Robot Workspace Research Articles

Genetic algorithm-based compliant robot path planning: an improved Bi-RRT-based initialization method

PurposeThe purpose of this paper is to improve the performance of the genetic algorithm-based compliant robot path planning (GACRPP) in complex dynamic environment by proposing an improved bidirectional rapidly exploring random tree (Bi-RRT)-based population initialization method.Design/methodology/approachTo achieve GACRPP in complex dynamic environment with high performance, an improved Bi-RRT-based population initialization method is proposed. First, the grid model is adopted to preprocess the working space of mobile robot. Second, an improved Bi-RRT is proposed to create multi-cluster connections between the starting point and the goal point. Third, the backtracking method is used to generate the initial population based on the multi-cluster connections generated by the improved Bi-RRT. Subsequently, some comparative experiments are implemented where the performances of the improved Bi-RRT-based population initialization method are compared with other population initialization methods, and the comparison results of the improved genetic algorithm (IGA) combining with the different population initialization methods are shown. Finally, the optimal path is further smoothed with the help of the technique of quadratic B-spline curves.FindingsIt is shown in the experiment results that the improved Bi-RRT-based population initialization method is capable of deriving a more diversified initial population with less execution time and the IGA combining with the proposed population initialization method outperforms the one with other population initialization methods in terms of the length of optimal path and the execution time.Originality/valueIn this paper, the Bi-RRT is introduced as a population initialization method into the GACRPP problem. An improved Bi-RRT is proposed for the purpose of increasing the diversity of initial population. To characterize the diversity of initial population, a new notion of breadth is defined in terms of Hausdorff distance between different paths.

Finding sets of solutions to systems of nonlinear inequalities

The problem of approximating the set of all solutions to a system of nonlinear inequalities is studied. A method based on the concept of nonuniform coverings is proposed. It allows one to obtain an interior and exterior approximation of this set with a prescribed accuracy. The efficiency of the method is demonstrated by determining the workspace of a parallel robot.

An improved Monte Carlo method based on Gaussian growth to calculate the workspace of robots

This paper presents a new Monte Carlo method to calculate the workspace of robot manipulators, which we called the Gaussian Growth method. In contrast to classical brute-force Monte Carlo methods, which rely on increasing the number of randomly generated points in the whole workspace to attain higher accuracy, the Gaussian Growth method focuses on populating and improving the precision of poorly defined regions of the workspace. For this purpose, the proposed method first generates an inaccurate seed workspace using a classical Monte Carlo method, and then it uses the Gaussian distribution to densify and grow this seed workspace until the boundaries of the workspace are attained. The proposed method is compared with previous Monte Carlo methods using a 10-degrees-of-freedom robot as a case study, and it is demonstrated that the Gaussian Growth method can generate more accurate workspaces than previous methods requiring the same or less computation time.

Unsupervised early prediction of human reaching for human–robot collaboration in shared workspaces

This paper focuses on human–robot collaboration in industrial manipulation tasks that take place in a shared workspace. In this setting we wish to predict, as quickly as possible, the human’s reaching motion so that the robot can avoid interference while performing a complimentary task. Given an observed part of a human’s reaching motion, we thus wish to predict the remainder of the trajectory, and demonstrate that this is effective as a real-time input to the robot for human–robot collaboration tasks. We propose a two-layer framework of Gaussian Mixture Models and an unsupervised online learning algorithm that updates these models with newly-observed trajectories. Unlike previous work in this area which relies on supervised learning methods to build models of human motion, our approach requires no offline training or manual labeling. The main advantage of this unsupervised approach is that it can build models on-the-fly and adapt to new people and new motion styles as they emerge. We test our method on motion capture data from a human-human collaboration experiment to show the early prediction performance. We also present two human–robot workspace sharing experiments of varying difficulty where the robot predicts the human’s motion every 0.1 s. The experimental results suggest that our framework can use human motion predictions to decide on robot motions that avoid the human in real-time applications with high reliability.

Robotic teleoperation systems using a wearable multimodal fusion device

Teleoperation is of great importance in the area of robotics especially when people’s presence at the robot working space is unavailable. It provides an alternative to employ human intelligence in the control of the robot remotely. We establish robotic teleoperation systems with a wearable multimodal fusion device. The device is integrated with 18 low-cost inertial and magnetic measurement units, which cover all segments of the arm and hand. The multimodal fusion algorithm based on extended Kalman filter is deduced to determine the orientations and positions of each segment. Then, the robotic teleoperation systems using the proposed device are designed. The novel teleoperation schemes can be applied for 11DOF robotic arm–hand system and 10DOF robotic arm–hand system, in which the operator’s fingers are used for robotic hand teleoperation, and the arms with palm are used for robotic arm teleoperation. Meanwhile, the proposed robotic teleoperation systems are fully realized with a user-friendly human–machine interaction interface. Finally, a series of experiments are conducted with our robotic teleoperation system successfully.

