TechTalks from event: Technical session talks from ICRA 2012

Conference registration code to access these videos can be accessed by visiting this link: PaperPlaza. Step-by-step to access these videos are here: step-by-step process .
Why some of the videos are missing? If you had provided your consent form for your video to be published and still it is missing, please contact support@techtalks.tv

Surveillance

  • A Game Theoretical Approach to Finding Optimal Strategies for Pursuit Evasion in Grid Environments Authors: Amigoni, Francesco; Basilico, Nicola
    Pursuit evasion problems, in which evading targets must be cleared from an environment, are encountered in surveillance and search and rescue applications. Several works have addressed variants of this problem in order to study strategies for the pursuers. As a common trait, many of these works present results in the general form: given some assumptions on the environment, on the pursuers, and on the evaders, upper and lower bounds are calculated for the time needed for (the probability of, the resources needed for, ...) clearing the environment. The question ''what is the optimal strategy for a given pursuer in a given environment to clear a given evader?'' is left largely open. In this paper, we propose a game theoretical framework that contributes in finding an answer to the above question in a version of the pursuit evasion problem in which the evader enters and exits a grid environment and the pursuer has to intercept it along its path. We adopt a criterion for optimality related to the probability of capture. We experimentally evaluate the proposed approach in simulated settings and we provide some hints to generalize the framework to other versions of the pursuit evasion problem.
  • Online Patrolling Using Hierarchical Spatial Representations Authors: Basilico, Nicola; Carpin, Stefano
    Unmanned Aerial Vehicles (UAVs) can be an effective technology for security applications involving patrolling and search missions. Defining online patrolling strategies for UAVs presents challenges related both to classical patrolling, as periodic monitoring of the environment, and to search, as accurate localization and identification of the mission-related activities. In this paper, we deal with this problem considering probabilistic intrusions and a variable resolution sensing model that naturally applies to the domain of UAVs. We present three online single--robot patrolling strategies exploiting a variable resolution paradigm to represent the environment that has recently shown promising results for search problems. The approach uses a hierarchical representation based on probabilistic quadtrees that allows UAVs to tradeoff sensing accuracy with sensing area. The model is extended by adding stochastic arrivals of intruders in space and time. Obtained results validate this approach for online patrolling against approaches based on uniform grids.
  • Laser-Based Intelligent Surveillance and Abnormality Detection in Extremely Crowded Scenarios Authors: Song, Xuan; Shao, Xiaowei; Zhang, Quanshi; Shibasaki, Ryosuke; Zhao, Huijing; Zha, Hongbin
    Abnormal activity detection plays a crucial role in surveillance applications, and a surveillance system that can perform robustly in the extremely crowded area has become an urgent need for public security. In this paper, we propose a novel laser-based system which can simultaneously perform the tracking, semantic scene learning and abnormality detection in the large and crowded environment. In our system, a novel abnormality detection model is proposed, and it considers and combines various factors that will influence human activity. Moreover, this model intensively investigate the relationship between pedestrians' social behaviors and their walking scenarios. We successfully applied the proposed system to the JR subway station of Tokyo, which can cover a 60*35m area, robustly track more than 180 targets at the same time and simultaneously perform the online semantic scene learning and abnormality detection with no human intervention.
