Showing papers in "The International Journal of Robotics Research in 2009"

Planning Long Dynamically Feasible Maneuvers for Autonomous Vehicles

Abstract: In this paper, we present an algorithm for generating complex dynamically feasible maneuvers for autonomous vehicles traveling at high speeds over large distances. Our approach is based on performing anytime incremental search on a multi-resolution, dynamically feasible lattice state space. The resulting planner provides real-time performance and guarantees on and control of the suboptimality of its solution. We provide theoretical properties and experimental results from an implementation on an autonomous passenger vehicle that competed in, and won, the Urban Challenge competition.

Decentralized, Adaptive Coverage Control for Networked Robots

Abstract: A decentralized, adaptive control law is presented to drive a network of mobile robots to an optimal sensing configuration. The control law is adaptive in that it uses sensor measurements to learn the distribution of sensory information in the environment. It is decentralized in that it requires only information local to each robot. The controller is then improved by introducing a consensus algorithm to propagate sensory information from every robot throughout the network. Convergence and consensus of parameters is proven with a Lyapunovtype proof. The controller with and without consensus is demonstrated in numerical simulations. These techniques are suggestive of broader applications of adaptive control methodologies to decentralized control problems in unknown dynamic environments.

Hand Posture Subspaces for Dexterous Robotic Grasping

Abstract: In this paper we focus on the concept of low-dimensional posture subspaces for artificial hands. We begin by discussing the applicability of a hand configuration subspace to the problem of automated grasp synthesis; our results show that low-dimensional optimization can be instrumental in deriving effective pre-grasp shapes for a number of complex robotic hands. We then show that the computational advantages of using a reduced dimensionality framework enable it to serve as an interface between the human and automated components of an interactive grasping system. We present an on-line grasp planner that allows a human operator to perform dexterous grasping tasks using an artificial hand. In order to achieve the computational rates required for effective user interaction, grasp planning is performed in a hand posture subspace of highly reduced dimensionality. The system also uses real-time input provided by the operator, further simplifying the search for stable grasps to the point where solutions can be found at interactive rates. We demonstrate our approach on a number of different hand models and target objects, in both real and virtual environments.

Requirements for Safe Robots: Measurements, Analysis and New Insights

Abstract: Physical human—robot interaction and cooperation has become a topic of increasing importance and of major focus in robotics research. An essential requirement of a robot designed for high mobility and direct interaction with human users or uncertain environments is that it must in no case pose a threat to the human. Until recently, quite a few attempts were made to investigate real-world threats via collision tests and use the outcome to considerably improve safety during physical human—robot interaction. In this paper, we give an overview of our systematic evaluation of safety in human—robot interaction, covering various aspects of the most significant injury mechanisms. In order to quantify the potential injury risk emanating from such a manipulator, impact tests with the DLR-Lightweight Robot III were carried out using standard automobile crash test facilities at the German Automobile Club (ADAC). Based on these tests, several industrial robots of different weight have been evaluated and the influence of the robot mass and velocity have been investigated. The evaluated non-constrained impacts would only partially capture the nature of human—robot safety. A possibly constrained environment and its effect on the resulting human injuries are discussed and evaluated from different perspectives. As well as such impact tests and simulations, we have analyzed the problem of the quasi-static constrained impact, which could pose a serious threat to the human even for low-inertia robots under certain circumstances. Finally, possible injuries relevant in robotics are summarized and systematically classified.

Flagellated Magnetotactic Bacteria as Controlled MRI-trackable Propulsion and Steering Systems for Medical Nanorobots Operating in the Human Microvasculature

Abstract: Although nanorobots may play critical roles for many applications in the human body, such as targeting tumoral lesions for therapeutic purposes, miniaturization of the power source with an effective onboard controllable propulsion and steering system have prevented the implementation of such mobile robots. Here, we show that the flagellated nanomotors combined with the nanometer-sized magnetosomes of a single magnetotactic bacterium can be used as an effective integrated propulsion and steering system for devices, such as nanorobots, designed for targeting locations only accessible through the smallest capillaries in humans while being visible for tracking and monitoring purposes using modern medical imaging modalities such as magnetic resonance imaging. Through directional and magnetic field intensities, the displacement speeds, directions, and behaviors of swarms of these bacterial actuators can be controlled from an external computer.