Reconfigurable workspace and torque capacity of a compliant ankle rehabilitation robot (CARR)

Abstract The novelty of this paper is the adjustable workspace and torque capacity of a compliant ankle rehabilitation robot (CARR). The robot has three rotational degrees of freedom (DOFs) redundantly actuated by four compliant actuators. It suffers from conflicting workspace and actuation torque due to the use of a parallel mechanism and compliant actuators. To address this issue, also considering physical constraints imposed by human users, the CARR is designed with reconfigurability to make a trade-off between workspace and torque capacity for meeting different training requirements. Theoretical analysis indicates that varying kinematic and dynamic performance of the robot can be achieved by reconfiguring the layout of the actuators. Experiments with/without load also demonstrate the validity of the reconfigurable robotic design for practical applications.

Identification of the Workspace of a Hexapod Mobile Robot Using Multobjective Optimization

In this paper, an innovative method to findthe workspace of a hexapod mobile robot is presented.Differently, to the current state of the art methods thatallows to determine the working spaces for walking instraight line, the proposed method allows estimatingthe optimal set of configurations for walking in anyviable direction. The method takes advantage of theexisting similitudes between the hexapod and theDelta robot during the tripod walk; however, thereare some movement restrictions between them, whichwere conveniently solved by handling them as amultiobjective optimization problem. Particularly, theMOEA/D algorithm was used to solve the problem.

Solving Method of Workspace for 6 DOF Robot Based on Vision

In order to get the solution of the robot workspace, a method that based on the SimMechanics model in Matlab and the Monte-Carlo method was adopted to solve the robot workspace．Matlab/Simulink software was used to realize movement simulation of robot arms and got a compact workspace. Compared to traditional Monte-Carlo method, this method had the advantages of better speed in solving workspace，strong visibility and avoiding complicated mathematical operations, which provided an important basis for robot's obstacle avoidance planning.

Intelligent Design and Algorithms to Control a Stereoscopic Camera on a Robotic Workspace

Intelligent Design and Algorithms to Control a Stereoscopic Camera on a Robotic Workspace

Mobile Mixed-Reality Interfaces That Enhance Human–Robot Interaction in Shared Spaces

Although user interfaces with gesture-based input and augmented graphics have promoted intuitive human-robot interactions (HRI), they are often implemented in remote applications on research-grade platforms requiring significant training and limiting operator mobility. This paper proposes a mobile mixed-reality interface approach to enhance HRI in shared spaces. As a user points a mobile device at the robot's workspace, a mixed-reality environment is rendered providing a common frame of reference for the user and robot to effectively communicate spatial information for performing object manipulation tasks, improving the user's situational awareness while interacting with augmented graphics to intuitively command the robot. An evaluation with participants is conducted to examine task performance and user experience associated with the proposed interface strategy in comparison to conventional approaches that utilize egocentric or exocentric views from cameras mounted on the robot or in the environment, respectively. Results indicate that, despite the suitability of the conventional approaches in remote applications, the proposed interface approach provides comparable task performance and user experiences in shared spaces without the need to install operator stations or vision systems on or around the robot. Moreover, the proposed interface approach provides users the flexibility to direct robots from their own visual perspective (at the expense of some physical workload) and leverages the sensing capabilities of the tablet to expand the robot's perceptual range.

Multimodal sensor-based whole-body control for human–robot collaboration in industrial settings

This paper describes the development of a dual-arm robotic system for industrial human–robot collaboration. The robot demonstrator described here possesses multiple sensor modalities for the monitoring of the shared human–robot workspace and is equipped with the ability for real-time collision-free dual-arm manipulation. A whole-body control framework is used as a key control element which generates a coherent output signal for the robot’s joints given the multiple controller inputs, tasks’ priorities, physical constraints, and current situation. Furthermore, sets of controller-constraints combinations of the whole-body controller constitute the basic building blocks that describe actions of a high-level action plan to be sequentially executed. In addition, the robotic system can be controlled in an intuitive manner via human gestures. These individual robotic capabilities are combined into an industrial demonstrator which is validated in a gearbox assembly station of a Volkswagen factory.

