Showing papers in "Journal of Intelligent and Robotic Systems in 1997"

Robot Pose Estimation in Unknown Environments by Matching 2D Range Scans

Abstract: A mobile robot exploring an unknown environment has no absolute frame of reference for its position, other than features it detects through its sensors. Using distinguishable landmarks is one possible approach, but it requires solving the object recognition problem. In particular, when the robot uses two-dimensional laser range scans for localization, it is difficult to accurately detect and localize landmarks in the environment (such as corners and occlusions) from the range scans. In this paper, we develop two new iterative algorithms to register a range scan to a previous scan so as to compute relative robot positions in an unknown environment, that avoid the above problems. The first algorithm is based on matching data points with tangent directions in two scans and minimizing a distance function in order to solve the displacement between the scans. The second algorithm establishes correspondences between points in the two scans and then solves the point-to-point least-squares problem to compute the relative pose of the two scans. Our methods work in curved environments and can handle partial occlusions by rejecting outliers.

Intelligent Control for an Acrobot

Abstract: The acrobot is an underactuated two-link planar robot that mimics the human acrobat who hangs from a bar and tries to swing up to a perfectly balanced upside-down position with his/her hands still on the bar. In this paper we develop intelligent controllers for swing-up and balancing of the acrobot. In particular, we first develop classical, fuzzy, and adaptive fuzzy controllers to balance the acrobot in its inverted unstable equilibrium region. Next, a proportional-derivative (PD) controller with inner-loop partial feedback linearization, a state-feedback, and a fuzzy controller are developed to swing up the acrobot from its stable equilibrium position to the inverted region, where we use a balancing controller to ‘catch’ and balance it. At the same time, we develop two genetic algorithms for tuning the balancing and swing-up controllers, and show how these can be used to help optimize the performance of the controllers. Overall, this paper provides (i) a case study of the development of a variety of intelligent controllers for a challenging application, (ii) a comparative analysis of intelligent vs. conventional control methods (including the linear quadratic regulator and feedback linearization) for this application, and (iii) a case study of the development of genetic algorithms for off-line computer-aided-design of both conventional and intelligent control systems.

Obstacle Collision Detection Using Best Ellipsoid Fit

Abstract: This paper describes a method for estimating the distance between a robot and its surrounding environment using best ellipsoid fit. The method consists of the following two stages. First we approximate the detailed geometry of the robot and its environment by minimum-volume enclosing ellipsoids. The computation of these ellipsoids is a convex optimization problem, for which efficient algorithms are known. Then we compute a conservative distance estimate using an important but little known formula for the distance of a point from and n-dimensional ellipse. The computation of the distance estimate (and its gradient vector) is shown to be an eigenvalue problem, whose solution can be rapidly found using standard techniques. We also present an incremental version of the distance computation, which takes place along a continuous trajectory taken by the robot. We have implemented the proposed approach and present some preliminary results.

Fast Motion Planning by Parallel Processing – a Review

Abstract: One of the many features needed to support the activities of autonomous systems is the ability to plan motion. This enables robots to move in their environment securely and to accomplish given tasks. Unfortunately, the control loop comprising sensing, planning, and acting has not yet been closed for robots in dynamic environments. One reason involves the long execution times of the motion planning component. A solution for this problem is offered by the use of highly parallel computation. Thus, an important task is the parallelization of existing motion planning algorithms for robots so that they are suitable for highly parallel computation. In several cases, completely new algorithms have to be designed, so that a parallelization is feasible. In this survey, we review recent approaches to motion planning using parallel computation. As a classification scheme, we use the structure given by the different approaches to the robot’s motion planning. For each approach, the available parallel processing methods are discussed. Each approach is assigned a unique class. Finally, for each research work referenced, a list of keywords is given.

