We present a Bayesian object observation model for complete probabilistic semantic SLAM. Recent studies on object detection and feature extraction have become important for scene understanding and 3D mapping. However, 3D shape of the object is too complex to formulate the probabilistic observation model; therefore, performing the Bayesian inference of the object-oriented features as well as their pose is less considered. Besides, when the robot equipped with an RGB mono camera only observes the projected single view of an object, a significant amount of the 3D shape information is abandoned. Due to these limitations, semantic SLAM and viewpoint-independent loop closure using volumetric 3D object shape is challenging. In order to enable the complete formulation of probabilistic semantic SLAM, we approximate the observation model of a 3D object with a tractable distribution. We also estimate the variational likelihood from the 2D image of the object to exploit its observed single view. In order to evaluate the proposed method, we perform pose and feature estimation, and demonstrate that the automatic loop closure works seamlessly without additional loop detector in various environments.

This paper presents a feature encoding method of complex 3D objects for high-level semantic features. Recent approaches to object recognition methods become important for semantic simultaneous localization and mapping (SLAM). However, there is a lack of consideration of the probabilistic observation model for 3D objects, as the shape of a 3D object basically follows a complex probability distribution. Furthermore, since the mobile robot equipped with a range sensor observes only a single view, much information of the object shape is discarded. These limitations are the major obstacles to semantic SLAM and view-independent loop closure using 3D object shapes as features. In order to enable the numerical analysis for the Bayesian inference, we approximate the true observation model of 3D objects to tractable distributions. Since the observation likelihood can be obtained from the generative model, we formulate the true generative model for 3D object with the Bayesian networks. To capture these complex distributions, we apply a variational auto-encoder. To analyze the approximated distributions and encoded features, we perform classification with maximum likelihood estimation and shape retrieval.

This paper presents a probabilistic normal distributions transform (NDT) representation which improves the accuracy of point cloud registration by using the probabilities of point samples. Since conventional NDT does not generate distributions in cells having fewer point samples than the number threshold, it would be failed to represent the environment if the point cloud is divided by high-resolution cells. Also, it can lead to incorrect estimations of pose variations. To solve the problem, we define the probability of a point sample and compute the mean and covariance based on the probability. Besides, we show that the generalization property of the probabilistic NDT objective function. The probabilistic NDT has two advantages. First, it generates distributions in all of the occupied cells regardless of the resolution of cells. Second, it reduces the degeneration effect by using modified covariance. The experimental results show that all of the occupied cells have distributions even if the point cloud is divided by high-resolution cells and that the probabilistic NDT improves the accuracy of NDT-based registration.

In this paper, we propose a classification method for single views of 3D objects with missing data retrieval. A mobile robot equipped with range sensors basically obtains only single view information of a 3D scene. Therefore, large amount of information is missing by self-occlusion, which leads to severe restriction on the object classification exploiting the whole shapes of 3D objects. Humans can precisely identify the objects from single view, since they already have concepts of the entire shape of 3D objects by learning process. Based on these concepts, humans can infer the entire shape and category of the object from a single view. Inspired from this, the proposed algorithm learns concepts in abbreviated form for the shapes of 3D objects, then infers the entire shape and object category from these concepts simultaneously. We apply a generative model based on variational auto-encoder (VAE) to learn the concepts for complex shapes of 3D objects. Our method is evaluated on 3D CAD model dataset, and also compared with other state-of-the-art methods.

In this paper, we present an efficient approach to deployment for unmanned aerial vehicles (UAVs). For a number of scattered tasks, we aim to minimize the duration of time that all UAVs reach their task locations. In our previous work, we suggested the collaborative deployment algorithm for mobile robots using a carrier robot which transports and deploys the mobile robots. However, the method worked only in 2-dimensional plane where UAV could not be applied. Therefore, this paper extends the previous work on 3-dimensional space and gives the relevant algorithm. Finally, we presents the feasibility of the proposed algorithm by simulation results.

This paper presents the problem of robot deployment for a number of scattered tasks. We aim to minimize the duration it takes for all robots to reach their assigned task locations. In previous work, we have proposed a team composed of one carrier robot (CR) and several servant robots to accomplish the mission. Then we have suggested an algorithm that determines a path of the CR for an efficient deployment under a few constraints, which is verified by simulations. Assuming that the servant robots are unmanned aerial vehicles (UAVs), the present paper extends the discussion to a real robot experiment. We design and implement a deployment system in intelligent space. The feasibility of the study is demonstrated through an experiment.

In this paper, we introduce an efficient technique that addresses the deployment problem of a team of heterogeneous robots. For multiple scattered tasks in arbitrary space, the objective to be solved is to minimize time for robots to reach all the task locations. In order to reach the assigned task locations efficiently, the team composed of one carrier robot and several servant robots with different motion capabilities is considered. The carrier robot is responsible for transporting and deployment of the servant robots. The servant robot, deployed by the carrier robot, moves to the assigned task location and executes the given task. Then, the overall time to reach all the task locations is determined by considering the path of the carrier that is made by deployment points, and by the maximum time traveled by the servant robots. To find efficient deployment points, the set of all the tasks are iteratively divided into two subsets. Then, an optimal deployment point corresponding to two subsets is calculated by considering the speed of the carrier robot and servant robot, the unloading time for deployment, and few dynamics of the carrier robot. The overall time is computed and compared to the previous result so that the time it takes is continuously reduced. The proposed method has been tested on simulations.