Human-robot collaboration while sharing production activities in dynamic environment: SPADER system

Interactive robot doing collaborative work in hybrid work cell need adaptive trajectory planning strategy. Indeed, systems must be able to generate their own trajectories without colliding with dynamic obstacles like humans and assembly components moving inside the robot workspace. The aim of this paper is to improve collision-free motion planning in dynamic environment in order to insure human safety during collaborative tasks such as sharing production activities between human and robot. Our system proposes a trajectory generating method for an industrial manipulator in a shared workspace. A neural network using a supervised learning is applied to create the waypoints required for dynamic obstacles avoidance. These points are linked with a quintic polynomial function for smooth motion which is optimized using least-square to compute an optimal trajectory. Moreover, the evaluation of human motion forms has been taken into consideration in the proposed strategy. According to the results, the proposed approach is an effective solution for trajectories generation in a dynamic environment like a hybrid workspace.

Workspace Analysis of Tendon-Driven Continuum Robots Based on Mechanical Interference Identification

Continuum robots have excited increasing attention and efforts from the robotic community due to their high dexterity and safety. This paper proposes a design for a type of multimodule continuum robot equipped with an elastic backbone structure and tendon-driven actuation system. The kinematic model of the robot is formulated where the maximum bending angle of a module is obtained by identifying the interference between the backbone structure and the tendons. A superposition method is then used to determine the configuration space of the robotic module. Finally, an approximation method is presented to estimate the workspace of the tendon-driven continuum robot that reduces the computational complexity in comparison with the previously used scanning method. Experiments are provided to validate the proposed methods.

Direct Optimal Motion Planning for Omni-directional Mobile Robots under Limitation on Velocity and Acceleration

This paper describes a low computational direct approach for optimal motion planning and obstacle avoidance of Omni-directional mobile robots within velocity and acceleration constraints on the robot motion. The main purpose of this problem is the minimization of a quadratic cost function while limitation on velocity and acceleration of robot is considered and collision with any obstacle in the robot workspace is avoided. This problem can be formulated as a constraint nonlinear optimal control problem. To solve this problem, a direct method is utilized which employs polynomials functions for parameterization of trajectories. By this transforming, the main optimal control problem can be rewritten as a nonlinear programming problem (NLP) with lower complexity. To solve the resulted NLP and obtain optimal trajectories, a new approach with very small run time is used. Finally, the performance and effectiveness of the proposed method are tested in simulations and some performance indexes are computed for better assessment. Furthermore, a comparison between proposed method and another direct method is done to verify the low computational cost and better performance of the proposed method.

Optimal capture occasion determination and trajectory generation for space robots grasping tumbling objects

This paper presents an optimal trajectory planning scheme for robotic capturing of a tumbling object. Motion planning of a space robot is much more complex than that of a fixed-based robot, due to the dynamic coupling between the manipulator and its base. In this work, the Path Independent Workspace (PIW), in which no dynamic singularity occurs, and Path Dependent Workspace (PDW) of the space robot are first calculated by the proposed algorithm. The motion equations of the tumbling object are formulated based on the Euler dynamics equations and the quaternion, which are used to predict the long-term motion of a grasping point on the tumbling object. Subsequently, the obtained PIW workspace and predicted motion trajectories are used to plan the trajectory of the end-effector to intercept the grasping point with zero relative velocity (to avoid impact) in an optimal way. In order to avoid dynamic singularity occurring at the capture moment, the optimal capture occasion is first determined by three proposed criterions guaranteeing the capture can be safely, reliably and rapidly performed, then the optimal trajectory of the end-effector is generated minimizing a cost function which acts as a constraint on acceleration magnitude. Simulations are presented to demonstrate the trajectory planning scheme for a space robot with a 3-degree of freedom (DOF) manipulator grasping a tumbling satellite, the results show that the manipulator end-effector can smoothly intercept the grasping point on the tumbling satellite with zero relative velocity.

Which impedance strategy is the most effective for cooperative object manipulation?

PurposeThe purpose of this paper is to present a comparison study of cooperative object manipulation control algorithms. To this end, a full comprehensive survey of the existing control algorithms in this field is presented.Design/methodology/approachCooperative manipulation occurs when manipulators are mechanically coupled to the object being manipulated, and the manipulators may not be treated as an isolated system. The most important and basic impedance control (IC) strategies for an assumed cooperative object manipulation task are the Augmented Object Model (AOM) control and the multiple impedance control (MIC) which are found based on the IC, where the former is designed based on the object movement, and the latter is designed based on the whole robot movement. Thus, the basis of these two algorithms are fully studied.FindingsThe results are fully analyzed, and it is practically verified that the MIC algorithm has the better performance. In fact, the results reveal that the MIC system could successfully perform the object manipulation task, as opposed to the AOM controller: for the same controller gains, the MIC strategy showed better performance than the AOM strategy. This means that because there is no control on the robot base with the AOM algorithm, the object manipulation task cannot be satisfactorily performed whenever the desired path is not within the robot work space. On the other hand, with the MIC algorithm, satisfactory object manipulation is achieved for a mobile robotic system in which the robot base, the manipulator endpoints and the manipulated object shall be moved.Practical implicationsA simple conceptual model for cooperative object manipulation is considered, and a suitable setup is designed for practical implementation of the two ICs.Originality/valueThe basis of these two aspects or these two algorithms is fully studied and compared which is the foundation of this paper. For this purpose, a case study is considered, in which a space free-flying robotic system, which contains two 2-degrees of freedom planar cooperative manipulators, is simulated to manipulate an object using the above control strategies. The system also includes a rotating antenna and camera as its third and fourth arm. Finally, a simple conceptual model for cooperative object manipulation is considered, and a suitable setup is designed for practical implementation of the two ICs.

