On-line collision avoidance system for two PTP command-based manipulators with distributed controller

This research aims to solve online collision avoidance problem of two manipulators with independent controller. Since industrial robot controller is a closed commercial system, trajectory generation part of robot controlling is always proprietary or unknown. Thus, this paper proposes a collision avoidance system of two manipulators which are controlled by point-to-point (PTP) commands, in condition that the internal of robot controller is unknown and unchangeable. Based on this condition, collision avoidance is supposed to be realized by online scheduling of these PTP controlling commands. This paper proposes the collision avoidance method that assumes the three-dimensional common workspace between two manipulators can be partitioned into many subregion elements. And with managing these subregion elements, which are occupied by robot motion, PTP commands are scheduled to adjust execution timing for collision avoidance. A deadlock problem caused by the partition of the workspace is also taken into consideration in the method. And the effectiveness and efficiency of the method have been verified by simulations and experiments.     

Neuro-fuzzy and model-based motion control for mobile manipulator among dynamic obstacles

This paper focuses on autonomous motion control of a nonholonomic platform with a robotic arm, which is called mobile manipulator. It serves in transportation of loads in imperfectly known industrial environments with unknown dynamic obstacles. A union of both procedures is used to solve the general problems of collision-free motion. The problem of collision-free motion for mobile manipulators has been approached from two directions, Planning and Reactive Control. The dynamic path planning can be used to solve the problem of locomotion of mobile platform, and reactive approaches can be employed to solve the motion planning of the arm. The execution can generate the commands for the servo-systems of the robot so as to follow a given nominal trajectory while reacting in real-time to unexpected events. The execution can be designed as an Adaptive Fuzzy Neural Controller. In real world systems, sensor-based motion control becomes essential to deal with model uncertainties and unexpected obstacles.     

An efficient local approach for the path generation of robot manipulators

In this article an efficient local approach for the path generation of robot manipulators is presented. The approach is based on formulating a simple nonlinear programming problem. This problem is considered as a minimization of energy with given robot kinematics and subject to the robot requirements and a singularities avoidance constraint. From this formulation a closed form solution is derived which has the properties that allows to pursue both singularities and obstacle avoidance simultaneously; and that it can incorporate global information. These properties enable the accomplishment of the important task that while a specified trajectory in the operational space can be closely followed, also a desired joint configuration can be attained accurately at a given time. Although the proposed approach is primarily developed for redundant manipulators, its application to nonredundant manipulators is examplified by considering a particular commercial manipulator.     

B-splines joint trajectory planning

This paper describes how B-splines can be used to construct joint trajectories for robot manipulators. The motion is specified by a sequence of Cartesian knots, i.e., positions and orientations of the end effector of a robot manipulator. For a six joint robot manipulator, these Cartesian knots are transformed into six sets of joint variables, with each set corresponding to a joint. Splines, represented as linear combinations of B-splines, are used to fit the sequence of joint variables for each of the six joints. A computationally very simple recurrence formula is used to generate the B-splines. This approach is used for the first time to establish the mathematical model of trajectory generation for robot manipulators, and offers flexibility, computational efficiency, and a compact representation.     

An open-source multi-DOF articulated robotic educational platform for autonomous object manipulation

This research presents an autonomous robotic framework for academic, vocational and training purpose. The platform is centred on a 6 Degree Of Freedom (DOF) serial robotic arm. The kinematic and dynamic models of the robot have been derived to facilitate controller design. An on-board camera to scan the arm workspace permits autonomous applications development. The sensory system consists of position feedback from each joint of the robot and a force sensor mounted at the arm gripper. External devices can be interfaced with the platform through digital and analog I/O ports of the robot controller. To enhance the learning outcome for beginners, higher level commands have been provided. Advanced users can tailor the platform by exploiting the open-source custom-developed hardware and software architectures. The efficacy of the proposed platform has been demonstrated by implementing two experiments; autonomous sorting of objects and controller design. The proposed platform finds its potential to teach technical courses (like Robotics, Control, Electronics, Image-processing and Computer vision) and to implement and validate advanced algorithms for object manipulation and grasping, trajectory generation, path planning, etc. It can also be employed in an industrial environment to test various strategies prior to their execution on actual manipulators.     