Design and Integration of a Telerobotic System for Minimally Invasive Surgery of the Throat

Abstract: In this paper we present the clinical motivation, design specifications, kinematics, statics, and actuation compensation for a newly constructed telerobotic system for Minimally Invasive Surgery (MIS) of the throat. A hybrid dual-arm telesurgical slave, with 20 joint-space Degrees-of-Freedom (DoFs), is used in this telerobotic system to provide the necessary dexterity in deep surgical fields such as the throat. The telerobotic slave uses novel continuum robots that use multiple super-elastic backbones for actuation and structural integrity. We present the kinematics of the telesurgical slave and methods for actuation compensation to cancel the effects of backlash, friction, and flexibility of the actuation lines. A method for actuation compensation is presented in order to overcome uncertainties of modeling, friction, and backlash. This method uses a tiered hierarchy of two novel approaches of actuation compensation for remotely actuated snake-like robots. The tiered approach for actuation compensation uses compensation in both joint space and configuration space of the continuum robots. These hybrid actuation compensation schemes use intrinsic model information and external data through a recursive linear estimation algorithm and involve compensation using configuration space and joint space variables. Experimental results validate the ability of our integrated telemanipulation system through experiments of suturing and knot tying in confined spaces.

The Belief Roadmap: Efficient Planning in Belief Space by Factoring the Covariance

Abstract: When a mobile agent does not know its position perfectly, incorporating the predicted uncertainty of future position estimates into the planning process can lead to substantially better motion performance However, planning in the space of probabilistic position estimates, or belief space, can incur a substantial computational cost In this paper, we show that planning in belief space can be performed efficiently for linear Gaussian systems by using a factored form of the covariance matrix This factored form allows several prediction and measurement steps to be combined into a single linear transfer function, leading to very efficient posterior belief prediction during planning We give a belief-space variant of the probabilistic roadmap algorithm called the belief roadmap (BRM) and show that the BRM can compute plans substantially faster than conventional belief space planning We conclude with performance results for an agent using ultra-wide bandwidth radio beacons to localize and show that we can efficiently generate plans that avoid failures due to loss of accurate position estimation

Cooperative localization for autonomous underwater vehicles

Abstract: Self-localization of an underwater vehicle is particularly challenging due to the absence of Global Positioning System (GPS) reception or features at known positions that could otherwise have been used for position computation. Thus Autonomous Underwater Vehicle (AUV) applications typically require the pre-deployment of a set of beacons. This thesis examines the scenario in which the members of a group of AUVs exchange navigation information with one another so as to improve their individual position estimates. We describe how the underwater environment poses unique challenges to vehicle navigation not encountered in other environments in which robots operate and how cooperation can improve the performance of self-localization. As intra-vehicle cornmunication is crucial to cooperation, we also address the constraints of the communication channel and the effect that these constraints have on the design of cooperation strategies. The classical approaches to underwater self-localization of a single vehicle, as well as more recently developed techniques are presented. We then examine how methods used for cooperating land-vehicles can be transferred to the underwater domain. An algorithm for distributed self-localization, which is designed to take the specific characteristics of the environment into account, is proposed. We also address how correlated position estimates of cooperating vehicles can lead to overconfidence in individual position estimates. Finally, key to any successful cooperative navigation strategy is the incorporation of the relative positioning between vehicles. The performance of localization algorithms with different geometries is analyzed and a distributed algorithm for the dynamic positioning of vehicles, which serve as dedicated navigation beacons for a fleet of AUVs, is proposed. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)

The New College Vision and Laser Data Set

Abstract: In this paper we present a large dataset intended for use in mobile robotics research. Gathered from a robot driving several kilometers through a park and campus, it contains a five-degree-of-freedom dead-reckoned trajectory, laser range/reflectance data and 20 Hz stereoscopic and omnidirectional imagery. All data is carefully timestamped and all data logs are in human readable form with the images in standard formats. We provide a set of tools to access the data and detailed tagging and segmentations to facilitate its use.