Posture optimization methodology of 6R industrial robots for machining using performance evaluation indexes

Abstract For industrial robots, the relatively low posture-dependent stiffness deteriorates the absolute accuracy in the robotic machining process. Thus, it is reasonable to consider performing machining in the regions of the robot workspace where the kinematic, static and even dynamic performances are highest, thereby reducing machining errors and exhausting the advantages of the robot. Simultaneously, an optimum initial placement of the workpiece with respect to the robot can be obtained by optimizing the above performances of the robot. In this paper, a robot posture optimization methodology based on robotic performance indexes is presented. First, a deformation evaluation index is proposed to directly illustrate the deformation of the six-revolute (6R) industrial robot (IR) end-effector (EE) when a force is applied on it. Then, the kinematic performance map drawn according to the kinematic performance index is utilized to refine the regions of the robot workspace. Furthermore, main body stiffness index is proposed here to simplify the performance index of the robot stiffness, and its map is used to determine the position of the EE. Finally, the deformation map obtained according to the proposed deformation evaluation index is used to determine the orientation of the EE. Following these steps, the posture of the 6R robot with the best performance can be obtained, and the initial workpiece placement can be consequently determined. Experiments on a Comau Smart5 NJ 220-2.7 robot are conducted. The results demonstrate the feasibility and effectiveness of the present posture optimization methodology.

Using Dynamics to Consider Torque Constraints in Manipulator Planning With Heavy Loads

Input constraints are active in robot trajectory planning when a mobile robot traverses mobility challenges such as steep hills that limit the acceleration of the robot due to the torque constraints of the motor or engine or in manipulator lifting tasks when the load is sufficiently heavy that the torque constraints of the robot's motor prevent it from statically supporting the load in regions of the robot's workspace. This paper presents a general methodology for solving these planning tasks using a minimum-time cost function and applies it to the problem of a multiple degrees-of-freedom (DOF) manipulator lifting a heavy load. Planning for these types of problems requires use of the robot's dynamic model. Here, we plan using sampling-based model predictive optimization (SBMPO), which is only practical if the planning can be done quickly. Hence, substantial attention is given to efficient computations by: (1) using the dynamic model without integrating it, (2) using optimal control theory to develop an “optimistic A* estimate of cost-to-goal,” which is in this case a rigorous lower bound on the minimum time from a current state to a goal state, and (3) using prior experience to speed up the computation of a new trajectory. The methodology is experimentally verified for heavy lifting using a two-link manipulator.

IMRK: Demonstrator for Intelligent and Intuitive Human–Robot Collaboration in Industrial Manufacturing

This report describes an intelligent and intuitive dual-arm robotic system for industrial human–robot collaboration which provides the basis for further work between DFKI (Robotics Innovation Center) and Volkswagen Group (Smart Production Lab) in the field of intuitive and safe collaborative robotics in manufacturing scenarios. The final robot demonstrator developed in a pilot project possesses multiple sensor modalities for environment monitoring and is equipped with the ability for online collision-free dual-arm manipulation in a shared human–robot workspace. Moreover, the robot can be controlled via simple human gestures. The capabilities of the robotic system were validated at a mockup of a gearbox assembly station at a Volkswagen factory.

Analysis on the Workspace of Six-degrees-of-freedom Industrial Robot Based on AutoCAD

This research discusses the workspace of the industrial robot with six degrees of freedom(6-DOF) based on AutoCAD platform. Based on the analysis of the overall configuration of the robot, this research establishes the kinematic mathematical model of the industrial robot by using DH parameters, and then solves the workspace of the robot consequently. In the AutoCAD, Auto Lisp language program is adopted to simulate the two-dimensional(2D) and three-dimensional(3D) space of the robot. Software user interface is written by using the dialog box control language of Visual LISP. At last, the research analyzes the trend of the shape and direction of the workspace when the length and angle range of the robot are changed. This research lays the foundation for the design, control and planning of industrial robots.