Nonlinear Filtering of Non-Gaussian Noise

Abstract: This paper introduces a new nonlinear filter for a discrete time, linear system which is observed in additive non-Gaussian measurement noise. The new filter is recursive, computationally efficient and has significantly improved performance over other linear and nonlinear schemes. The problem of narrowband interference suppression in additive noise is considered as an important example of non-Gaussian noise filtering. It is shown that the new filter outperforms currently used approaches and at the same time offers simplicity in the design.

Robust Sliding-mode Control of Nine-link Biped Robot Walking

Abstract: A nine-link planar biped robot model is considered which, in addition to the main links (i.e., legs, thighs and trunk), includes a two-segment foot. First, a continuous walking pattern of the biped on a flat terrain is synthesized, and the corresponding desired trajectories of the robot joints are calculated. Next, the kinematic and dynamic equations that describe its locomotion during the various walking phases are briefly presented. Finally, a nonlinear robust control approach is followed, motivated by the fact that the control which has to guarantee the stability of the biped robot must take into account its exact nonlinear dynamics. However, an accurate model of the biped robot is not available in practice, due to the existence of uncertainties of various kinds such as unmodeled dynamics and parameter inaccuracies. Therefore, under the assumption that the estimation error on the unknown (probably time-varying) parameters is bounded by a given function, a sliding-mode controller is applied, which provides a successful way to preserve stability and achieve good performance, despite the presence of strong modeling imprecisions or uncertainties. The paper includes a set of representative simulation results that demonstrate the very good behavior of the sliding-mode robust biped controller.

Inverse Kinematics of Binary Manipulators Using a Continuum Model

Abstract: Binary actuators have only two discrete states, both of which are stable without feedback As a result, manipulators with binary actuators have a finite number of states The major benefits of binary actuation are that extensive feedback control is not required, reliability and task repeatability are very high, and two-state actuators are generally very inexpensive, resulting in low cost robotic mechanisms These manipulators have great potential for use in both the manufacturing and service sectors, where the cost of high performance robotic manipulators is often difficult to justify The most difficult challenge with a binary manipulator is to achieve relatively continuous end-effector trajectories given the discrete nature of binary actuation Since the number of configurations attainable by binary manipulators grows exponentially in the number of actuated degrees of freedom, calculation of inverse kinematics by direct enumeration of joint states and calculation of forward kinematics is not feasible in the highly actuated case This paper presents an efficient method for performing binary manipulator inverse kinematics and trajectory planning based on having the binary manipulator shape adhere closely to a time-varying curve In this way the configuration of the arm does not exhibit drastic changes as the end effector follows a discrete trajectory

Adaptive Impedance Control Applied to a Pneumatic Legged Robot

Abstract: An adaptive impedance control scheme with estimation of robot and environment parameters is proposed in this paper It consists of two stages of adaptation and control The first one performs an on-line estimation of the robot inertial parameters, during the complete (constrained or not) motion of the leg, while the second one compensates for the uncertainties on the characteristics of the ground (position and stiffness) Simulation results obtained for a single leg of a pneumatic driven, quadruped robot show the effectiveness of the proposed control scheme in case of considerable uncertainty both in the robot and ground parameters

Mobile Manipulator Motion Planning for Multiple Tasks Using Global Optimization Approach

Abstract: A mobile manipulator can perform various tasks efficiently by utilizing mobility and manipulation functions. The coupling of these two functions creates a particular kinematic redundancy introduced by mobility, which is different from that introduced by extra joints. This redundancy is quite desirable for dexterous motion in cluttered environments, but it also significantly complicates the motion planning and control problem. In this paper we propose a new motion planning method for mobile manipulators to execute a multiple task which consists of a sequence of tasks. The task considered in this paper is that the end-effector tracks a prespecified trajectory in a fixed world frame. In a multiple task, the final configuration of each task becomes the initial configuration of the next subsequent task. Such a configuration is known as a commutation configuration, which significantly affects the performance of the multiple task. We formulate the motion planning problem as a global optimization problem and simultaneously obtain the motion trajectory set and commutation configurations. In the formulation, we take account of the case that the platform has a non-holonomic constraint as well as the one that the platform has a holonomic constraint. Simulation results are demonstrated to verify the effectiveness of the proposed method.