  • Strong Shadow Removal Via Patch-Based Shadow Edge Detection Authors: Wu, Qi
    Detecting objects in shadows is a challenging task in computer vision. For example, in clear path detection application, strong shadows on the road confound the detection of the boundary between clear path and obstacles, making clear path detection algorithms less robust. Shadow removal, relies on the classification of edges as shadow edges or non-shadow edges. We present an algorithm to detect strong shadow edges, which enables us to remove shadows. By analyzing the patch-based characteristics of shadow edges and non-shadow edges (e.g., object edges), the proposed detector can discriminate strong shadow edges from other edges in images by learning the distinguishing characteristics. In addition, spatial smoothing is used to further improve shadow edge detection. Numerical experiments show convincing results that shadows on the road are either removed or attenuated with few visual artifacts, which benefits the clear path detection. In addition, we show that the proposed method outperforms the state-of-art algorithms in different conditions.
  • Integrated Probabilistic Generative Model for Detecting Smoke on Visual Images Authors: Vidal-Calleja, Teresa A.; Agamennoni, Gabriel
    Early fire detection is crucial to minimise damage and save lives. Video surveillance smoke detectors do not suffer from transport delays and can cover large areas. The smoke detection on images is, however, a difficult problem due the variability of smoke density, lighting conditions, background clutter, and unstable patterns. In order to solve this problem, we propose a novel unsupervised object classifier. Single visual features are classified using a model that simultaneously creates a codebook and categorises the smoke using a bag-of-words paradigm based on LDA model. Our algorithm can also tell the amount of smoke present on the image. Multiple image sequences from different cameras are used to show the viability of the proposed approach. Our experiments show that the model generalises well for different cameras, perspectives and scales.
  • Localization in Indoor Environments by Querying Omnidirectional Visual Maps Using Perspective Images Authors: Pedro, Vítor Manuel; Lourenço, Miguel; Barreto, João P.
    This article addresses the problem of image-based localization in a indoor environment. The localization is achieved by querying a database of omnidirectional images that constitutes a detailed visual map of the building where the robot operates. Omnidirectional cameras have the advantage, when compared to standard perspectives, of capturing in a single frame the entire visual content of a room. This, not only speeds up the process of acquiring data for creating the map, but also favors scalability by significantly decreasing the size of the database. The problem is that omnidirectional images have strong non-linear distortion, which leads to poor retrieval results when the query images are standard perspectives. This paper reports for the first time thorough experiments in using perspectives to index a database of para-catadioptric images for the purpose of robot localization. We propose modifications to the SIFT algorithm that significantly improve point matching between the two types of images with positive impact in the recognition based in visual words. We also compare the classical bags-of-words against the recent framework of visual-phrases, showing that the latter outperforms the former.