Modeling and Experimental Characterization of an Untethered Magnetic Micro-Robot

Abstract: Here we present the control, performance and modeling of an untethered electromagnetically actuated magnetic micro-robot. The microrobot, which is composed of neodymium—iron—boron with dimensions 250 I¼m 1 130 I¼m 1 10 I¼m , is actuated by a system of six macro-scale electromagnets. Periodically varying magnetic fields are used to impose magnetic torques, which induce stick—slip motion in the micro-robot. These magnetic forces and torques are incorporated into a comprehensive dynamic model, which captures the behavior of the micro-robot. By pivoting the micro-robot about an edge, non-planar obstacles with characteristic sizes comparable to the robot length can be surmounted. Actuation is demonstrated on several substrates with different surface properties, in a fluid environment, and in a vacuum. Observed micro-robot translation speeds can exceed 10 mm s-1.

A Hybrid Approach to Intricate Motion, Manipulation and Task Planning

Abstract: We propose a representation and a planning algorithm able to deal with problems integrating task planning as well as motion and manipulation planning knowledge involving several robots and objects. Robot plans often include actions where the robot has to place itself in some position in order to perform some other action or to "modify" the configuration of its environment by displacing objects. Our approach aims at establishing a bridge between task planning and manipulation planning that allows a rigorous treatment of geometric preconditions and effects of robot actions in realistic environments. We show how links can be established between a symbolic description and its geometric counterpart and how they can be used in an integrated planning process that is able to deal with intricate symbolic and geometric constraints. Finally, we describe the main features of an implemented planner and discuss several examples of its use.

The RAVEN: Design and Validation of a Telesurgery System

Abstract: The collaborative effort between fundamental science, engineering and medicine provides physicians with improved tools and techniques for delivering effective health care. Minimally invasive surgery (MIS) techniques have revolutionized the way a number of surgical procedures are performed. Recent advances in surgical robotics are once again revolutionizing MIS interventions and open surgery. In an earlier research endeavor, 30 surgeons performed 7 different MIS tasks using the Blue Dragon system to collect measurements of position, force, and torque on a porcine model. This data served as the foundation for a kinematic optimization of a spherical surgical robotic manipulator. Following the optimization, a seven-degree-of-freedom cable-actuated surgical manipulator was designed and integrated, providing all degrees of freedom present in manual MIS as well as wrist joints located at the surgical end-effector. The RAVEN surgical robot system has the ability to teleoperate utilizing a single bi-directional UDP socket via a remote master device. Preliminary telesurgery experiments were conducted using the RAVEN. The experiments illustrated the system’s ability to operate in extreme conditions using a variety of network settings.

MRI-based Medical Nanorobotic Platform for the Control of Magnetic Nanoparticles and Flagellated Bacteria for Target Interventions in Human Capillaries

Abstract: Medical nanorobotics exploits nanometer-scale components and phenomena with robotics to provide new medical diagnostic and interventional tools. Here, the architecture and main specifications of a novel medical interventional platform based on nanorobotics and nanomedicine, and suited to target regions inaccessible to catheterization, are described. The robotic platform uses magnetic resonance imaging (MRI) for feeding back information to a controller responsible for the real-time control and navigation along pre-planned paths in the blood vessels of untethered magnetic carriers, nanorobots, and/or magnetotactic bacteria (MTB) loaded with sensory or therapeutic agents acting like a wireless robotic arm, manipulator, or other extensions necessary to perform specific remote tasks. Unlike known magnetic targeting methods, the present platform allows us to reach locations deep in the human body while enhancing targeting efficacy using real-time navigational or trajectory control. We describe several versions of the platform upgraded through additional software and hardware modules allowing enhanced targeting efficacy and operations in very difficult locations such as tumoral lesions only accessible through complex microvasculature networks.

