Showing papers on "Robot kinematics published in 2010"

Unfreezing the robot: Navigation in dense, interacting crowds

Abstract: In this paper, we study the safe navigation of a mobile robot through crowds of dynamic agents with uncertain trajectories. Existing algorithms suffer from the “freezing robot” problem: once the environment surpasses a certain level of complexity, the planner decides that all forward paths are unsafe, and the robot freezes in place (or performs unnecessary maneuvers) to avoid collisions. Since a feasible path typically exists, this behavior is suboptimal. Existing approaches have focused on reducing the predictive uncertainty for individual agents by employing more informed models or heuristically limiting the predictive covariance to prevent this overcautious behavior. In this work, we demonstrate that both the individual prediction and the predictive uncertainty have little to do with the frozen robot problem. Our key insight is that dynamic agents solve the frozen robot problem by engaging in “joint collision avoidance”: They cooperatively make room to create feasible trajectories. We develop IGP, a nonparametric statistical model based on dependent output Gaussian processes that can estimate crowd interaction from data. Our model naturally captures the non-Markov nature of agent trajectories, as well as their goal-driven navigation. We then show how planning in this model can be efficiently implemented using particle based inference. Lastly, we evaluate our model on a dataset of pedestrians entering and leaving a building, first comparing the model with actual pedestrians, and find that the algorithm either outperforms human pedestrians or performs very similarly to the pedestrians. We also present an experiment where a covariance reduction method results in highly overcautious behavior, while our model performs desirably.

Robot motor skill coordination with EM-based Reinforcement Learning

Abstract: We present an approach allowing a robot to acquire new motor skills by learning the couplings across motor control variables. The demonstrated skill is first encoded in a compact form through a modified version of Dynamic Movement Primitives (DMP) which encapsulates correlation information. Expectation-Maximization based Reinforcement Learning is then used to modulate the mixture of dynamical systems initialized from the user's demonstration. The approach is evaluated on a torque-controlled 7 DOFs Barrett WAM robotic arm. Two skill learning experiments are conducted: a reaching task where the robot needs to adapt the learned movement to avoid an obstacle, and a dynamic pancake-flipping task.

WiFi localization and navigation for autonomous indoor mobile robots

Abstract: Building upon previous work that demonstrates the effectiveness of WiFi localization information per se, in this paper we contribute a mobile robot that autonomously navigates in indoor environments using WiFi sensory data. We model the world as a WiFi signature map with geometric constraints and introduce a continuous perceptual model of the environment generated from the discrete graph-based WiFi signal strength sampling. We contribute our WiFi localization algorithm which continuously uses the perceptual model to update the robot location in conjunction with its odometry data. We then briefly introduce a navigation approach that robustly uses the WiFi location estimates. We present the results of our exhaustive tests of the WiFi localization independently and in conjunction with the navigation of our custom-built mobile robot in extensive long autonomous runs.

Body Schema in Robotics: A Review

Abstract: How is our body imprinted in our brain? This seemingly simple question is a subject of investigations of diverse disciplines, psychology, and philosophy originally complemented by neurosciences more recently. Despite substantial efforts, the mysteries of body representations are far from uncovered. The most widely used notions-body image and body schema-are still waiting to be clearly defined. The mechanisms that underlie body representations are coresponsible for the admiring capabilities that humans or many mammals can display: combining information from multiple sensory modalities, controlling their complex bodies, adapting to growth, failures, or using tools. These features are also desirable in robots. This paper surveys the body representations in biology from a functional or computational perspective to set ground for a review of the concept of body schema in robotics. First, we examine application-oriented research: how a robot can improve its capabilities by being able to automatically synthesize, extend, or adapt a model of its body. Second, we summarize the research area in which robots are used as tools to verify hypotheses on the mechanisms underlying biological body representations. We identify trends in these research areas and propose future research directions.

Inverse dynamics control of floating base systems using orthogonal decomposition

Abstract: Model-based control methods can be used to enable fast, dexterous, and compliant motion of robots without sacrificing control accuracy. However, implementing such techniques on floating base robots, e.g., humanoids and legged systems, is non-trivial due to under-actuation, dynamically changing constraints from the environment, and potentially closed loop kinematics. In this paper, we show how to compute the analytically correct inverse dynamics torques for model-based control of sufficiently constrained floating base rigid-body systems, such as humanoid robots with one or two feet in contact with the environment. While our previous inverse dynamics approach relied on an estimation of contact forces to compute an approximate inverse dynamics solution, here we present an analytically correct solution by using an orthogonal decomposition to project the robot dynamics onto a reduced dimensional space, independent of contact forces. We demonstrate the feasibility and robustness of our approach on a simulated floating base bipedal humanoid robot and an actual robot dog locomoting over rough terrain.

