TechTalks from event: Technical session talks from ICRA 2012

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Perception for Autonomous Vehicles

  • Active Perception for Autonomous Vehicles Authors: Unterholzner, Alois; Himmelsbach, Michael; Wuensche, Hans J
    Precise perception of a vehicle's surrounding is crucial for safe autonomous driving. It requires a high sensor resolution and a large field of view. Active perception, i.e. the redirection of a sensor's focus of attention, is an approach to provide both. With active perception, however, the selection of an appropriate sensor orientation becomes necessary. This paper presents a method for determining the sensor orientation in urban traffic scenarios based on three criteria: the importance of traffic participants w.r.t. the current situation, the available information about traffic participants while considering alternative sensor orientations as well as sensor coverage of the vehicle's relevant surrounding area.
  • A Probabilistic Framework for Car Detection in Images using Context and Scale Authors: Held, David; Levinson, Jesse; Thrun, Sebastian
    Detecting cars in real-world images is an important task for autonomous driving, yet it remains unsolved. The system described in this paper takes advantage of context and scale to build a monocular single-frame image-based car detector that significantly outperforms the baseline. The system uses a probabilistic model to combine multiple forms of evidence for both context and scale to locate cars in a real-world image. We also use scale filtering to speed up our algorithm by a factor of 3.3 compared to the baseline. By using a calibrated camera and localization on a road map, we are able to obtain context and scale information from a single image without the use of a 3D laser. The system outperforms the baseline by an absolute 9.4% in overall average precision and 11.7% in average precision for cars smaller than 50 pixels in height, for which context and scale cues are especially important.
  • Real-Time Topometric Localization Authors: Badino, Hernan; Huber, Daniel; Kanade, Takeo
    Autonomous vehicles must be capable of localizing even in GPS denied situations. In this paper, we propose a real-time method to localize a vehicle along a route using visual imagery or range information. Our approach is an implementation of topometric localization, which combines the robustness of topological localization with the geometric accuracy of metric methods. We construct a map by navigating the route using a GPS-equipped vehicle and building a compact database of simple visual and 3D features. We then localize using a Bayesian filter to match sequences of visual or range measurements to the database. The algorithm is reliable across wide environmental changes, including lighting differences, seasonal variations, and occlusions, achieving an average localization accuracy of 1 m over an 8 km route. The method converges correctly even with wrong initial position estimates solving the kidnapped robot problem.
  • SeqSLAM: Visual Route-Based Navigation for Sunny Summer Days and Stormy Winter Nights Authors: Milford, Michael J; Wyeth, Gordon
    Learning and then recognizing a route, whether travelled during the day or at night, in clear or inclement weather, and in summer or winter is a challenging task for state of the art algorithms in computer vision and robotics. In this paper, we present a new approach to visual navigation under changing conditions dubbed SeqSLAM. Instead of calculating the single location most likely given a current image, our approach calculates the best candidate matching location within every local navigation sequence. Localization is then achieved by recognizing coherent sequences of these “local best matches”. This approach removes the need for global matching performance by the vision front-end – instead it must only pick the best match within any short sequence of images. The approach is applicable over environment changes that render traditional feature-based techniques ineffective. Using two car-mounted camera datasets we demonstrate the effectiveness of the algorithm and compare it to one of the most successful feature-based SLAM algorithms, FAB-MAP. The perceptual change in the datasets is extreme; repeated traverses through environments during the day and then in the middle of the night, at times separated by months or years and in opposite seasons, and in clear weather and extremely heavy rain. While the feature-based method fails, the sequence-based algorithm is able to match trajectory segments at 100% precision with recall rates of up to 60%.
  • Image Sequence Partitioning for Outdoor Mapping Authors: Korrapati, Hemanth; Mezouar, Youcef; Martinet, Philippe
    Most of the existing appearance based topological mapping algorithms produce dense topological maps in which each image stands as a node in the topological graph. Sparser maps can be built by representing groups of visually similar images as nodes of a topological graph. In this paper, we present a sparse topological mapping framework which uses Image Sequence Partitioning (ISP) techniques to group visually similar images as topological graph nodes. We present four different ISP techniques and evaluate their performance. In order to take advantage of the afore mentioned maps, we make use of Hierarchical Inverted Files (HIF) which enable efficient hierarchical loop closure. Outdoor experimental results demonstrating the sparsity, efficiency and accuracy achieved by the combination of ISP and HIF in performing loop closure are presented.
  • Anytime Merging of Appearance Based Maps Authors: Erinc, Gorkem; Carpin, Stefano
    Appearance based maps are emerging as an important class of spatial representations for mobile robots. In this paper we tackle the problem of merging together two or more appearance based maps independently built by robots operating in the same environment. Noticing the lack of well accepted metrics to measure the performance of map merging algorithms, we propose to use algebraic connectivity as a metric to assess the advantage gained by merging multiple maps. Next, based on this criterion, we propose an anytime algorithm aiming to quickly identify the more advantageous parts to merge. The system we proposed has been fully implemented and tested in indoor scenarios and shows that our algorithm achieves a convenient tradeoff between accuracy and speed.