This paper proposes a measurement method of human position based on sensor network First, we explain informationally structured space, robot partners, and sensor networks developed in this study. Next, we discuss the applicability of the sensor network and robot partners for human interaction. Next, we apply a steady-state genetic algorithm using template matching to extract a person in 3D distance image based on differential extraction. Finally, we discuss the effectiveness of the proposed methods through several experimental results.

This paper presents a collision anticipation method that shows when and where collisions will occur in configuration time space by considering the future behavior of agents. Previous solutions to collision anticipation have mainly focused on generating immediate reactive solutions to time-varying environments because of the high inaccuracy of sensors and a heavy computation burden. However, the recent rapid growth in sensor and estimation technology has led to a need for new systems that consider agents' future behavior explicitly. Based on this need, we formalized a mathematical approach to collision anticipation and proposed a tool, the 3-D triangular collision object(TCO), that informs a robot where and when collisions will occur over all possible heading angles. This formulation greatly reduced computation loads. Considering that this result is especially advantageous to fast moving robots, a full-speed collision-free (FSCF) motion planner is proposed based on the TCO. In real experiments, analytic solutions from the planner were modified to compensate for sensor error, and finally yielded safe motions for fast moving robots.

This paper presents two innovative driving methodologies using magnetically driven microtools for precise cell manipulations and its automation systems. First, the magnetic analysis has been conducted to show the current MMT problem. Then, the new driving methodologies are introduced and backed up with FEM analysis and the experimental results. The positioning accuracy improves 3–10 times and the following response become 10 times higher against the driving linear stage. Using this methodology, the enucleation of oocytes is demonstrated to show the effectiveness of the method.

This paper proposes a method of 3D topological reconstruction for informationally structured space including sensor networks and robot partners for co-existing with people. The informationally structured space realizes the quick update and access of valuable and useful information for both people and robots on real and virtual environments. In this paper, we use distance information and color information measured by 3D distance image sensor and CMOS camera for 3D topological reconstruction. First, we propose an extraction method of objects from the background image based on Hough transform as preprocessing. Next, we propose a method of 3D topological reconstruction based on growing neural gas to construct informationally structured space. Finally, we show experimental results of the proposed method and discuss the effectiveness of the proposed method.

One of the fundamental requirements of an autonomous mobile robot is that it must not collide with obstacles. This paper addresses the problem of controlling an autonomous robot to avoid obstacles for reactive route following navigation. The four-wheeled mobile robot is equipped with three monocular cameras for route following and a range sensor for obstacle avoidance. The equipped robot moves route environments using a reactive navigation method. When an obstacle is detected by a range sensor, the proposed obstacle avoidance method estimates the free space in the route and generates the turning direction vector for heading the robot to the free space which is wider than other space. We executed experiments about the navigation algorithm of the robot with an obstacle including curved path environments in this paper. Through the experiment results in various cases, it is discovered that the proposed method has a better time performance for obstacle avoidance in comparison with other conventional technique.

This paper proposes an adaptive and probabilistic extension of Rapidly-exploring Random Tree (RRT) for visual route navigation of a mobile robot. Using measurements from cameras and infrared range sensors, a temporary local map is built probabilistically with Gaussian processes and adaptively to the change of the route curvature. Based on the probabilistic map, RRT searches the most robust and efficient local path with the probability of collision, and the robot is controlled along the selected path. The performance of the proposed method was verified by reducing not only centering error and standard deviation in simulations but also travel time in real experiments.

This paper deals with the stealth target-interception problem based on the active prediction planning execution (APPE) strategy. In order to cope with the evasive feature of the invader, we make the robot move stealthily by hiding behind obstacles. We adopt a roadmap-based decoupled approach, which plans the path and the trajectory separately by developing new concept; the detection map. Simulation results demonstrate the efficiency of the proposed algorithm. It has an enough practical running time to be applied to the real-time situation.

In order to improve the efficiency of a terrain covering algorithm for a robot (AUV), an artificial island(AI) technique is developed. Such an algorithm is necessary to acquire information and make a terrain map in an unknown three dimensional underwater environment. This algorithm considers that the three dimensional environment consists of a number of planes, at various ocean depths. The artificial island technique has the advantage of reducing the covering path length and the time cost for the robot, because it takes the correlation between every plane. In this paper, the concept of the artificial island technique is presented and its validity is proved under certain conditions. Through various simulations, we validate the efficiency in terms of the total path length and the running time of the AUV. An example of a complete three dimensional map obtained using this technique is provided.

This paper proposes a novel path planning method for cleaning task using multiple robots in large environment. To do so, we suggested algorithm which partitions a given region into several smaller regions and plans the covering path which can completely cover these divided areas. The algorithm also allocates the planed areas to cleaning robots sequentially and generates the paths which robots can take, when moving from an area to another area, in the shortest time. For partitioning the given region into several small areas, the Virtual Door Algorithm is used, and for planning the paths, the template-based approach is used. Previously generated look-up table, which uses the Dijkstra algorithm, is used for determining the path from one are to another. The Task Area Allocation Algorithm, which allocates the divided areas to cleaning robots using look-up table, is used for planning paths that cover the area completely. Finally, we evaluated the performance of our algorithms which can completely cover the given region regardless of the number of cleaning robots and verify the effectiveness of our proposed algorithms through computer simulations.