An Attempt to Raise the Level of Software Abstraction in Assembly Robotics through an Apposite Choice of Underlying Mechatronics

Robot manipulators were meant to be the production engineer"s flexible friend. Assembly robots, however, have failed to fulfill their promise. The problem that has continuously plagued robotic assembly is that of spatial uncertainty. It is our thesis that the ubiquitous problem of spatial uncertainty is an artefact of the fact that current industrial manipulators are designed for an operational paradigm that assumes position control is of primary importance. In this paper we propound an alternative approach based on sliding as the primary motion primitive. We first present a model that uses sliding to allow us to raise the level of abstraction of robot programming tasks. We then describe an inherently accommodating, (planar) three degree of freedom, direct-drive robot arm that was constructed to test our approach. Finally, we present data collected from representative (planar) manipulation tasks that substantiate our claims.     

An adaptive fuzzy strategy for motion control of robot manipulators

This paper makes an attempt to develop a self-tuned proportional-integral-derivative (PID)-type fuzzy controller for the motion control of robot manipulators. In recent past, it has been widely believed that static fuzzy controllers can not be suitably applied for controlling manipulators with satisfaction because the robot manipulator dynamics is too complicated. Hence more complicated and sophisticated neuro-fuzzy controllers and fuzzy versions of nonlinear controllers have been more and more applied in this problem domain. The present paper attempts to look back at this widely accepted idea and tries to develop a self-tuned fuzzy controller with small incremental complexity over conventional fuzzy controllers, which can yet attain satisfactory performance. The proposed controller is successfully applied in simulation to control two-link and three-link robot manipulators.     

Robot path planning in a constrained workspace by using optimal control techniques

Robot manipulators are programmable mechanical systems designed to execute a great variety of tasks in a repetitive way. In industrial environment, while productivity increases, cost reduction associated with robotic operation and maintenance can be obtained as a result of decreasing the values of dynamic quantities such as torque and jerk, with respect to a specific task. Furthermore, this procedure allows the execution of various tasks that require maximum system performance. By including obstacle avoidance ability to the robot skills, it is possible to improve the robot versatility, i.e., the robot can be used in a variety of operating conditions. In the present contribution, a study concerning the dynamic characteristics of serial robot manipulators is presented. An optimization strategy that considers the obstacle avoidance ability together with the dynamic performance associated with the movement of the robot is proposed. It results an optimal path planning strategy for a serial manipulator over time varying constraints in the robot workspace. This is achieved by using multicriteria optimization methods and optimal control techniques. Numerical simulation results illustrate the interest of the proposed methodology and the present techniques can be useful for the design of robot controllers. Commemorative contribution.     

A Pseudo-Distance Algorithm for Collision Detection of Manipulators Using Convex-Plane-Polygons-based Representation

Robots have become indispensable parts for the industrial automated workshop. The distance calculation between the industrial robot and obstacles is a fundamental problem for the robotic collision-free motion control. But existing methods for this problem have the disadvantage that the accuracy and execution efficiency cannot be guaranteed at the same time. In this paper, a pseudo-distance algorithm is presented based on the convex-plane-polygons-based representation to solve this problem. In this algorithm, convex plane polygons (CPPs) and cylinders are utilized to represent obstacles and the manipulator, respectively. The spatial relationship between each CPP and each cylinder is discussed through defining condition parameters, and is divided into six conditions including three special conditions when the contact happens, and the other three conditions when there exists a certain distance between the CPP and the cylinder. The distance is modelled under different relations based on the theory of mathematics and geometry. The pseudo distance is determined through selecting the smallest value from distances between CPPs and cylinders. The numerical experiment is performed based on the proposed and two previous algorithms to estimate the shortest distance between an industrial robot and a groove-shape obstacle. And an application example is performed to show the significance of this work on the robotic motion control. Results indicate that the proposed algorithm is more efficient than previous algorithms under a certain precision requirement, and can be effectively applied in the robotic motion control.     

Multi‐objective learning control for robotic manipulator

Several types of learning controllers have been proposed in the literature to improve the tracking performance of robot manipulators. In most cases, the learning algorithms emphasize mainly on a single objective of learning a desired motion of the end‐effector. In some applications, more than one objective may be specified at the same time. For example, a robot may be required to follow a desired trajectory (primary objective) and at the same time avoid an obstacle (secondary objective). Thus, multi‐objective learning control can be more effective to realize the collision‐free tasks. In this paper, a multi‐objective learning control problem is formulated and solved. In the proposed learning control system, the primary objective is to track a desired end‐effector's motion and several secondary objectives can be specified for the desired orientation and for obstacles avoidance. To avoid obstacles in the workspace, a new learning concept called “region learning control” is also proposed in this paper. The proposed learning controllers do not require the exact knowledge of robot kinematics and dynamics. Sufficient condition is presented to guarantee the convergence of the learning system. The proposed learning controllers are applied to a four‐link planar redundant manipulator and simulation results are presented to illustrate the performance. © 2004 Wiley Periodicals, Inc.     