Position Tracking for Non-linear Teleoperators with Variable Time Delay

Abstract: In this paper the problem of position tracking in the presence of variable time delay is studied. It is proved that simple P-like and PD-like controllers can stabilize the teleoperator under variable time delays and, moreover, they provide position tracking. Then, a controller based on the scattering transformation that also provides position tracking is proposed. In this paper we present the conditions under which the velocities and position error of the non-linear teleoperator, for the three controllers, are bounded, and if the human does not move the local manipulator and the remote manipulator does not interact with the environment, then it is proved that velocities and position error converge to zero. Simulations and real experiments, using the Internet from Urbana-Champaign (USA) to Barcelona (Spain), validate the proposed schemes.

Metastable Walking Machines

Abstract: Legged robots that operate in the real world are inherently subject to stochasticity in their dynamics and uncertainty about the terrain. Owing to limited energy budgets and limited control authority, these “disturbances” cannot always be canceled out with high-gain feedback. Minimally actuated walking machines subject to stochastic disturbances no longer satisfy strict conditions for limit-cycle stability; however, they can still demonstrate impressively long-living periods of continuous walking. Here, we employ tools from stochastic processes to examine the “stochastic stability” of idealized rimless-wheel and compass-gait walking on randomly generated uneven terrain. Furthermore, we employ tools from numerical stochastic optimal control to design a controller for an actuated compass gait model which maximizes a measure of stochastic stability—the mean first-passage time—and compare its performance with a deterministic counterpart. Our results demonstrate that walking is well characterized as a metastable process, and that the stochastic dynamics of walking should be accounted for during control design in order to improve the stability of our machines.

Efficient Multi-robot Search for a Moving Target

Abstract: This paper examines the problem of locating a mobile, non-adversarial target in an indoor environment using multiple robotic searchers. One way to formulate this problem is to assume a known environment and choose searcher paths most likely to intersect with the path taken by the target. We refer to this as the multi-robot efficient search path planning (MESPP) problem. Such path planning problems are NP-hard, and optimal solutions typically scale exponentially in the number of searchers. We present an approximation algorithm that utilizes finite-horizon planning and implicit coordination to achieve linear scalability in the number of searchers. We prove that solving the MESPP problem requires maximizing a non-decreasing, submodular objective function, which leads to theoretical bounds on the performance of our approximation algorithm. We extend our analysis by considering the scenario where searchers are given noisy non-line-of-sight ranging measurements to the target. For this scenario, we derive and integrate online Bayesian measurement updating into our framework. We demonstrate the performance of our framework in two large-scale simulated environments, and we further validate our results using data from a novel ultra-wideband ranging sensor. Finally, we provide an analysis that demonstrates the relationship between MESPP and the intuitive average capture time metric. Results show that our proposed linearly scalable approximation algorithm generates searcher paths that are competitive with those generated by exponential algorithms.

Navigating, Recognizing and Describing Urban Spaces With Vision and Lasers

Abstract: In this paper we describe a body of work aimed at extending the reach of mobile navigation and mapping. We describe how running topological and metric mapping and pose estimation processes concurrently, using vision and laser ranging, has produced a full six-degree-of-freedom outdoor navigation system. It is capable of producing intricate three-dimensional maps over many kilometers and in real time. We consider issues concerning the intrinsic quality of the built maps and describe our progress towards adding semantic labels to maps via scene de-construction and labeling. We show how our choices of representation, inference methods and use of both topological and metric techniques naturally allow us to fuse maps built from multiple sessions with no need for manual frame alignment or data association.

Comparison of Mechanical Design and Energy Consumption of Adaptable, Passive-compliant Actuators

Abstract: Different, adaptable, passive-compliant actuators have been developed recently such as the antagonistic setup of two Series Elastic Actuators, the Mechanically Adjustable Compliance and Controllable Equilibrium Position Actuator, the Actuator with Mechanically Adjustable Series Compliance, and the Variable Stiffness Actuator. The main purpose of these designs is to reduce the energy consumption of walking/running robots and prostheses. This paper presents a design formulation to link the different mechanical designs together, and a study on the power consumption of these actuators.