FastSLAM is a framework which solves the problem of simultaneous localization and mapping using a Rao-Blackwellized particle filter. Conventional FastSLAM is known to degenerate over time in terms of accuracy due to the particle depletion in resampling phase. In this work, a new FastSLAM framework is proposed to prevent the degeneracy by particle cooperation. First, after resampling phase, a target that represents an estimated robot position is computed using the positions of particles. Then, particle swarm optimization is performed to update the robot position by means of particle cooperation. Computer simulations revealed that the proposed framework shows lower RMS error in both robot and feature positions than conventional FastSLAM.

FastSLAM has been shown to degenerate over time due to sample impoverishment, that is, poor samples are generated during the sampling process. One of major culprits of the sample impoverishment problem is lack of the number of particles estimating the pose of the robot and the environment. In this work, an adaptive prior boosting technique is proposed for the efficient sample size according to the uncertainty of each situation in performing FastSLAM. It uses a back-propagation neural network, learned in various environments, in order to decide the required sample size. This adaptive approach generates a small number of particles when the uncertainty is low while performing FastSLAM, and it generates a large number of particles when the uncertainty is high. This technique efficiently generates the sample size in computer simulations and real environmental experiments, which significantly reduces the RMS feature and position errors.

The state-of-the-art FastSLAM has been shown to cause a particle depletion problem while performing simultaneous localization and mapping for mobile robots. As a result, it always produces over-confident estimates of uncertainty as time progresses. This particle depletion problem is mainly due to the resampling process in FastSLAM, which tends to eliminate particles with low weights. Therefore, the number of particles to perform loop-closure decreases, which makes the performance of FastSLAM degenerate. The resampling process has not been thoroughly analyzed even though it is the main reason for the particle depletion problem. In this paper, standard resampling algorithms (systematic residual and partial resampling), and a rank-based resampling applying genetic algorithms are analyzed using computer simulations. For the thorough analysis, several performance measures are used such as effective sample size, the number of distinct particles, root mean square (RMS) errors, and complexity. According to the simulation results, all resampling algorithms could not resolve the particle depletion problem.

This paper introduces a navigation method for a teleoperated mobile agent (or robot) moving in an unstructured environment that includes unknown obstacles and uneven terrain, based on a guided-navigation algorithm (GNA) and a rollover-prevention algorithm (RPA). Although the mobile agent is primarily driven by an operator at a remote site, it reacts autonomously for avoiding collision with obstacles and for preventing rollover when it suspects/detects possible collision or rollover. The autonomous reactive motion is normally unexpected, thus there exists the inconsistency between the intended motion and the controlled motion of the agent from the operator. A force-reflection technique utilizing a force-feedback joystick is developed to manipulate this inconsistency. To verify the feasibility and effectiveness of the proposed navigation method, experiments with the Robot for Hazardous Application-Double Tracks (ROBHAZ-DT) (actual mobile agent) are successfully carried out.

We propose a low cost, accurate and safe active range sensor with a halogen sheet-of-light and a USB camera. We also propose an algorithm suitable for building a local map using the probabilistic sensor model. In this paper, the inexpensive structured light source that uses tungsten-halogen lamp is used, and an efficient method of detecting the sheet-of-light by the camera is proposed. A local map is built using the spatial occupancy technique which is inputs values in an occupancy grid that reflect the probability of occupancy for each cell. In addition, a suitable probabilistic sensor model is employed to build the local map. The experimental results show the validity of the proposed range sensor and the algorithm to build a map using the probabilistic sensor model.

We propose an efficient tracking algorithm for uncalibrated visual tracking system based on image-based visual servo control. We present an efficient algorithm to track a target using a numerical method and a method to regulate the joint velocities for the stability of the system efficiently. The robot system is controlled using the nonlinear least squares optimization technique. The full Newton's method and the secant approximation method are used to calculate the joint angles. Large residuals of joint values are calculated using the secant approximation method. We define a secondary cost function to regulate the joint velocities using the adaptive limit of joint velocity, and propose the modified iterative forms of the joint values. The image Jacobian is then estimated using the recursive least squares (RLS) algorithm. We then apply this method in a visual servoing application.

This work presents a new practical environment modeling for mobile robots in an unknown circumstance. To perform environment modeling, obstacles located in the circumstance should be detected using sensor data. Then some sensor fusion methods are used to model environment better. Environment modeling shown In this work is based on the heuristic sensor fusion method. In this method, we define a segment as one ultrasonic sensor and two adjacent infrared sensors. Especially, infrared sensors are used to reduce defects of ultrasonic sensor such as poor directionality and frequent misreading. Whether infrared sensors of a segment detect obstacles or not, ultrasonic sensor data are modified. To evaluate the usefulness of the sensor fusion method, we used semicircular sensor module composed of 6 ultrasonic sensors and 7 infrared sensors. With this module, several experiments in indoor environment are performed.

In this paper, a reflective force generation method of a force feedback joystick is suggested for a nonautonomous mobile robot maneuvered by a teleoperator. Three types of the reflective forces are defined such as obstacle detection, obstacle avoidance and robot posture recognition forces. To generate the reflective force for obstacle detection and avoidance, range data from ultrasonic sensors of the robot are used. Using these data, wall and center following of the robot are implemented by a fuzzy logic controller for obstacle avoidance. Next, the reflective force for robot posture recognition is generated using a gyro sensor of the robot. Thus the operator can not only maneuver the robot without collisions but also recognize the existence of obstacles and the posture of the robot by the reflective forces while driving the robot. Then, to determine the type of the reflective forces according to robot and obstacle states a state transition diagram is suggested. Finally, the experimental and simulation results with the mobile robot, ROBHAZ-DT developed by KIST show the effectiveness of the suggested method.

