localization sensor.We therefore propose a gradual pattern formation algorithm, i.e., a group of robots improves complexity of their pattern from to a simple pattern to a goal pattern like a polygon. In the algorithm, the Turing diffusion-driven instability theory is used so that it could differentiate roles of each robot in a group based only on local information. In experiment, we demonstrate that robots can make a few polygon patterns from a circle pattern by periodically differentiating robot’s roles into a vertex or a side. We show utilities of the proposed gradual pattern formation algorithm for multiple autonomous robots based on local information through some experiments.     

An algorithm for safe navigation of mobile robots by a sensor network in dynamic cluttered industrial environments

Mobile robots have been widely implemented in industrial automation and smart factories. Different types of mobile robots work cooperatively in the workspace to complete some complicated tasks. Therefore, the main requirement for multi-robot systems is collision-free navigation in dynamic environments. In this paper, we propose a sensor network based navigation system for ground mobile robots in dynamic industrial cluttered environments. A range finder sensor network is deployed on factory floor to detect any obstacles in the field of view and perform a global navigation for any robots simultaneously travelling in the factory. The obstacle detection and robot navigation are integrated into the sensor network and the robot is only required for a low-level path tracker. The novelty of this paper is to propose a sensor network based navigation system with a novel artificial potential field (APF) based navigation algorithm. Computer simulations and experiments confirm the performance of the proposed method.     

Design and experiments on a free-floating planar robot for optimal chase and capture operations in space

This paper describes an autonomous free-floating robot system that was designed to investigate the behavior of free-floating robots that are involved in the capture of satellites in space. The robot is used as a test bed for algorithms that have been developed for efficient and autonomous capture of objects in space. The robot is a completely autonomous system running under a real-time operating system. It is equipped with two three-degree-of-freedom arms, a three-axis thruster system and a fast communications module. The robot works in conjunction with a host computer. The host computer is used to process the capture algorithms and the robot implements the results in real time. The entire system provides a test bed for algorithms developed for optimal capture of objects in space.     

Optimal Capture of a Tumbling Object in Orbit Using a Space Manipulator

This paper introduces an optimal capture strategy for a manipulator based on a servicing spacecraft to approach an arbitrarily rotating object, such as a malfunctioning satellite or a piece of orbital debris, for capturing with minimal impact to the robot’s base spacecraft. The method consists of two steps. The first step is to determine an optimal future time and the target object’s corresponding motion state for the robot to capture the tumbling object, so that, at the time when the gripper of the robot intercepts the target the very first instant, the resulting impact or disturbance to the attitude of the base spacecraft will be minimal. The second step is to control the robot to reach the tumbling object at the predicted optimal time along an optimal trajectory. The optimal control problem is solved with random uncertainties in the initial and final boundary conditions. Uncertainties are introduced because sensor and estimation errors inevitably exist in the first step, i.e., determination process of the initial and final boundary conditions. The application of the method is demonstrated using a dynamics simulation example.     

Two strategies of position and force control for two industrial robots handling a single object

The simultaneous position and force control problem for two industrial robots in handling a single object is studied in this paper. Two strategies, position-position control and position-force control, are first proposed. Stability analysis is then conducted for the strategies. Both strategies result in a stable system for simple robotic models. Steady state error is also studied for the strategies. Finally, experimental results are presented to evaluate the two strategies.     

基于3D目标跟踪算法的机器人手眼协调研究

为了适应环境的复杂性和多样性,增强机器人抓取任务的鲁棒性,本文从3D目标跟踪算法出发,提出了一种实现机器人手眼协调的新方法。该方法采用改进的基于区域的位姿追踪算法同时跟踪机械臂夹持器和目标物体的位姿,根据二者的相对位置关系引导机械臂运动。对基于区域的位姿跟踪算法,本文提出根据局部区域分割线构建分割模型并改进模型颜色似然的线性更新方式,使得算法能够准确跟踪机械臂夹持器与目标物体。基于ROS平台搭建了一套仿真实验环境,并分别在仿真环境和真实环境下验证了此手眼协调系统的有效性和鲁棒性。这种方式不仅不需要手眼标定,更接近于人类“Sensor-Actor”带反馈的闭环控制方式,同时赋予了机器人足够的灵活性来应对弹性的任务和多变的环境。