Kinematic-Parameter Identification for Serial-Robot Calibration Based on POE Formula

Abstract: This paper presents a generic error model, which is based on the product of exponentials (POEs) formula, for serial-robot calibration. The identifiability of parameters in this error model was analyzed. The analysis shows the following: 1) Errors in all joint twists are identifiable. 2) The joint zero-position errors and the initial transformation errors cannot be identified when they are involved in the same error model. With either or neither of them, three practicable error models were obtained. The joint zero-position errors are identifiable when the following condition is satisfied: Coordinates of joint twists are linearly independent. 3) The maximum number of identifiable parameters is 6n + 6 for an n-degree-of-freedom (DOF) generic serial robot. Simulation results show the following: 1) The maximum number of identifiable parameters is 6r + 3t + 6, where r is the number of revolute joints, and t is the number of prismatic joints. 2) All the kinematic parameters of the selective compliant assembly robot arm (SCARA) robot and programmable universal machine for assembly (PUMA) 560 robots were identified by using the three error models, respectively. The error model based on the POE formula can be a complete, minimal, and continuous kinematic model for serial-robot calibration.

A Communication Robot in a Shopping Mall

Abstract: This paper reports our development of a communication robot for use in a shopping mall to provide shopping information, offer route guidance, and build rapport. In the development, the major difficulties included sensing human behaviors, conversation in a noisy daily environment, and the needs of unexpected miscellaneous knowledge in the conversation. We chose a network-robot system approach, where a single robot's poor sensing capability and knowledge are supplemented by ubiquitous sensors and a human operator. The developed robot system detects a person with floor sensors to initiate interaction, identifies individuals with radio-frequency identification (RFID) tags, gives shopping information while chatting, and provides route guidance with deictic gestures. The robot was partially teleoperated to avoid the difficulty of speech recognition as well as to furnish a new kind of knowledge that only humans can flexibly provide. The information supplied by a human operator was later used to increase the robot's autonomy. For 25 days in a shopping mall, we conducted a field trial and gathered 2642 interactions. A total of 235 participants signed up to use RFID tags and, later, provided questionnaire responses. The questionnaire results are promising in terms of the visitors' perceived acceptability as well as the encouragement of their shopping activities. The results of the teleoperation analysis revealed that the amount of teleoperation gradually decreased, which is also promising.

A Simple Adaptive Control Approach for Trajectory Tracking of Electrically Driven Nonholonomic Mobile Robots

Abstract: Almost all existing controllers for nonholonomic mobile robots are designed without considering the actuator dynamics. This is because the presence of the actuator dynamics increases the complexity of the system dynamics, and makes difficult the design of the controller. In this paper, we propose a simple adaptive control approach for path tracking of uncertain nonholonomic mobile robots incorporating actuator dynamics. All parameters of robot kinematics, robot dynamics, and actuator dynamics are assumed to be uncertain. For the simple controller design, the dynamic surface control methodology is applied and extended to mobile robots that the number of inputs and outputs is different. We also adopt the adaptive control technique to treat all uncertainties and derive adaptation laws from the Lyapunov stability theory. Finally, simulation results demonstrate the effectiveness of the proposed controller.

Global Manipulation Planning in Robot Joint Space With Task Constraints

Abstract: We explore global randomized joint-space path planning for articulated robots that are subjected to task-space constraints. This paper describes a representation of constrained motion for joint-space planners and develops two simple and efficient methods for constrained sampling of joint configurations: tangent-space sampling (TS) and first-order retraction (FR). FR is formally proven to provide global sampling for linear task-space transformations. Constrained joint-space planning is important for many real-world problems, which involves redundant manipulators. On the one hand, tasks are designated in workspace coordinates: to rotate doors about fixed axes, to slide drawers along fixed trajectories, or to hold objects level during transport. On the other hand, joint-space planning gives alternative paths that use redundant degrees of freedom (DOFs) to avoid obstacles or satisfy additional goals while performing a task. We demonstrate that our methods are faster and more invariant to parameter choices than the techniques that exist.

Goal Babbling Permits Direct Learning of Inverse Kinematics

Abstract: We present an approach to learn inverse kinematics of redundant systems without prior- or expert-knowledge. The method allows for an iterative bootstrapping and refinement of the inverse kinematics estimate. The essential novelty lies in a path-based sampling approach: we generate training data along paths, which result from execution of the currently learned estimate along a desired path towards a goal. The information structure thereby induced enables an efficient detection and resolution of inconsistent samples solely from directly observable data. We derive and illustrate the exploration and learning process with a low-dimensional kinematic example that provides direct insight into the bootstrapping process. We further show that the method scales for high dimensional problems, such as the Honda humanoid robot or hyperredundant planar arms with up to 50 degrees of freedom.