This paper describes a control method of mobile robots for avoiding slip and turnover in sloped terrain. An inexpensive vision/gyro sensor module that consists of a laser line generator, a USB camera and a gyro sensor, is suggested for obtaining terrain information nearby the robot system. Using information of the terrain and the robot state, the maximum limit of the forward velocity of the robot is defined for avoiding slip and turnover. Simultaneously the maximum value of the robot velocity is reflected to an operator in the form of a reflective force. Consequently the operator can recognize the maximum velocity of the robot determined by terrain information and the robot state. In this point of view, the inconsistency between the robot state and the user's command can be compensated by the reflective force. The experimental results show the effectiveness of the suggested method.

This paper addresses a moving target tracking system with estimation of the image Jacobian without knowledge of camera configuration or robot kinematics. We propose an efficient algorithm to track a moving target using a numerical method and apply this algorithm to a robot system. The robot system is controlled using the nonlinear least squares optimization technique. The Full Newton's method and the secant approximation method are used to calculate joint angles. In this paper, large residuals of joint values are calculated using the secant approximation method. The image Jacobian is then estimated using the recursive least squares (RLS) algorithm. In addition, the velocity of the target influences the performance of the system because of the delay to process an image. Thus, we present a motion prediction algorithm for a moving target. The target position on the image plane is predicted using the autoregressive model (ARM). We then compare the performance of the small residual case with the performance of the large residual case using simulation. The experimental results show the improved performance using this prediction algorithm.

In this paper, a real-time obstacle avoidance method is discussed for a remote mobile robot controlled by a teleoperator. The method enables the remote user to drive the mobile robot without collisions. It consists of a real-time environment modeling algorithm using ultrasonic sensors, and a mobile robot control algorithm for obstacle avoidance. The former solves the limitations of the ultrasonic sensors such as noise sensitivity, poor directionality, and specular reflection for obtaining more accurate sensor data. Then the latter conducts wall or center following implemented by a fuzzy controller for obstacle avoidance using the environment model obtained by the previous algorithm. The command for obstacle avoidance is actually applied as a reflective force using a haptic device such as a force feedback joystick. The remote mobile robot, ROBHAZ-DT(RoBot in HAZardous environment-Double Track) developed by KIST(Korea Institute of Science and Technology), is employed as the mobile robot model. The normal and the maximum speed of the ROBHAZ-DT are 3.1 km/h and 7.2 km/h, respectively. The weight of the robot is 50 kg. Simulations with the ROBHAZ-DT model are carried out to verify the performance of the proposed method.

The dynamic stability of a mobile manipulator using ZMP compensation is considered. A unified approach for the two subsystems is formulated using a redundant scheme. First, to conserve the dynamic stability of the system, we define the performance index for the redundant system using the ZMP (zero moment point). This performance index represents the stability of the whole mobile manipulator system. Then, the redundancy resolution problem for optimizing the given performance index is solved using the null motion. Finally, the performance of this method is demonstrated by simulation study.

A new control method for kinematically redundant manipulators having the properties of local torque-optimality and singularity-robustness is developed. The proposed control method is applied to the numerical model of the SNU-ERC 3-DOF direct-drive manipulator. It is shown that the proposed control method is very effective in overcoming the singularity problem, especially in the case of using the local torque optimization control of redundant manipulators.

A robotic work cell with multiple robots can accomplish complex tasks that a single robot cannot manage. Robots in the work cell cooperate with each other, or compete for shared motion path, resources and workspace. So coordinating multiple robots is essential for designing a robotic work cell. We present a logic model of a robotic work cell to build controllers that meet required behavior, and construct a performance model to measure the performance of controllers. We verify the presented model with an experimental multi-robot work cell.

This paper addresses the problem of stabilizing a base-excited inverted pendulum around its upright position with a robot. The pendulum has two rotational degrees of freedom and its base point is moved in two-dimensional space by a planar direct-drive robot. The robot has three degrees of freedom and its kinematic redundancy occurring at the inverse kinematics is utilized to optimize the joint acceleration and the manipulability measure. A decentralized joint acceleration control with a software velocity/acceleration observer, is used to accelerate/decelerate each joint of the robot to balance the pendulum. Experimental results are described to show the stable and robust responses of the control system.

This paper considers the problem of minimizing the assembly time of multi-head surface mounting machines. The problem is decomposed into a hierarchy of related subproblems. Since all subproblems in the hierarchy are known to be of a combinatorial nature and computationally intractable, we develop heuristics which are based on dynamic programming and the nearest neighbour travelling salesman problem technique. We implement the method as a computer program and perform computer simulations. The results are compared to a heuristic that is currently in use.

This paper addresses the problem of stabilizing a base-excited inverted pendulum around its upright position with a robot. The pendulum has two rotational degrees of freedom and its base point is moved in two-dimensional space by a planar direct-drive robot. The robot has three degrees of freedom and its kinematic redundancy occurring at the inverse kinematics is utilized to optimize the joint acceleration and the manipulability measure while balancing the pendulum. A decentralized joint acceleration control with a software velocity/acceleration observer, is used to accelerate/decelerate each joint of the robot to balance the pendulum. Experimental results are described to show the stable and robust responses of the control system.