Rehabilitation Robotics

  • A Comparison of Parallel and Series Elastic Elements in an Actuator for Mimicking Human Ankle Joint in Walking and Running Authors: Grimmer, Martin; Seyfarth, Andre; Eslamy, Mahdy
    Elastic elements in prosthetic devices can help to reduce peak power (PP) and energy requirements (ER) for the actuators. Calculations showed that it is impossible with current commercial motor technology to mimic human ankle behavior in detail for higher walking and running speeds with single motor solutions using a Serial Elastic Actuator (SEA). Concerning this result we checked the requirements of a parallel elastic actuator (PEA) and a combination of serial and parallel (SE+PEA) springs. We found that a PEA can reduce PP additionally in comparison to the SEA by preloading the spring in the flight phase. This reduces also peak torque. But this loading needs additional energy so that the ER increase in comparison to the SEA. The SE+PEA concept can further decrease PP. With that, the ER are less than the PEA but higher than for the SEA. The results show less benefit for the PEA and the SE+PEA when a constant stiffness and a fixed parallel spring slack length is used for both gaits and all speeds. All concepts show that mimicking human ankle joint behavior in running and walking at higher speeds is still challenging for single motor devices.
  • Measuring End-Point Stiffness by Means of a Modular Mechatronic System Authors: Masia, Lorenzo; Squeri, Valentina; Sandini, Giulio; Morasso, Pietro Giovanni
    human arm muscular stiffness measurement is often a complex procedure which is of great interest for many disciplines from biomechanics to medicine and robotics. Modulation of impedance represents the principal mechanism underlying control of movements and interaction with external environment. Past literature proposed several methods to estimate multijoint hand stiffness while postural maintaining and dynamic tasks, mainly performed by means of planar robotic manipulanda. Despite these approaches are still considered robust and accurate, the computational burden of the robotic controller and hardware limitations make them not easy to implement. In the present paper a novel mechanism conceived for measuring multijoint planar stiffness by in single trial and in a reduced execution time is described and tested in different configurations. The device consisted in a mechanical rotary mechanism which applies cyclic radial perturbation to human arm of a known displacement and the force is acquired by means of a 6-axes commercial load cell. The outcomes suggest that the system is not only reliable in standalone mode but allows obtaining a reliable bi-dimensional estimation of arm stiffness even plugged in a planar manipulandum, dramatically reducing the amount of time for measurement and allowing to decouple the two controllers of the planar manipulator on which is mounted and the device itself.
  • AssistOn-SE: A Self-Aligning Shoulder-Elbow Exoskeleton Authors: Ergin, Mehmet Alper; Patoglu, Volkan
    We present AssistOn-SE, a novel powered exoskeleton for robot-assisted rehabilitation that allows for movements of the shoulder girdle as well as shoulder rotations. Automatically adjusting its joint axes, AssistOn-SE can enable a perfect match between human joint axes and the device axes, not only guaranteeing ergonomy and comfort throughout the therapy, but also extending the usable range of motion for the shoulder joint. Moreover, the adjustability feature significantly shortens the setup time required to attach the patient to the exoskeleton, allowing more effective time be spend on exercises instead of wasting this valuable resource for adjustments. Back-driveable design of AssistOn-SE supports both passive translational movements of the center of glenohumeral joint and independent active control of these degrees of freedom. Thanks to this property, glenohumeral mobilization and scapular stabilization exercises can also be delivered with AssistOn-SE, extending the type of therapies that can be administered using upper-arm exoskeletons. We introduce the design of the exoskeleton and present the kinematic analysis of its self-aligning joint. We also provide implementation details for an early prototype as well as some experimental results detailing range of motion of the device and its ability to track movements of the shoulder girdle.