Kinodynamic Motion Planning

Abstract: This article extends the capabilities of the harmonic potential field (HPF) approach to planning to cover both the kinematic and dynamic aspects of a robot's motion. The suggested approach converts the gradient guidance field from a harmonic potential to a control signal by augmenting it with a novel type of damping forces called nonlinear, anisotropic, damping forces (NADF). The HPF (harmonic potential field) approach to planning is emerging as a powerful paradigm for the guidance of autonomous agents.

LTLMoP: Experimenting with language, Temporal Logic and robot control

Abstract: The Linear Temporal Logic MissiOn Planning (LTLMoP) toolkit is a software package designed to assist in the rapid development, implementation, and testing of high-level robot controllers. In this toolkit, structured English and Linear Temporal Logic are used to write high-level reactive task specifications, which are then automatically transformed into correct robot controllers that can be used to drive either a simulated or a real robot. LTLMoP's modular design makes it ideal for research in areas such as controller synthesis, semantic parsing, motion planning, and human-robot interaction.

Recognizing engagement in human-robot interaction

Abstract: Based on a study of the engagement process between humans, we have developed and implemented an initial computational model for recognizing engagement between a human and a humanoid robot. Our model contains recognizers for four types of connection events involving gesture and speech: directed gaze, mutual facial gaze, conversational adjacency pairs and backchannels. To facilitate integrating and experimenting with our model in a broad range of robot architectures, we have packaged it as a node in the open-source Robot Operating System (ROS) framework. We have conducted a preliminary validation of our computational model and implementation in a simple human-robot pointing game.

Modeling and Optimal Control of Human-Like Running

Abstract: Designing and controlling an anthropomorphic mechatronic system that is able to perform a dynamic running motion is a challenging task. One difficulty is that the fundamental principles of natural human running motions are not yet fully understood. The purpose of this paper is to show that mathematical optimization is a helpful tool to gain this insight into fast and complex motions. We present physics-based running motions for complex models of human-like running in three dimensions that have been generated by optimization. Running is modeled as a multiphase periodic motion with discontinuities, based on multibody system models of the locomotor system with actuators and spring-damper elements at each joint. The problem of generating gaits is formulated as offline optimal control problem and solved by an efficient direct multiple shooting method. We present optimization results using energy-related criteria and show that they have a close resemblance to running motions of humans. The results provide information about the internal forces and torques required to produce natural human running, as well as on the resulting kinematics.

Bilateral Teleoperation of Multiple Mobile Agents: Coordinated Motion and Collision Avoidance

Abstract: This paper presents theoretical and experimental results on bilateral teleoperation of multiple mobile slave agents coupled to a single master robot. We first design a passifying proportional-derivative (PD) controller to enforce motion tracking and formation control of master and slave vehicles under constant, bounded communication delays. Then, we incorporate avoidance functions to guarantee collision-free transit through obstructed spaces. The unified control framework is validated by experiments with two coaxial helicopters as slave agents and a haptic device as the master robot.

A Control Strategy for Upper Limb Robotic Rehabilitation With a Dual Robot System

Abstract: This paper describes the development and use of the cooperative control scheme used by the intelligent pneumatic arm movement (iPAM) system to deliver safe, therapeutic treatment of the upper limb during voluntary reaching exercises. A set of clinical and engineering requirements for the control scheme are identified and detailed, which entail controlled, coordinated movement of a dual robot system with respect to the human upper limb. This is achieved by using a 6-DOF model of the upper limb that forms the controller's coordinate system. An admittance control scheme is developed by using this coordinate system such that robotic assistance can be varied as appropriate. Key controller components are derived, including kinematic and force transformations between the upper limb model and the dual robot task space. The controller is tested using a computational simulation and with a stroke subject in the iPAM system. The results demonstrate that the control scheme can reliably coordinate the dual robots to assist upper limb movements. A discussion considers the ramifications of using the system in practice, including the effects of measurement errors and controller limitations. In conclusion, the iPAM system has been shown to be effective at delivering variable levels of assistance to the upper limb joints during therapeutic movements in a clinically appropriate manner.