As a practical application, we focus on the systematic genetic algorithm for a multi-head surface mounting machine. The multi-head surface mounting machine is becoming increasingly popular due to its merit that the mounting speed is high though the price is low. Since the scheduling problem for these machines is known to be of a combinatorial nature and NP-hard, no exact algorithm has been proposed. A genetic algorithm (GA) has been recognized as an efficient and useful procedure for solving large combinatorial optimization problems. The paper proposes to use a GA to solve the scheduling problem for the multi-head surface mounting machine. The result of computer simulation shows that the proposed method performs better than the conventional methods.

In redundancy optimization problems related to cooperating manipulators such as optimal force distribution, constraints on the physical limits of the manipulators should be considered. We propose quadratic inequality constraints (QICs), which lead to ellipsoidal feasible regions, to solve the optimization problem more efficiently. We investigate the effect of the use of QICs from the points of view of problem size and change of the feasible region. To efficiently deal with the QICs, we also propose the dual quadratically constrained quadratic programming (QCQP) method. In this method, the size of the optimization problem is reduced so that the computational burden is lightened. The proposed method and another well-known quadratic programming method are applied to the two PUMA robots system and compared with each other. The results show that the use of QICs with the dual QCQP method allows for faster computation than the existing method.

In redundancy optimization problems related to cooperating manipulators, constraints should be considered for physical limits of the manipulators. The constraints have been imposed mostly in the form of linear inequality constraints, which lead to polyhedric feasible regions. We propose quadratic inequality constraints (QICs) which lead to ellipsoidal feasible regions to solve the optimization problem faster and to directly handle constraints on quadratic quantities. We investigate the effect of the use of QICs from the points of view of problem size and change of the feasible region. In order to efficiently deal with QICs, we utilize the dual quadratically constrained quadratic programming (QCQP) method. The proposed scheme and another well-known quadratic programming method are applied to numerical examples and compared with each other. The results show that the use of QICs makes it possible to make trade-off between optimality and fast computation capability and implements faster computation than the existing method.

A two-staged traffic control scheme, in which sets of candidate paths are prepared off-line prior to overall motion planning process, has been widely adopted for motion planning of mobile robots, but relatively little attention has been given to the application of the two-staged scheme to multiple automated guided vehicle systems (MAGVSs). In the paper, a systematic two-staged traffic control scheme is presented to obtain collision-free minimum-time motions of AGVs along loopless paths. The overall structure of the controller is divided into two tandem modules of off-line routing table generator (RTG) and an online traffic controller (OTC). First, an induced network model is established considering the configurational restrictions of guide-paths. With this model and a modified k-shortest path algorithm, RTG finds sets of k candidate paths from each station nodes to all the other station nodes off-line and stores them in the form of routing tables. Each time a dispatch command for an AGV is issued, OTC utilizes these routing tables to generate a collision-free minimum-time motion along a loopless path. Real-time computation is guaranteed in that the time-consuming graph searching process is executed off-line by RTG, and OTC looks for the minimum time motion among the k candidate paths. The traffic control scheme proposed is suitable for practical application in centralized MAGVS with zone blocking technique.

The concept of dynamic tracking line is proposed as the feasible tracking region for a robot in a robot-conveyor system, which takes the conveyor speed into consideration. This paper presents an effective method to find the dynamic tracking line in a robotic workcell. The maximum permissible line-speed which is a quantitative measure of the robot capability for conveyor tracking, is defined on the basis of the relation between the end-effector speed and the bounds on the joint velocities, accelerations, and torques. This measure is derived in an analytic form using the parameterized dynamics and kinematics of the manipulator, and some of its properties are established mathematically. The problem of finding the dynamic tracking line is then formulated as a root-solving problem for a single-variable equation, and solved by the use of a simple numerical technique. Finally, numerical examples are presented to demonstrate the methodology and its applications in workspace specification.

Concerns a sawing task by two cooperating arms. To follow a line in a horizontal plane, three directional motions must be controlled, 2 translational and 1 rotational. A certain level of force must be controlled in the vertical direction, to keep contact with the object to be sawn. The two-arm system consists of a 4-DOF SCARA robot and a 5-DOF PT200V robot. When the two arms are rigidly grasping a saw, the mobility of the system is 3, which is not enough. Therefore, we deliberately insert a passive joint at the end of the SCARA robot to increase the mobility to 4. A hybrid control method to regulate force and position is proposed. The proposed scheme has three typical features: First, the two arms are treated as one in a kinematic viewpoint. Second, the hybrid control method is based on a Jacobian and an internal kinematics for a single closed-kinematic chain of the two arms to reflect the nature of the position-controlled manipulator. Third, the proposed scheme is not only able to operate the system even if a passive joint exists, but also is able to utilize the internal loads for useful applications such as pitch motion control. We experimentally show that the performance of the position and force response are satisfactory, and that one additional passive joint not only prevents the system from unwanted roll motion in the sawing task, but also allows an unwanted pitch motion to be notably reduced.

The redundancy of robot manipulators plays an important role in increasing their flexibility and versatility, especially in an environment with obstacles. In this paper, the redundancy is utilized for enabling collision-free motion for a given trajectory of a manipulator end-effector. Manipulator links and obstacles are approximated by polytopes that are described by their vertices. The distance algorithm, which allows computation of the distance information between polytopes in R/sup m/, is used to obtain distance information between robot and obstacles. Three kinds of method based on the distance information-gradient projection method, obstacle avoidance force method, obstacle avoidance velocity method-are proposed to calculate the joint trajectory that enable a manipulator to perform a desired end-effector motion and avoid obstacles. A simple numerical example involving a two-dimensional five degree-of-freedom manipulator working among obstacles is described.