Environment Mapping

  • A Dependable Perception-Decision-Execution Cycle for Autonomous Robots Authors: Gspandl, Stephan; Podesser, Siegfried; Reip, Michael; Steinbauer, Gerald; Wolfram, Máté
    The tasks robots are employed to achieve are becoming increasingly complex, demanding for dependable operation, especially if robots and humans share common space. Unfortunately, for these robots non-determinism is a severe challenge. Malfunctioning hardware, inaccurate sensors, exogenous events and incomplete knowledge lead to inconsistencies in the robot’s belief about the world. Thus, a robot has to cope efficiently with such adversities while sensing its surroundings, deciding what to do next, and executing its decisions. In this paper, we present such a dependable perception-decision-execution cycle. It employs a belief management system that performs history-based diagnosis in the high-level control module. The belief management enables robots to detect these inconsistencies and thus operate successfully in non-deterministic environments. The main contributions of this paper are a robot design extending the high-level control IndiGolog by a belief management allowing to deal with a large variety of faults in a unique way, together with an evaluation on a real robot system.
  • Efficient Change Detection in 3D Environment for Autonomous Surveillance Robots based on Implicit Volume Authors: Wilson Vieira, Antonio; Drews Jr, Paulo; Campos, Mario Montenegro
    The ability to detect changes in the environment is an essential trait for robots commissioned to work in several applications. In surveillance, for instance, a robot needs to detect meaningful changes in the environment which is achieved by comparing current sensory data with previously acquired information from the environment. The large amount of sensory data, which are often complex and very noisy, explains the inherent difficulty of this task. As an attempt to tackle this hard problem, we present an efficient method to automatically segment 3D data, corrupted with noise and outliers, into an implicit volume bounded by a surface. The method makes it possible to efficiently apply Boolean operations to 3D data in order to detect changes and to update existing maps. We show that our approach is powerful, albeit simple, with linear time complexity. The method has been validated through several trials using mobile robots operating in real environments and their performance was compared to another state-of-art algorithm. Experimental results demonstrate the performance of the proposed method, both in accuracy and computational cost.
  • Stochastic Source Seeking in Complex Environments Authors: Atanasov, Nikolay; Le Ny, Jerome; Michael, Nathan; Pappas, George J.
    The objective of source seeking problems is to determine the minimum of an unknown signal field, which represents a physical quantity of interest, such as heat, chemical concentration, or sound. This paper proposes a strategy for source seeking in a noisy signal field using a mobile robot and based on a stochastic gradient descent algorithm. Our scheme does not require a prior map of the environment or a model of the signal field and is simple enough to be implemented on platforms with limited computational power. We discuss the asymptotic convergence guarantees of algorithm and give specific guidelines for its application to mobile robots in unknown indoor environments with obstacles. Both simulations and real-world experiments were carried out to evaluate the performance of our approach. The results suggest that the algorithm has good finite time performance in complex environments.
  • Robust Sound Localization for Various Platform of Robots Using TDOA Map Adaptation Authors: Shen, Guanghu, Guanghu; Hwang, Dohyung; Nguyen, Quang; Choi, Jongsuk
    In realistic environments, mismatches between the calculated angle-TDOA map with its real exact values are the major reason of performance degradation in sound localization. Usually, those mismatches come from some certain configuration errors or deviations caused by the change of environments. To reduce those mismatches, in this paper we proposed an angle-TDOA map adaptation method, which can achieve the robust sound localization in various robot platforms (i.e., various types of microphone array configuration). Especially, the proposed method is possible to easily apply to the sound localization system by using only several sound sources which generated from some known directions. As a result, the proposed method not only showed a good localization performance, and the program running time is also very short.