Bio-inspired design and dynamic maneuverability of a minimally actuated six-legged robot

Abstract: Rapidly running arthropods like cockroaches make use of passive dynamics to achieve remarkable locomotion performance with regard to stability, speed, and maneuverability In this work, we take inspiration from these organisms to design, fabricate, and control a 10cm, 24 gram underactuated hexapedal robot capable of running at 14 body lengths per second and performing dynamic turning maneuvers Our design relies on parallel kinematic mechanisms fabricated using the scaled smart composite microstructures (SCM) process and viscoelastic polymer legs with tunable stiffness In addition to the novel robot design, we present experimental validation of the lateral leg spring (LLS) locomotion model's prediction that dynamic turning can be achieved by modulating leg stiffness Finally, we present and validate a leg design for active stiffness control using shape memory alloy and demonstrate the ability of the robot to execute near-gymnastic 90° turns in the span of five strides

Design, simulation and evaluation of kinematic alternatives for Insertable Robotic Effectors Platforms in Single Port Access Surgery

Abstract: This paper presents the task specifications for designing a novel Insertable Robotic Effectors Platform (IREP) with integrated stereo vision and surgical intervention tools for Single Port Access Surgery (SPAS). This design provides a compact deployable mechanical architecture that may be inserted through a single O15 mm access port. Dexterous surgical intervention and stereo vision are achieved via the use of two snake-like continuum robots and two controllable CCD cameras. Simulations and dexterity evaluation of our proposed design are compared to several design alternatives with different kinematic arrangements. Results of these simulations show that dexterity is improved by using an independent revolute joint at the tip of a continuum robot instead of achieving distal rotation by transmission of rotation about the backbone of the continuum robot. Further, it is shown that designs with two robotic continuum robots as surgical arms have diminished dexterity if the bases of these arms are close to each other. This result justifies our design and points to ways of improving the performance of existing designs that use continuum robots as surgical arms.

Kinematic-Sensitivity Indices for Dimensionally Nonhomogeneous Jacobian Matrices

Abstract: Numerous performance indices have been proposed to compare robot architectures based on their kinematic properties. However, none of these indices seems to draw a consensus among the robotics community. The most notorious indices, which are manipulability and dexterity, still entail some drawbacks, which are mainly due to the impossibility to define a single invariant metric for the special Euclidean group. The natural consequence is to use two distinct metrics, i.e., one for rotations and one for point displacements, as has already been proposed by other researchers. This is the approach used in this paper, where we define the maximum rotation sensitivity and the maximum point-displacement sensitivity. These two indices provide tight upper bounds to the end-effector rotation and point-displacement sensitivity under a unit-magnitude array of actuated-joint displacements. Therefore, their meaning is thought to be clear and definite to the designer of a robotic manipulator. Furthermore, methods for the computation of the proposed indices are devised, some of their properties are established and interpreted in the context of robotic manipulator design, and an example is provided.

Modular Robot Systems

Abstract: We have presented a detailed retrospective on modular robots and discussed connections between modular robots and programmable matter. This field has seen a great deal of creativity and innovation at the level of designing physical systems capable of matching shape to function and algorithms that achieve this capability. The success of these projects rests on the convergence of innovation in hardware design and materials for creating the basic building blocks, information distribution for programming the interaction between the blocks, and control. Most current systems have dimensions on the order of centimeters, yet pack computation, communication, sensing, and power transfer capabilities into their form factors. Additionally, these modules operate using distributed algorithms that use a modules ability to observe its current neighborhood and local rules to decide what to do next.

Trajectory planning for robots in dynamic human environments

Abstract: This paper presents a trajectory planning algorithm for a robot operating in dynamic human environments. Environments such as pedestrian streets, hospital corridors, train stations or airports. We formulate the problem as planning a minimal cost trajectory through a potential field, defined from the perceived position and motion of persons in the environment.

Kinetostatic danger field - a novel safety assessment for human-robot interaction

Abstract: This paper presents a novel method for evaluating the danger within the environment of a robot manipulator. It is based on the introduced concept of kinetostatic danger field, a quantity that captures the complete state of the robot - its configuration and velocity. The field itself is invariant with respect to objects around the robot and can be computed in any given point of the workspace using measurements from the proprioceptive sensors. Moreover, all the computation can be performed in closed form, yielding compact algebraic expressions that allow for real time applications. The danger field is not only a meaningful indicator about the risk in the vicinity of the robot, but can also be fed back within control skills that implement some well known safety strategies like collision avoidance and virtual impedance control, provided that some environment perception is available in order to determine the points where the field should be computed. Kinematic redundancy for simultaneous task performance and danger minimization can be exploited. The methodology described in the paper is supported with simulation results.