This paper considers the problem of minimizing the time required to populate a printed circuit board using a surface mounting machine. The total assembly time depends on two optimization problems; reel assignment problem (RAP) and pick-and-place sequencing problem (PAPSP). We formulate RAP as a quadratic integer programming problem and PAPSP as an asymmetric travelling salesman problem. Since these problems are NP-hard, we focus on the development of heuristics that yield near optimal solutions in a reasonable time. The implementation of these methods have achieved an average saving of 31% over randomly generated sequences in total assembly time.

For PD-controlled industrial robot manipulators performing point-to-point (PTP) motion, the effect of joint flexibility grows as the cycle time for the PTP motion increases. To reduce residual vibration induced by joint flexibility, a method of generating preshaped reference trajectory command is proposed. This preshaped reference trajectory command is developed from the given PTP motion and the parameters of given PD controller. Simulations show that low overshoot and short settling time in fast PTP motion is achieved by applying the preshaped trajectory reference command, without excessive control effort. This proposed method is readily applicable to PD-controlled industrial robots which perform repetitive fast PTP motions.

The concept of dynamic tracking line is proposed as the feasible tracking region for a robot in a robot-conveyor system, which takes the conveyor speed into consideration. This paper presents an effective method to find the dynamic tracking line in a robotic workcell. The maximum permissible line-speed which is a quantitative measure of the robot capability for conveyor tracking, is defined on the basis of the relation between the end effector speed and the bounds on the joint velocities, accelerations, and torques. This measure is derived in an analytic form using the parameterized dynamics and kinematics of the manipulator, and some of its properties are established mathematically. The problem of finding the dynamic tracking line is then formulated as a root-solving problem for a single-variable equation, and solved by the use of a simple numerical technique. Finally, numerical examples are presented to demonstrate the methodology and its applications in workspace specification.

An optimal force distribution scheme of multiple cooperating robots is proposed. General formulations of the optimal force distribution problem are built into quadratic programming with linear equality and quadratic inequality constraints. The quadratic constraints are due to the norm constraints of grasping forces and the approximation of maximum joint torque constraints which is used for simplification of the problem. This paper presents a technique for solving these general optimal force distribution problems. The original problem is transformed into a compact problem by eliminating the linear equality constraints. Then, the compact problem is transformed into a dual problem to be solved by using Lagrange multipliers and nonlinear programming dual method. This technique reduces the problem size considerably and makes the problem almost unconstrained so that no initial feasible points are needed. These features exhibit the efficiency of this technique. Simulation results for two cooperating PUMA robots indicating the ability of real time applications of the proposed technique are presented.

This paper proposes a new motion planning method of a mobile robot avoiding moving obstacles. To avoid moving obstacles, the trajectories of the obstacles are predicted using a stochastic model of obstacle motion. The obstacle motion is modeled as a random walk process. The method plans robot motion by the unit of view-time and view-period. View-time is defined as the time instant at which the robot senses the obstacle position and velocity. View-period is defined as the time interval during which the robot performs sensing, predicting and planning for collision-free motion. From the position and velocity at a view-time, we predict the future position of the obstacle. The random walk process model of obstacle motion is used to calculate the probability density that the predicted position is reached during the view-period. From the probability density function of the predicted position, the probability that a position can be swept by the obstacle during the view-period is calculated. Then artificial potential is assigned at every position by considering the probability. The force induced by the artificial potential field repels the robot away from the probable obstacle trajectory. This method is a look ahead scheme, and effective for moving obstacle avoidance. This method is applied to collision-free motion planning for a mobile robot in a dynamic and unknown environment with several moving and stationary obstacles.

This paper presents a new solution approach to moving obstacle avoidance problem of a robot. A new concept avoidability measure (AVM) is defined to describe the state of a pair of a robot and an obstacle regarding the collision between them. As an AVM, virtual distance function (VDF) is derived as a function of the distance from the obstacle to the robot and outward speed of the obstacle relative to the robot. By keeping the virtual distance above some positive limit value, the robot avoids the obstacle. In terms of the VDF, an artificial potential field is constructed to repel the robot away from the obstacle and to attract the robot toward a goal location. At every sampling time, the artificial potential field is updated and the force driving the robot is derived from the gradient of the artificial potential field. The suggested algorithm drives the robot to avoid moving obstacles in real time. Since the algorithm considers the mobility of the obstacle as well as the distance, it is effective for moving obstacle avoidance. Some simulation studies show the effectiveness of the proposed approach.

This paper presents a design experience of a supervisory control system for coordination of multiple robotic devices. To effectively program job commands, a Petri net-type graphical robot language(PGRL) is proposed, where some functions for coordination among tasks such as concurrency and synchronization, can be easily programmed. Each task of PGRL is described by employing formal model languages, which are composed of three modules, sensory, data handling, and action module. It is expected that by using our proposed PGRL and formal model languages, one can efficiently describe a job or task, and hence can easily operate a complex real-time concurrent system. The proposed control system has been implemented by using VME-based 32-bit microprocessor boards and a real-time multitasking operating system (VxWorks), and is shown to successfully work for robotic jobs.