Adaptive neural controller for cooperative multiple robot manipulator system manipulating a single rigid object

In this article, an adaptive neural controller is developed for cooperative multiple robot manipulator system carrying and manipulating a common rigid object. In coordinated manipulation of a single object using multiple robot manipulators simultaneous control of the object motion and the internal force exerted by manipulators on the object is required. Firstly, an integrated dynamic model of the manipulators and the object is derived in terms of object position and orientation as the states of the derived model. Based on this model, a controller is proposed that achieves required trajectory tracking of the object as well as tracking of the desired internal forces arising in the system. A feedforward neural network is employed to learn the unknown dynamics of robot manipulators and the object. It is shown that the neural network can cope with the unknown nonlinearities through the adaptive learning process and requires no preliminary offline learning. The adaptive learning algorithm is derived from Lyapunov stability analysis so that both error convergence and tracking stability are guaranteed in the closed loop system. Finally, simulation studies and analysis are carried out for two three-link planar manipulators moving a circular disc on specified trajectory.     

Physics-based simulation for manual robot guidance—An eRobotics approach

Manual robot guidance is an intuitive approach to teach robots with human's skills in the loop. It is particularly useful to manufacturers because of its high flexibility and low programming effort. However, manual robot guidance requires compliance control that is generally not available in position-controlled industrial robots. We address this issue from a simulation-driven approach. We systematically capture the interactive dynamic behavior of intelligent robot manipulators within physics-based virtual testbeds, regardless of the type of application. On this basis, we develop structures to equip and employ simulated robots with motion control capabilities that include soft physical interaction control driven in real-time with real external guidance forces. We then transfer the virtual compliant behavior of the simulated robots to their physical counterparts to enable manual guidance. The simulator provides assistance to operators through timely and insightful robot monitoring, as well as meaningful performance indexes. The testbed allows us to swiftly assess guidance within numerous interaction scenarios. Experimental case studies illustrate the practical usefulness of the symbiotic transition between 3D simulation and reality, as pursued by the eRobotics framework to address challenging issues in industrial automation.     

Dual arm manipulation—A survey

Recent advances in both anthropomorphic robots and bimanual industrial manipulators had led to an increased interest in the specific problems pertaining to dual arm manipulation. For the future, we foresee robots performing human-like tasks in both domestic and industrial settings. It is therefore natural to study specifics of dual arm manipulation in humans and methods for using the resulting knowledge in robot control. The related scientific problems range from low-level control to high level task planning and execution. This review aims to summarize the current state of the art from the heterogenous range of fields that study the different aspects of these problems specifically in dual arm manipulation.     

Operating robot manipulators through kinematic singularities using a continuously sliding mode control

Planning the motion of end-effectors of robot manipulators can be carried out more directly in the Cartesian space compared to the joint space. Yet, Cartesian paths may include singular configurations where conventional control schemes suffer from excessive joint velocities and loss of tracking accuracy. The difficulties arise because the Jacobian matrix that is used to establish a linear relation between the velocities in the task and joint spaces loses rank at singularities. The problem can be resolved by using a local second-order approximation of robot kinematics for the joint velocities, which is called Resolved Motion Quadratic Rate Control. In this article, we present a control strategy based on this approach and a recently developed variable structure control scheme. The controller receives Cartesian inputs whenever the manipulator is outside the singular domain. Otherwise, it uses resolved motion quadratic rate control to compute the required joint inputs. Numerical simulation is performed to show that the proposed control scheme provides accurate tracking of the desired motion without inducing excessive control activity when operating robot manipulators through singular configurations. © 1994 John Wiley & Sons, Inc.     