Driver Inattention Detection based on Eye Gaze-Road Event Correlation

Abstract: Current road safety initiatives are approaching the limit of their effectiveness in developed countries. A paradigm shift is needed to address the preventable deaths of thousands on our roads. Previous systems have focused on one or two aspects of driving: environmental sensing, vehicle dynamics or driver monitoring. Our approach is to consider the driver and the vehicle as part of a combined system, operating within the road environment. A driver assistance system is implemented that is not only responsive to the road environment and the driver's actions but also designed to correlate the driver's eye gaze with road events to determine the driver's observations. Driver observation monitoring enables an immediate in-vehicle system able to detect and act on driver inattentiveness, providing the precious seconds for an inattentive human driver to react. We present a prototype system capable of estimating the driver's observations and detecting driver inattentiveness. Due to the "look but not see" case it is not possible to prove that a road event has been observed by the driver. We show, however, that it is possible to detect missed road events and warn the driver appropriately.

Robotic Motion Compensation for Beating Heart Intracardiac Surgery

Abstract: 3D ultrasound imaging has enabled minimally invasive, beating heart intracardiac procedures. However, rapid heart motion poses a serious challenge to the surgeon that is compounded by significant time delays and noise in 3D ultrasound. This paper investigates the concept of using a one-degree-of-freedom motion compensation system to synchronize with tissue motions that may be approximated by 1D motion models. We characterize the motion of the mitral valve annulus and show that it is well approximated by a 1D model. The subsequent development of a motion compensation instrument (MCI) is described, as well as an extended Kalman filter (EKF) that compensates for system delays. The benefits and robustness of motion compensation are tested in user trials under a series of non-ideal tracking conditions. Results indicate that the MCI provides an approximately 50% increase in dexterity and 50% decrease in force when compared with a solid tool, but is sensitive to time delays. We demonstrate that the use of the EKF for delay compensation restores performance, even in situations of high heart rate variability. The resulting system is tested in an in vitro 3D ultrasound-guided servoing task, yielding accurate tracking (1.15 mm root mean square) in the presence of noisy, time-delayed 3D ultrasound measurements.

A Review on the Dynamic Control of Parallel Kinematic Machines: Theory and Experiments

Abstract: In this article, we review the dynamic control of parallel kinematic machines. It is shown that the classical control strategies from serial robotics generally used for parallel kinematic machine have to be rethought. Indeed, it is first shown that the joint space control is not relevant for these mechanisms for several reasons such as mechanical behavior or computational efficiency. Consequently, Cartesian space control should be preferred over joint space control. Nevertheless, some modifications to the well-known Cartesian space control strategies of serial robotics are proposed to make them perfectly suited to parallel kinematic machines, particularly a solution using an exteroceptive measure of the end-effector pose. The expected improvement in terms of accuracy, stability and robustness are discussed. A comparison between the main presented strategies is finally performed both in simulation and experiments.

A High-performance Redundantly Actuated Parallel Mechanism for Ankle Rehabilitation

Abstract: In this paper we present a redundantly actuated parallel mechanism for ankle rehabilitation. The proposed device has the advantage of mechanical and kinematic simplicity when compared with the state-of-the-art multi-degree-of-freedom parallel mechanism prototypes while at the same time it is fully capable of carrying out the exercises required by the ankle rehabilitation protocols. Optimization of the device workspace, dexterity, torque output and size was carried out during the design phase of the device. The development of the system involved the realization of a new customized linear actuator able to meet the speed and force requirements of the device functionality. We also discuss the impedance-based control scheme used for the redundantly actuated device, which allows the execution of both assistive and resistive strengthening rehabilitation regimes. Results from the control of a single linear actuator and further experimental tests including the position tracking of the fully actuated platform are presented. It is believed that the performance and the simplicity of the proposed mechanism will allow the widespread use of the system as a new aid tool for ankle rehabilitation.