How to Control Robots Interacting with Dynamic Environment

Abstract: The goal of this paper is to shed light on the control problem of constrained robot motion from the aspect of the dynamical nature of the environment with which the robot is in contact. Therefore, the criticism of traditional hybrid control which allows position/force feedback loops to split into independent control with respect to position and force, is not the main point we want to make. Reference to the papers written by the founders of hybrid control and their numerous followers served only to better understand the reason and motivation for suggesting a different approach to control of robots interacting with environment. The paper has a predominantly review character, based on recently published work. It also contains some new, unpublished results in the framework of the unified approach to the position/force control of robots, proposed by the present author and his co-workers. By pointing to the possibility of introducing an environment dynamics in the contact tasks of the machining type, the author emphasizes that the proposed dynamically interactive control can be applied to a completely different class of tasks, in which a contact is made between the system (constructions or structure) and very specific kinds of dynamic environments.

Robust Practical Point Stabilization of a Nonholonomic Mobile Robot Using Neural Networks

Abstract: A control structure that makes possible the integration of a kinematic controller and a neural network (NN) computed-torque controller for nonholonomic mobile robots is presented. A combined kinematic/torque control law is developed and stability is guaranteed by Lyapunov theory. This control algorithm is applied to the practical point stabilization problem i.e., stabilization to a small neighborhood of the origin. The NN controller can deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics in the vehicle. On-line NN weight tuning algorithms that do not require off-line learning yet guarantee small tracking errors and bounded control signals are utilized.

Neurofuzzy Motion Planners for Intelligent Robots

Abstract: A neurofuzzy methodology is presented for motion planning in semi-autonomous mobile robots. The robotic automata considered are devices whose main feature is incremental learning from a human instructor. Fuzzy descriptions are used for the robot to acquire a repertoire of behaviors from an instructor which it may subsequently refine and recall using neural adaptive techniques. The robot is endowed with sensors providing local environmental input and a neurofuzzy internal state processing predictable aspects of its environment. Although it has no prior knowledge of the presence or the position of any obstructing objects, its motion planner allows it to make decisions in an unknown terrain. The methodology is demonstrated through a robot learning to travel from some start point to some target point without colliding with obstacles present in its path. The skills acquired are similar to those possessed by an automobile driver. The methodology has been successfully tested with a simulated robot performing a variety of navigation tasks.

Self-Motion Representation and Global Path Planning Optimization for Redundant Manipulators through Topology-Based Discretization

Abstract: This paper addresses manipulator redundancy from a global perspective, aiming at kinematic control through the exploration of self-motion topology The methodology is based on collecting information about the structure of the kinematic map with the use of topological tools, providing an overall view of the configuration space and its relationship to the work space – a suitable framework for the efficient implementation of global approaches A space discretization method has been developed to benefit from the topological structure, embedding kinematics in its representation This method enables an efficient exploration of global redundancy resolution and path planning, offering the means to avoid local minima and deadlocks with minimum effort The discretization was implemented for a planar manipulator, demonstrating significant improvement in the search for globally optimum solutions of path planning when compared to traditional approaches