This paper deals with a systematic analysis of the time-optimal motion of a multiple robot system carrying an object along a prescribed path. In our approach, the time-optimal motion planning problem is formulated in a concise form by employing a parameter describing the movement along the path and a vector representing the internal force. Various constraints governing the motion yield the so-called admissible region in the phase plane of the parameter. The orthogonal projection technique and the theory of multiple objective optimization make it possible to construct the admissible region, while taking into account the load distribution problem that is coupled with the motion. Furthermore, our approach provides a way of detailed investigation for the admissible region that is not simply connected. The resulting velocity profile of the path parameter and the internal force at every instant determine the optimal actuator torques for each robot.

An efficient solution algorithm of the optimal load distribution problem with joint torque constraints is presented. A multiple robot system where each robot is rigidly grasping a common object is considered. The optimality criterion used is the sum of weighted norm of the joint torque vectors. The maximum and minimum bounds of each joint torque in arbitrary form are considered as constraints, and the solution that reduces internal force to zero is obtained. The optimal load distribution problem is formulated as a quadratic optimization problem in R/sup l/, where l is the number of robots. The general solution can be obtained using any efficient numerical method for quadratic programming, and in particular, for dual robot case, the optimal solution is given in a simple and efficient closed form so that the solution scheme can easily be utilized in the real time control of the dual robot coordination.

With rapid development of the surface mounting technology (SMT), continual efforts have been made to make the surface mounting machines efficient as well as reliable. The authors propose an algorithmic approach to the improvement of efficiency for surface mounting machines. For minimizing the set-up time, a CAD data conversion algorithm, which makes the data for the pick-and-place sequences from the CADSTAR output files, is proposed. Also, for optimizing the feeder arrangement and the traveling path of heads in the surface mounting machine, SCM 130 model, effective feeder arrangement algorithms are proposed. To evaluate their performance, the proposed algorithms are tested on the assembly tasks of PCBs manufactured through simulations. As a result, it is shown that the proposed algorithm reduces the time required to perform the assembly sequences by maximum 9 percent when the task time is compared with the time required to perform the assembly sequences made by an expert.

The conflict-free minimum-time motion planning problem in a multiple AGV system using zone control requires a traffic control scheme based upon a non-search technique, since the number of nodes contained in the network model for its guidepath is considerably large. The traffic control scheme presented in this paper partitions the overall task into two constituent tasks of path generation and traffic control. The Path Generator employs a k-shortest path search algorithm to construct path sets between all possible station node pairs. The path generation process is performed in off-line. Occupation times for each link are calculated and stored in a link occupation table which is represented from a link-oriented viewpoint. The traffic controller examines the link occupation table and dispatches AGVs in a conflict-free minimum-time path. The on-line motion planning operation is performed in real time.

The concept of dynamic tracking line is proposed as the feasible tracking region for a robot in a robot-conveyor system, which takes the variable conveyor speed into consideration. This paper presents an effective method to find the dynamic tracking line in a robotic workcell. The maximum permissible line-speed which is a quantitative measure of the robot capability for conveyor tracking, is defined on the basis of the relation between the end-effector speed and the bounds on the joint velocities, accelerations, and torques. This measure is derived in an analytic form, and some of its properties are established mathematically. The problem of finding the dynamic tracking line is then formulated as a root-solving problem for a single-variable equation, and solved by the use of a simple numerical technique.

This paper proposes a unified method for moving obstacle avoidance of a robot. The method incorporates the artificial potential field (APF) concept into view-time based motion planning where the driving force is generated at every interval of the view-time. The view-time is defined as the time set from one sampling time instant to the next. The velocity and acceleration of the moving obstacle is assumed to be monitored or priorly known at each sampling time. At each sampling time, an accessible region that will be swept by the obstacle in the next view-time is predicted from the velocity, acceleration, and dynamic constraints of the obstacle. Then, an APF which exerts repulsive force on the robot is constructed around the accessible region. During the view-time, the force induced by the artificial potential field drives the robot away from the accessible obstacle trajectories in real-time. The dynamic constraints of the robot are also considered. Application of the described procedure at each successive sampling time from the initial to final location yields the collision-free trajectory for moving obstacle avoidance.

This paper deals with a systematic analysis of the time-optimal motion of a multiple robot system carrying an object along a prescribed path. In our approach, the time-optimal motion planning problem is formulated in a concise form by employing a parameter describing the movement along the path and a vector representing the internal force. Various constraints governing the motion yield the so-called admissible region in the phase plane of the parameter. The orthogonal projection technique and the theory of multiple objective optimization make it possible to construct the admissible region, while taking into account the load distribution problem that is coupled with the motion. Furthermore, our approach provides a way of detailed investigation for the admissible region that is not simply connected. The resulting velocity profile of the path parameter and the internal force at every instant determine the optimal actuator torques for each robot. Computer simulation results reinforce the physical interpretation on the characteristics of the optimal actuator torques.