Energy analysis of robot task motions

An energy criterion for choosing the best type of manipulator for a specified task is developed. First, the energy required to perform the robotic task is calculated. Then the lower bound of the mechanical energy consumed by the various kinds of manipulators during their motion, while performing a task, is calculated. Thus, the efficiency of a manipulator for the task is determined. Some examples show that the proper selection of the manipulator configuration can reduce the required energy to a quarter of that of a less suitable one. Once the most suitable manipulator is chosen, the criterion for its most energy efficient motion is developed. The model takes into account the kinematic configuration of the robot, gravitational and other external and internal forces acting on the robot during its operation, and the electric motor driving the robot links. Energy optimization of different paths of motion in joint coordinates is discussed briefly.     

Motion coordination and reactive control of autonomous multi-manipulator systems

The emerging field of service robots demands new systems with increased flexibility. The flexibility of a robot system can be increased in many different ways. Mobile manipulation—the coordinated use of manipulation capabilities and mobility—is an approach to increase robots flexibility with regard to their motion capabilities. Most mobile manipulators that are currently under development use a single arm on a mobile platform. The use of a two-arm manipulator system allows increased manipulation capabilities, especially when large, heavy, or non-rigid objects must be manipulated. This article is concerned with motion control for mobile two-arm systems. These systems require new schemes for motion coordination and control. A coordination scheme called transparent coordination is presented that allows for an arbitrary number of manipulators on a mobile platform. Furthermore, a reactive control scheme is proposed to enable the platform to support sensor-guided manipulator motion. Finally, this article introduces a collision avoidance scheme for mobile two-arm robots. This scheme surveys the vehicle motion to avoid platform collisions and arm collisions caused by self-motion of the robot. © 1996 John Wiley & Sons, Inc.     

A class of nonlinear PD-type controllers for robot manipulators

In this article we present the stability analysis of a class of PD-type controllers for position and motion control of robot manipulators. The main feature of this class of controllers is that the proportional and derivative gains can be nonlinear functions of the robot states. These controllers can be obtained from control strategies that adjust the controller gains depending on the robot states. It is shown that global asymptotic stability of the control system is achieved provided that the P and D gains have suitable structure. As an outcome, we propose a global regulator constrained to deliver torques within prescribed limits of the actuator's capabilities. Experimental results on a two degrees of freedom direct drive arm show the usefulness of the proposed scheme. © 1996 John Wiley & Sons, Inc.     

Kinematics and the implementation of an elephant's trunk manipulator and other continuum style robots

Traditionally, robot manipulators have been a simple arrangement of a small number of serially connected links and actuated joints. Though these manipulators prove to be very effective for many tasks, they are not without their limitations, due mainly to their lack of maneuverability or total degrees of freedom. Continuum style (i.e., continuous "back-bone") robots, on the other hand, exhibit a wide range of maneuverability, and can have a large number of degrees of freedom. The motion of continuum style robots is generated through the bending of the robot over a given section; unlike traditional robots where the motion occurs in discrete locations, i.e., joints. The motion of continuum manipulators is often compared to that of biological manipulators such as trunks and tentacles. These continuum style robots can achieve motions that could only be obtainable by a conventionally designed robot with many more degrees of freedom. In this paper we present a detailed formulation and explanation of a novel kinematic model for continuum style robots. The design, construction, and implementation of our continuum style robot called the elephant trunk manipulator is presented. Experimental results are then provided to verify the legitimacy of our model when applied to our physical manipulator. We also provide a set of obstacle avoidance experiments that help to exhibit the practical implementation of both our manipulator and our kinematic model.     

Manipulating deformable linear objects: Sensor‐based skills of adjustment motions for vibration reduction

The vibration of a deformable object is often problematic during automatic handling by robot manipulators. However, humans can often handle and damp the vibration of deformable objects with ease. This paper presents force/torque sensor‐based skills for handling deformable linear objects in a manner suitable to reduce acute vibration with simple human skill inspired strategies that consist of one or two adjustment motions. The adjustment motion is a simple open‐loop motion that can be attached to the end of any arbitrary end‐effector's trajectory. As an ordinary industrial robot's simple action, it has three periods, i.e., acceleration, constant speed, and deceleration period; it starts from a predicted time tightly close to a force/moment maximum. The predicted time for the adjustment action is generated automatically on‐line based on the vibration rhythm and the data sensed by a force/torque sensor mounted on the robot's wrist. To find the matching point between the vibrational signal of the deformable object and a template, template matching techniques including cross‐correlation and minimum squared error methods are used and compared. Experiments are conducted with an industrial robot to test the new skills under various conditions. The results demonstrate that an industrial robot could perform effective vibration reduction skills with simple strategies. © 2005 Wiley Periodicals, Inc.