Kinematically optimal catching a flying ball with a hand-arm-system

Abstract: A robotic ball-catching system built from a multi-purpose 7-DOF lightweight arm (DLR-LWR-III) and a 12 DOF four-fingered hand (DLR-Hand-II) is presented. Other than in previous work a mechatronically complex dexterous hand is used for grasping the ball and the decision of where, when and how to catch the ball, while obeying joint, speed and work cell limits, is formulated as an unified nonlinear optimization problem with nonlinear constraints. Three different objective functions are implemented, leading to significantly different robot movements. The high computational demands of an online realtime optimization are met by parallel computation on distributed computing resources (a cluster with 32 CPU cores). The system achieves a catch rate of > 80% and is regularly shown as a live demo at our institute.

Athlete Robot with applied human muscle activation patterns for bipedal running

Abstract: The essential component of legged locomotion is control of the ground reaction force. To understand the role of the musculoskeletal body in dynamic locomotion, we investigate bipedal running using a musculoskeletal “Athlete Robot”. The configuration of the muscles in the robot is compatible with the human. The spring-like property of the human lower leg during running is modeled as an elastic blade foot based on findings from biomechanics. The motor command of the robot is represented by time series data of muscle activation. The muscle activation patterns are determined from numerical calculation using a model of the musculoskeletal leg based on the measurement of muscle activity and kinetic data of the human movements. In the simulation results, the robot runs 8 steps with a speed of 3 m/s. We also demonstrate that the real bipedal robot is able to run for several steps.

Design of high torque and high speed leg module for high power humanoid

Abstract: The high power ability of humanoid is desired for application of nursing or running or jumping motions. Achievement of the actuator of light and powerful equivalent to humans is required. In this paper, we propose a method to extract inherent performance from motors by an active temperature control. The method safely improves the output of motors. The active temperature control is achieved by combining the estimation of an internal temperature of the motor with the forced cooling by liquid. We also developed high power motor drivers for the proposed method. An experiment of a high power joint test bench is shown. In this paper, we show a high power prototype biped robot for application of nursing, running or jumping motions. High power actuator system and robust internal body network are developed for high power robot. Basic demonstration experiments of high power motion are shown.

Two foraging algorithms for robot swarms using only local communication

Abstract: Large collections of robots have the potential to perform tasks collectively using distributed control algorithms. These algorithms require communication between robots to allow the robots to coordinate their behavior and act as a collective. In this paper we describe two algorithms which allow coordination between robots, but do not require physical environment marks such as pheromones. Instead, these algorithms rely on simple, local, low bandwidth, direct communication between robots. We describe the algorithms and measure their performance in worlds with and without obstacles.

Multicriteria Optimization for Coordination of Redundant Robots Using a Dual Neural Network

Abstract: A dual neural-network method for the coordination of kinematically redundant robots is proposed in this paper. The performance criteria for single robots provided by Nedungadi and Kazerounian are generalized to a multicriteria form for the coordinated-manipulation system composed of multiple serial manipulators. By optimizing the local joint torques and generalized forces applied on the object/workpiece using a designed weighting matrix, the proposed method achieves the global stability during the coordinated-manipulation process. Moreover, the proposed algorithm has an explicit physical meaning, i.e., both the global kinetic energy of the coordination system and the two-norm of the generalized forces applied on the object are minimized simultaneously. In addition, the physical limits of both joint torques and the generalized forces applied on the object are considered, which makes the original coordination problem become a complicated optimization problem subject to both equality and inequality constraints. Compared with numerical optimization algorithms used in existing literatures, the dual neural-network method has better computational capability to deal with the complicated optimization problem. Finally, illustrative examples are given to show that the proposed method is effective and efficient for the multirobot coordinated-manipulation system.

Intrinsically motivated goal exploration for active motor learning in robots: A case study

Abstract: We introduce the Self-Adaptive Goal Generation - Robust Intelligent Adaptive Curiosity (SAGG-RIAC) algorithm as an intrinsically motivated goal exploration mechanism which allows a redundant robot to efficiently and actively learn its inverse kinematics. The main idea is to push the robot to perform babbling in the goal/operational space, as opposed to motor babbling in the actuator space, by self-generating goals actively and adaptively in regions of the goal space which provide a maximal competence improvement for reaching those goals. Then, a lower level active motor learning algorithm, inspired by the SSA algorithm, is used to allow the robot to locally explore how to reach a given self-generated goal. We present simulated experiments in a 32 dimensional continuous sensorimotor space showing that 1) exploration in the goal space can be a lot faster than exploration in the actuator space for learning the inverse kinematics of a redundant robot; 2) selecting goals based on the maximal improvement heuristics is statistically significantly more efficient than selecting goals randomly.