COLD: The CoSy Localization Database

Abstract: Two key competencies for mobile robotic systems are localization and semantic context interpretation. Recently, vision has become the modality of choice for these problems as it provides richer and more descriptive sensory input. At the same time, designing and testing vision-based algorithms still remains a challenge, as large amounts of carefully selected data are required to address the high variability of visual information. In this paper we present a freely available database which provides a large-scale, flexible testing environment for vision-based topological localization and semantic knowledge extraction in robotic systems. The database contains 76 image sequences acquired in three different indoor environments across Europe. Acquisition was performed with the same perspective and omnidirectional camera setup, in rooms of different functionality and under various conditions. The database is an ideal testbed for evaluating algorithms in real-world scenarios with respect to both dynamic and categorical variations.

An Embodied Cognition Approach to Mindreading Skills for Socially Intelligent Robots

Abstract: Future applications for personal robots motivate research into developing robots that are intelligent in their interactions with people. Toward this goal, in this paper we present an integrated socio-cognitive architecture to endow an anthropomorphic robot with the ability to infer mental states such as beliefs, intents, and desires from the observable behavior of its human partner. The design of our architecture is informed by recent findings from neuroscience and embodies cognition that reveals how living systems leverage their physical and cognitive embodiment through simulation-theoretic mechanisms to infer the mental states of others. We assess the robot's mindreading skills on a suite of benchmark tasks where the robot interacts with a human partner in a cooperative scenario and a learning scenario. In addition, we have conducted human subjects experiments using the same task scenarios to assess human performance on these tasks and to compare the robot's performance with that of people. In the process, our human subject studies also reveal some interesting insights into human behavior.

Nanonewton Force Sensing and Control in Microrobotic Cell Manipulation

Abstract: Cellular force sensing and control techniques are capable of enhancing the dexterity and reliability of microrobotic cell manipulation systems. In this paper we present two experimental techniques for nanonewton force sensing and control in microrobotic cell manipulation. A vision-based cellular force sensing approach, including a microfabricated elastic cell holding device and a sub-pixel visual tracking algorithm, was developed for resolving forces down to 3.7 nN during microrobotic mouse embryo injection. The technique also experimentally demonstrated that the measured mechanical difference could be useful for in situ differentiation of healthy mouse embryos from those with compromised developmental competence without requiring a separate mechanical characterization process. Centered upon force-controlled microrobotic cell manipulation, this paper also presents nanonewton force-controlled micrograsping of interstitial cells using a microelectromechanical systems (MEMS)-based microgripper with integrated two-axis force feedback. On-chip force sensors are used for detecting contact between the microgripper and cells to be manipulated (resolution: 38.5 nN at 15Hz) and sensing gripping forces (resolution: 19.9 nN at 15Hz) during force-controlled grasping. The experimental results demonstrate that the microgripper and the control system are capable of rapid contact detection and reliable force-controlled micrograsping to accommodate variations in size and stiffness of cells with a high degree of reproducibility.

Proxy-based Sliding Mode Control of a Planar Pneumatic Manipulator

Abstract: For a robotic system that shares its workspace with humans and physically interacts with them, safety is of paramount importance. In order to build a safe system, safety has to be considered in both hardware and software (control). In this paper, we present the safe control of a two-degree-of-freedom planar manipulator actuated by Pleated Pneumatic Artificial Muscles. Owing to its low weight and inherent compliance, the system hardware has excellent safety characteristics. In traditional control methods, safety and good tracking are often impossible to combine. This is different in the case of Proxy-Based Sliding Mode Control (PSMC), a novel control method introduced by Kikuuwe and Fujimoto. PSMC combines responsive and accurate tracking during normal operation with smooth, slow and safe recovery from large position errors. It can also make the system behave compliantly to external disturbances. We present both task- and joint-space implementations of PSMC applied to the pneumatic manipulator, and compare their performance with PID control. Good tracking results are obtained, especially with the joint-space implementation. Safety is evaluated by means of the Head Injury Criterion and by the maximum interaction force in the case of collision. It is found that in spite of the hardware safety features, the system is unsafe when under PID control. PSMC, on the other hand, provides increased safety as well as good tracking.