Grasping of Static and Moving Objects Using a Vision-Based Control Approach

Abstract: Robotic systems require the use of sensing to enable flexible operation in uncalibrated or partially calibrated environments. Recent work combining robotics with vision has emphasized an active vision paradigm where the system changes the pose of the camera to improve environmental knowledge or to establish and preserve a desired relationship between the robot and objects in the environment. Much of this work has concentrated upon the active observation of objects by the robotic agent. We address the problem of robotic visual grasping (eye-in-hand configuration) of static and moving rigid targets. The objective is to move the image projections of certain feature points of the target to effect a vision-guided reach and grasp. An adaptive control algorithm for repositioning a camera compensates for the servoing errors and the computational delays that are introduced by the vision algorithms. Stability issues along with issues concerning the minimum number of required feature points are discussed. Experimental results are presented to verify the validity and the efficacy of the proposed control algorithms. We then address an adaptation to the control paradigm that focuses upon the autonomous grasping of a static or moving object in the manipulator’s workspace. Our work extends the capabilities of an eye-in-hand system beyond those as a ‘pointer’ or a ‘camera orienter’ to provide the flexibility required to robustly interact with the environment in the presence of uncertainty. The proposed work is experimentally verified using the Minnesota Robotic Visual Tracker (MRVT) [7] to automatically select object features, to derive estimates of unknown environmental parameters, and to supply a control vector based upon these estimates to guide the manipulator in the grasping of a static or moving object.

Robust Path Planning for Non-Holonomic Robots

Abstract: This paper deals with mobile robots path planning. We decompose the problem in three parts. In the first part, we describe a modeling method based on a configuration space discretization. Each model element is built following a particular structure which is easy to handle, as we will show. We describe the methodologies and the algorithms allowing to build the model. In the second part, we propose a path-planning application for a non-holonomic robot in configuration space. In the third part, we modify the path in order to be robust according to the control errors.

Decentralized PD and Robust Nonlinear Control for Robot Manipulators

Abstract: A decentralized PD and robust nonlinear feedback law for robot motion control is proposed. The control system structure is based on the generalized tracking error proposed by Slotine and Li. Using this system structure, a simple and comprehensive result on the local stability conditions of PD control is obtained. A decentralized robust nonlinear feedback term is then added to it to improves the performance of tracking errors from local convergence to global convergence. Since the approach keeps the simplicity of the independent joint controller structure it can be easily implemented in most robot systems without hardware alteration.

Behaviour Synthesis of the Erect Stance for a Biped Control

Abstract: In this paper we use free fall approach to develop a high level control/command strategy for a bipedal robot called ‘BIPMAN’, based on a multi-chain mechanical model with a general control architecture. The strategy is composed of three levels: the ‘Legs and arms’ level, the ‘Coordinator’ level and the ‘Supervisor’ level. The ‘Coordinator’ level is devoted to controlling leg movements and to ensure the stability of the whole biped. Actually perturbation effects threaten the equilibrium of the ‘human’ robot and can only be compensated using a dynamic control strategy. This one is based on dynamic stability studies with a ‘center of mass acceleration control’ and a force distribution on each leg and arm. Free fall in the gravity field is assumed to be deeply involved in the human locomotor control. According to studies of this specific motion through a direct dynamic model, the notion of ‘equilibrium classes’ is introduced. They allow one to define time intervals in which the biped is able to maintain its posture. This notion is used for the definition of a ‘reconfigurable’ high level control of the robot.

Nonlinear Performance Index (npi): A Tool for Manipulator Dynamics Improvement

Abstract: The precise control of manipulators depends nonlinearly on the velocity of the motion as well as on manipulator configuration and commanded acceleration requiring complex control strategies. This paper presents a useful tool for identifying and quantifying nonlinear effects appearing during the motion of any manipulator, the Nonlinear Performance Index (npi). The npi takes into account not only the geometrical parameters defining the manipulator but also its structural dynamics through the use of inertial parameters like mass, inertia, centre of mass\,\ldots The npi can be used in the design stage for analysing and reducing these undesirable nonlinear effects in any general motion or in the trajectory planning looking for paths along which more precise control is expected. The last part of the paper shows how this design optimisation and path planning has been applied to the Agribot, a fruit picking robot designed at the IAI.

Force Sensing Using Kalman Filtering Techniques for Robot Compliant Motion Control

Abstract: Robot motion can be classified into free and contact motion. In practical tasks, however, the motion may transit from free motion to contact motion and vice versa. In such tasks, a position controller is designed in free motion. A compensator is then added in the force feedback loop to help the system reach the desired target impedance when the end-effector is in contact with the environment. To obtain accurate contact force, a Kalman filter is used to extract contact force from the wrist force sensor signal which contains inertial force of the end-effector also.