Modular Robots & Multi-Agent Systems

  • Programming and Controlling Self-Folding Sheets Authors: An, Byoungkwon; Rus, Daniela
    This paper describes a robot in the form of a self-folding sheet that is capable of origami-style autonomous folding. We describe the hardware device we designed and fabricated. The device is a sheet with a box-pleated pattern and an integrated electronic substrate and actuators. The sheet is programmed and controlled to achieve different shapes using an idea called sticker programming. We describe the sticker controller and its instantiation. We also describe the algorithms for programming and controlling a given sheet to self-fold into a desired shape. Finally we present experiments with a 4x4 hardware device and an 8x8 hardware device.
  • Task Allocation with Executable Coalitions in Multirobot Tasks Authors: Zhang, Yu (Tony); Parker, Lynne
    In our prior work, we proposed the IQ-ASyMTRe architecture with a measure of information quality to reason about forming coalitions in multirobot tasks. The formed coalitions are guaranteed to be executable, given the current configurations of the robots and environment. A cost and a quality measure are associated with each coalition to further determine its utility for the task. In this paper, we show that IQ-ASyMTRe-like architectures can be utilized to significantly reduce the overall complexity of task allocation by considering only executable coalitions. For implementation, we apply a layering technique such that most existing methods for task allocation can be easily incorporated. Furthermore, we introduce a general process to address situations in which no executable coalitions are available for certain tasks, and integrate it with IQ-ASyMTRe to achieve more autonomy. Such an approach is able to autonomously decompose unsatisfied preconditions of the required task behaviors into satisfiable components, in order to generate partial order plans for them accordingly. We show how this process can be implemented using a market-based approach. Simulation results are provided to demonstrate these techniques.
  • Mathematical Programming for Multi-Vehicle Motion Planning Problems Authors: Abichandani, Pramod; Ford, Gabriel; Benson, Hande; Kam, Moshe
    Real world Multi-Vehicle Motion Planning (MVMP) problems require the optimization of suitable performance measures under an array of complex and challenging constraints involving kinematics, dynamics, communication connectivity, target tracking, and collision avoidance. The general MVMP problem can thus be formulated as a mathematical program (MP). In this paper we present a mathematical programming (MP) framework that captures the salient features of the general MVMP problem. To demonstrate the use of this framework for the formulation and solution of MVMP problems, we examine in detail four representative works and summarize several other related works. As MP solution algorithms and associated numerical solvers continue to develop, we anticipate that MP solution techniques will be applied to an increasing number of MVMP problems and that the framework and formulations presented in this paper may serve as a guide for future MVMP research.
  • Decentralized Multi-Robot Cooperation with Auctioned POMDPs Authors: Capitan, Jesus; Spaan, Matthijs; Merino, Luis; Ollero, Anibal
    Planning under uncertainty faces a scalability problem when considering multi-robot teams, as the information space scales exponentially with the number of robots. To address this issue, this paper proposes to decentralize multi-agent Partially Observable Markov Decision Process (POMDPs) while maintaining cooperation between robots by using POMDP policy auctions. Also, communication models in the multi-agent POMDP literature severely mismatch with real inter-robot communication. We address this issue by applying a decentralized data fusion method in order to efficiently maintain a joint belief state among the robots. The paper focuses on a cooperative tracking application, in which several robots have to jointly track a moving target of interest. The proposed ideas are illustrated in real multi-robot experiments, showcasing the flexible and robust cooperation that our techniques can provide.