Robot-assisted Tactile Sensing for Minimally Invasive Tumor Localization

Abstract: The 10 mm incisions used in minimally invasive cancer surgery prevent the direct palpation of internal organs, making intraoperative tumor localization difficult. A tactile sensing instrument (TSI), which uses a commercially available sensor to measure distributed pressure profiles along the contacting surface, has been developed to facilitate remote tissue palpation. The objective of this research is to assess the feasibility of using the TSI under robotic control to reliably locate underlying tumors while reducing collateral tissue trauma. The performance of humans and a robot using the TSI to locate tumor phantoms embedded into ex vivo bovine livers is compared. An augmented hybrid impedance control scheme has been implemented on a Mitsubishi PA10-7C to perform the force/position control used in the trials. The results show that using the TSI under robotic control realizes an average 35% decrease in the maximum forces applied and a 50% increase in tumor detection accuracy when compared to manual manipulation of the same instrument. This demonstrates that the detection of tumors using tactile sensing is highly dependent on how consistently the forces on the tactile sensing area are applied, and that robotic assistance can be of great benefit when trying to localize tumors in minimally invasive surgery.

Growing Hidden Markov Models: An Incremental Tool for Learning and Predicting Human and Vehicle Motion

Abstract: Modeling and predicting human and vehicle motion is an active research domain. Owing to the difficulty in modeling the various factors that determine motion (e.g. internal state, perception) this is often tackled by applying machine learning techniques to build a statistical model, using as input a collection of trajectories gathered through a sensor (e.g. camera, laser scanner), and then using that model to predict further motion. Unfortunately, most current techniques use offline learning algorithms, meaning that they are not able to learn new motion patterns once the learning stage has finished. In this paper, we present an approach where motion patterns can be learned incrementally, and in parallel with prediction. Our work is based on a novel extension to hidden Markov models, called growing hidden Markov models, which gives us the ability to learn incrementally both the parameters and the structure of the model. The proposed approach has been evaluated using synthetic and real trajectory data. In our experiments our approach consistently learned motion models that were more compact and accurate than those produced by two other state-of-the-art techniques.

Dynamic Rolling for a Modular Loop Robot

Abstract: Reconfigurable modular robots have the ability to use different gaits and configurations to perform various tasks. A rolling gait is the fastest currently implemented gait available for traversal over level ground and shows dramatic improvements in efficiency. In this work, we analyze and implement a sensor-based feedback controller to achieve dynamic rolling for a loop robot. The robot senses its position relative to the ground and changes its shape as it rolls. This shape is such that its center of gravity is maintained to be in front of its contact point with the ground, so in effect the robot is continuously falling and thus accelerates forward. Using simulation and experimental results, we show how the desired shape can be varied to achieve higher terminal velocities. The highest velocity achieved in this work is 26 module lengths per second (1.6 m/s) which is believed to be the fastest gait yet implemented for an untethered modular robot. One of the major findings is that more elongated shapes achieve higher terminal velocities than rounder shapes. We demonstrate that this trend holds going up inclines as well as down. We show that rounder shapes have lower specific resistance and are thus more energy efficient. The control scheme is scalable to an arbitrary number of modules, shown here using eight to 14 modules.

Mini-Whegs TM Climbs Steep Surfaces Using Insect-inspired Attachment Mechanisms

Abstract: When climbing vertical or inclined surfaces, insects utilize claws, tibial spines, and tarsal pads to create attachment forces. These devices allow them to climb on a variety of substrates, including those that are smooth, soft, or porous. Recent advances in materials may make long-lasting dry adhesives and arrays of sharp hooks feasible attachment mechanisms for small robots. Mini-WhegsTM are a series of robots that use rotating wheel-legs driven by a single motor for locomotion. By testing specially designed wheel-legs with office tape, pairs of spines, and Velcro®, this work demonstrates the feasibility of applying novel adhesives and frictional materials passively on simple rotating legs. The resulting robot climbs vertical fabric surfaces with Velcro®, crosses ceilings with Scotch® tape, and climbs steep concrete inclines with sharp spines and provides a test-platform for future adhesive materials such as dry adhesive tape.