Sensor Fusion and Planning with Perception–Action Network

Abstract: Robot intelligence requires a real-time connection between sensing and action. A new computation principle of robotics that efficiently implements such a connection is utmost important for the new generation of robotics. In this paper, a perception–action network is presented as a means of efficiently integrating sensing, knowledge, and action for sensor fusion and planning. The network consists of a number of heterogeneous computational units, representing feature transformation and decision-making for action, which are interconnected as a dynamic system. New input stimuli to the network invoke the evolution of network states to a new equilibrium, through which a real-time integration of sensing, knowledge, and action can be accomplished. The network provides a formal, yet general and efficient, method of achieving sensor fusion and planning. This is because the uncertainties of signals, propagated in the network, can be controlled by modifying sensing parameters and robot actions. Algorithms for sensor planning based on the proposed network are established and applied to robot self-localization. Simulation and experimental results are shown.

A Stable Neuro-Adaptive Controller for Rigid Robot Manipulators

Abstract: In this paper a controller based on neural networks is proposed to achieve output trajectory tracking of rigid robot manipulators. Neural networks used here are one hidden layer ones so that their outputs depend linearly on the parameters. Our method uses a decomposed connectionist structure. Each neural network approximate a separate element of the dynamical model. These approximations are used to perform an adaptive stable control law. The controller is based on direct adaptive techniques and the Lyapunov approach is used to derive the adaptation laws of the nets’ parameters. By using an intrinsic physical property of the manipulator, the system is proved to be stable. The performance of the controller depends on the quality of the approximation, i.e. on the inherent reconstruction errors of the exact functions.

Increased Autonomy in Industrial Robotic Systems: A Framework

Abstract: This paper presents an event based control system structure for the control of a robot workcell and its implementation. The goal for this control system is to autonomously manage the dynamic environment of a robot workcell. The presented control system is event driven and operates from tasks and a World model, defined in a task oriented programming session. During realisation of the tasks, the World model is continuously updated by information from sensors. The system always operates on the latest information which may result in re-planning of sub-tasks or whole tasks. The autonomous functionality of the presented system is established through reactive re-planning and configurable corrections. A high level adaptation of the model in the control system to the workcell is of great value for the performance of the robot system. An important and efficient use of robot motion control for application process controlling purposes are enabled through the control systems internal configurable interaction with the environment.

Globally Optimal Trajectory Planning for Redundant Manipulators using State Space Augmentation Method

Abstract: The off-line global trajectory planning for kinematically redundant manipulators is formulated as an optimization problem whose solution is obtained by applying the Pontryagin’s Maximum Principle. The state space augmentation method is developed to obtain a set of optimal joint trajectories corresponding to a singularity-free Cartesian path which avoids joint limits and conserves joint configuration in cyclic motion. Results of computer simulation conducted on a three-degree-of-freedom planar manipulator are presented and discussed.

Position-Based Fuzzy Force Control for Dual Industrial Robots

Abstract: 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.

Stable Sampled-data Adaptive Control of Robot Arms Using Neural Networks

Abstract: Stable neural network-based sampled-data indirect and direct adaptive control approaches, which are the integration of a neural network (NN) approach and the adaptive implementation of the discrete variable structure control, are developed in this paper for the trajectory tracking control of a robot arm with unknown nonlinear dynamics. The robot arm is assumed to have an upper and lower bound of its inertia matrix norm and its states are available for measurement. The discrete variable structure control serves two purposes, i.e., one is to force the system states to be within the state region in which neural networks are used when the system goes out of neural control; and the other is to improve the tracking performance within the NN approximation region. Main theory results for designing stable neural network-based sampled data indirect and direct adaptive controllers are given, and the extension of the proposed control approaches to the composite adaptive control of a flexible-link robot is discussed. Finally, the effectiveness of the proposed control approaches is illustrated through simulation studies.