In this paper, a fuzzy force control algorithm is suggested for commercialized industrial robots equipped with the position servo drives, where control rules of the proposed fuzzy controller are changed according to the magnitude of environmental stiffness in such a way that good force response is maintained regardless of changes of environmental stiffness. Specifically, some fuzzy control rules are designed for several representative environmental stiffness values, and then a control action for a given arbitrary environmental stiffness value is decided by a fuzzy interpolation method. To show the validity of the proposed fuzzy controller, several experimental results are illustrated, where a 5-axis articulated robot manipulator equipped with the wrist force/torque sensor system and our prototype dual robot controller are employed.>

A simple time delay method for avoiding collisions between two general robot arms is proposed. Links of the robots are approximated by polyhedra and the danger of collision between two robots is expressed by distance functions defined between the robots. The collision map scheme, which can describe collisions between two robots effectively, is adopted. The minimum delay time value needed for collision avoidance is obtained by a simple procedure of following the boundary contour of collision region on collision map. To demonstrate the effectiveness of the proposed time delay method, a computer simulation study is shown where a collision is likely to occur realistically.>

The optimal load distribution for two cooperating robots is studied, and a solution approach utilizing a force ellipsoid is presented. The load distribution problem is formulated as a nonlinear optimization problem with a quadratic cost function. The effort exerted by the robots to follow the specified motion is defined to be the joint torque norm square, and the optimal solution minimizing the effort is obtained using the concept of the force ellipsoid and nonlinear optimization theory. Despite the presence of the joint torque constraints, the optimal solution is obtained almost as a closed form which requires small computation time. The proposed solution approach is illustrated and the internal force effect is studied using a numerical example.>

The robot motion is analyzed and planned for conveyor tracking considering the speed of the conveyor belt and the location of the part and the robot. The joint torque limit, together with the joint velocity, acceleration, and jerk limits of the robot, is also taken into account in the motion analysis and planning. To include the robot arm dynamics, the problem is formulated as second-order state equations using a parametric function. The conveyor tracking problem is then converted to an optimal tracking problem. The solution that minimizes the specified performance index is obtained using the dynamic programming approach. Numerical examples are finally presented to demonstrate the significance of the proposed method for conveyor tracking of the robot in a workcell.>

Robot motion is analyzed and planned for conveyor tracking considering the speed of the conveyor belt and the locations of the part and the robot. The joint torque limit, joint velocity, acceleration, and jerk limits of the robot are taken into account in the motion analysis and planning. To include the robot arm dynamics, the problem is formulated as second order state equations using a parametric function. The conveyor tracking problem is then converted to an optimal tracking problem. The solution that minimizes the specified performance index is obtained using the dynamic programming approach. Numerical examples are presented to demonstrate the significance of the proposed method for conveyor tracking of the robot in a workcell.>

The time-varying obstacle avoidance problem is considered mathematically. The manipulator motion is described in terms of constrained motions, which are classified into two categories: the environmental constraints and the manipulator constraints. The constraints are converted into the reachable path segment at each servo time instant to verify the existence of a collision-free trajectory.>

This paper addresses an overview of time-varying obstacle avoidance problems for robotic manipulators. We propose four different approaches to solving these problems. They are the Heuristic Off-line (HOF) approach, the Heuristic On-line (HON) approach, the Analytic Off-line (AOF) approach, and the Analytic On-line (AON) approach. The AOF approach is particularly pursued and derived in this paper. Some fundamental difficulties are then discussed in the AOF approach. It is shown that the analytic approach is not always successful in solving the time-varying obstacle avoidance problems.

This paper presents a new dynamic model for robot manipulators which is called the Generalized Newton equations of motion (G-N). The model is developed from the Newton's second law. The joint acceleration of the manipulator is found to be inversely proportional to the generalized Newton inertia of the corresponding link while it is directly proportional to the generalized Newton torque of the joint. The external torque contribution for the movement of a specific joint is determined by the developed "structural matrix [λ]" of the manipulator. The model also addresses the controllability issue of the robot system.

An approach to collision-free motion planning of two moving robots in a common workspace is presented. Each robot is represented by a sphere containing the wrist and the manipulator hand. The results from a strictly straight line trajectory planning method are utilized for planning a path avoiding potential collisions. Due to the distinct nature of the potential collisions between the two moving robots, a new classification of path requirement situations is presented and utilized for planning a collision-free path. Notions of collision map and time scheduling are developed and applied for realizing a collision-free motion planning. A procedure is developed for the time scheduling of the straight line trajectory. An example is shown for the time scheduling of the trajectory, which shows the significance of the proposed approach in collision-free motion planning of the two moving robots.

The development of generalized d'Alembert equations of motion for application to robot manipulators with rotary joints is presented. These equations result in an efficient and explicit set of second-order nonlinear differential equations with vector cross-product terms in symbolic form. They give well-"structured" equations of motion suitable for state-space control analysis. The interaction and coupling reaction forces/torques between the neighboring joints of a manipulator can be easily identified as coming from the translational and rotational effects of the links. An empirical method for obtaining a simplified dynamic model is discussed together with the computational complexity of the dynamic coefficients in the equations of motion. The dynamic equations of the first three links of a Pumas robot are derived to illustrate the simplicity of the generalized d'Alembert equations of motion.

This paper presents the study of an adaptive control which tracks a desired time-based trajectory as closely as possible for all times over a wide range of manipulator motion and payloads both in joint-variable coordinates and Cartesian coordinates. The proposed adaptive control is based on the linearized perturbation equations in the vicinity of a nominal trajectory. The controlled system is characterized by feedforward and feedback components which can be computed separately and simultaneously. The feedforward component computes the nominal torques from the Newton-Euler equations of motion either using the resolved joint information or the joint information from the trajectory planning program. This computation can be completed in O(n) time. The feedback component consisting of recursive least square identification and one-step optimal control algorithms for the linearized system computes the variational torques in O(n3) time. Because of the parallel structure, the computations of the adaptive control may be implemented in low-cost microprocessors. A computer simulation study was conducted to evaluate the performance of the adaptive control in joint-variable coordinates for a three-joint robot arm. The feasibility of implementing the adaptive control in Cartesian coordinates using present day low-cost microprocessors is discussed.