Position Control of Robots by Nonlinearity Estimation and Compensation: Theory and Experiments

Abstract: To improve the performance of position controllers of robots, a design approach using the method of nonlinearity estimation and compensation is presented. The controller designed has a similar structure to that of the linear independent joint control. Nonlinearities in the robot dynamics are estimated by a state observer based on approximate linear models of the system, and then compensated by an appropriate feedback. The paper presents the theory how to design such a controller as well as the experimental results verifying its performance.

A Robust Method for the Concurrent Motion Planning of Multi-Manipulators Systems

Abstract: In this article a robust and simple procedure for the on-line concurrent motion planning of multi-manipulators is presented The approach is based on solving for each manipulator a linear system of equations taking into account a vector for motion planning, and an original scheme for the appropriate perturbation of the pseudoinverse matrix This method can pursue simultaneously both motion coordination and singularities prevention in real time in a sensor based environment These properties make it suitable for fully autonomous or telerobotic systems operations

Impedance Control with On-Line Neural Network Compensator for Dual-Arm Robots

Abstract: A controller design strategy of dual-arm robots is proposed in this paper. The controller consists of a central controller and three force controllers. The central controller is used to calculate each arm’s force command according to the desired object motion. A force controller is used in each arm to track the commanding force. Another force controller is used to track the desired contact force between the manipulated object and its environment. The force controller can be partitioned into three parts. The computed torque method is used to linearize and decouple the dynamics of a manipulator. An impedance controller is then added to regulate the mechanical impedance between the manipulator and its environment. In order to track a reference force signal, an on-line neural network is used to compensate the effect of unknown parameters of the manipulator and environment. The simulation results are reported to show the performance of the neural network compensator.

A New Control Scheme of Single-link Flexible Manipulators Robust to Payload Changes

Abstract: A new scheme is presented in this paper to control single-link flexible manipulators. The objective is to control the tip position of the flexible arm in the presence of joint friction and payload changes. The control scheme is based on two nested loops: an inner feedback loop to control the motor position which is robust to joint friction, and an outer loop to control the tip position which is robust to payload changes. This outer loop is composed of a feedforward term and a feedback term. This results in a simple control law that needs minimal computing effort and, thus, can be used for real time control of flexible arms. The proposed method is general in the sense that it can be applied to very different arm structures and diverse sensor systems configurations. Results corresponding to two different arm setups are presented.

Passive Compliance from Robot Limbs and its Usefulness in Robotic Automation

Abstract: Robots have been traditionally used as positioning devices without much regard to external forces experienced by the tool. This has limited further potential applications of robots in automation. Most tasks that remain to be automated require constrained robot motion and/or involve work done by the robot on the environment. Such tasks require both force and position control. The ability to control the end-effector compliance is critical to successful force and position control tasks. Although the end-effector compliance can be actively controlled through the joint flexibilities provided by the joint servos or by active force sensing, the usefulness of having the minimum passive compliance in addition to active compliance control can improve performance. In surface following, for example, it is necessary to make the end-point of a robot have the right compliance to prevent jamming. The usefulness of passive compliance has been demonstrated by the use of compliance-devices on the robot end-effector such as the Remote Center Compliance. The natural compliance inherent in light weight and flexible robot structures, however, can be exploited to provide the necessary passive compliance required. In this paper we present a novel framework for computing the end-effector compliance from the compliance offered by the limbs of a serial robot. The emphasis is on the explanation of the passive end-effector compliance resulting from these structures, and particular attention is given to the use of these results in the selection of the type of robot for a particular task. We show examples of end-effector compliances as functions of joint configurations for the SCARA- and PUMA-type robots. The joint-configuration dependent end-effector compliance can be used to select the desired robot pose for the performance of a robotic task.