Octopus-Inspired Robotics

  • A General Mechanical Model for Tendon-Driven Continuum Manipulators Authors: Renda, Federico; Laschi, Cecilia
    Recently, continuum manipulators have drawn a lot of interest and effort from the robotic community, nevertheless control and modeling of such manipulators are still a challenging task especially because they require a continuum approach. In this paper, a general mechanical model with a geometrically exact approach for tendon-driven continuum manipulators is presented. This model can be applied to a wide range of manipulators thanks to the generality of the parameters which can be set. The approach proposed could as well be a powerful tool for developing the control strategy. The model is also capable of properly simulating the couple tendon drive, because it takes into account the torsion of the robot arm rather than neglecting it, as it is common practice in other existing models.
  • A Two Dimensional Inverse Kinetics Model of a Cable Driven Manipulator Inspired by the Octopus Arm Authors: Giorelli, Michele; Renda, Federico; Calisti, Marcello; Arienti, Andrea; Ferri, Gabriele; Laschi, Cecilia
    Control of soft robots remains nowadays a big challenge, as it does in the larger category of continuum robots. In this paper a direct and inverse kinetics models are described for a non-constant curvature structure. A major effort has been put recently in modelling and controlling constant curvature structures, such as cylindrical shaped manipulators. Manipulators with non-constant curvature, on the other hand, have been treated with a piecewise constant curvature approximation. In this work a non-constant curvature manipulator with a conical shape is built, taking inspiration from the anatomy of the octopus arm. The choice of a conical shape manipulator made of soft material is justified by its enhanced capability in grasping objects of different sizes. A different approach from the piecewise constant curvature approximation is employed for direct and inverse kinematics model. A continuum geometrically exact approach for direct kinetics model and a Jacobian method for inverse case are proposed. They are validated experimentally with a prototype soft robot arm moving in water. Results show a desired tip position in the task-space can be achieved automatically with a satisfactory degree of accuracy.
  • Characterizing the Stiffness of a Multi-Segment Flexible Arm During Motion Authors: Held, David; Yekutieli, Yoram; Flash, Tamar
    A number of robotic studies have recently turned to biological inspiration in designing control schemes for flexible robots. Examples of such robots include continuous manipulators inspired by the octopus arm. However, the control strategies used by an octopus in moving its arms are still not fully understood. Starting from a dynamic model of an octopus arm and a given set of muscle activations, we develop a simulation technique to characterize the stiffness throughout a motion and at multiple points along the arm. By applying this technique to reaching and bending motions, we gain a number of insights that can help a control engineer design a biologically inspired impedance control scheme for a flexible robot arm. The framework developed is a general one that can be applied to any motion for any dynamic model. We also propose a theoretical analysis to efficiently estimate the stiffness analytically given a set of muscle activations. This analysis can be used to quickly evaluate the stiffness for new static configurations and dynamic movements.
  • Robotic Underwater Propulsion Inspired by the Octopus Multi-Arm Swimming Authors: Sfakiotakis, Michael; Kazakidi, Asimina; Pateromichelakis, Nikolaos; Ekaterinaris, John A.; Tsakiris, Dimitris
    The multi-arm morphology of octopus-inspired robotic systems may allow their aquatic propulsion, in addition to providing manipulation functionalities, and enable the development of flexible robotic tools for underwater applications. In the present paper, we consider the multi-arm swimming behavior of the octopus, which is different than their, more usual, jetting behavior, and is often used to achieve higher propulsive speeds, e.g., for chasing prey. A dynamic model of a robot with a pair of articulated arms is employed to study the generation of this mode of propulsion. The model includes fluid drag contributions, which we support by detailed Computational Fluid Dynamic analysis. To capture the basic characteristics of octopus multi-arm swimming, a sculling mode is proposed, involving arm oscillations with an asymmetric speed profile. Parametric simulations were used to identify the arm oscillation characteristics that optimize propulsion for sculling, as well as for undulatory arm motions. Tests with a robotic prototype in a water tank provide preliminary validation of our analysis.
  • Developing Sensorized Arm Skin for an Octopus Inspired Robot Authors: Hou, Jinping; Bonser, Richard
    soft skin artefacts made of knitted nylon reinforced silicon rubber were fabricated mimicking octopus skin. A combination of ecoflex 0030 and 0010 were used as matrix of the composite to obtain the right stiffness for the skin artefacts. Material properties were characterised using static uniaxial tension and scissors cutting tests. Two types of tactile sensors were developed to detect normal contact; one used quantum tunnelling composite materials and the second was fabricated from silicone rubber and a conductive textile. Sensitivities of the sensors were tested by applying different modes of loading and the soft sensors were incorporated into the skin prototype. Passive suckers were developed and tested against squid suckers. An integrated skin prototype with embedded deformable sensors and attached suckers developed for the arm of an octopus inspired robot is also presented.
  • Artificial Adhesion Mechanisms Inspired by Octopus Suckers Authors: Tramacere, Francesca; Beccai, Lucia; Mattioli, Fabio; Sinibaldi, Edoardo; Mazzolai, Barbara
    We present the design and development of novel suction cups inspired by the octopus suckers. Octopuses use suckers for remarkable tasks and they are capable to obtain a good reversible wet adhesion on different substrates. We investigated the suckers morphology that allow octopus to attach them to different wet surfaces to obtain the benchmarks for new suction cups showing similar performances. The investigation was performed by using non-invasive techniques (i.e. ultrasonography and magnetic resonance imaging). We acquired images of contiguous sections of octopus suckers, which were used to make a 3D reconstruction aimed to obtain a CAD model perfectly equivalent to the octopus sucker in terms of sizes and anatomical proportion. The 3D information was used to develop the first passive prototypes of the artificial suction cups made in silicone. Then, in accordance with Kier and Smith’s octopus adhesion model, we put in tension the water volume in the interior chamber of the artificial suction cup to obtain suction. The characterization of the passive sucker was addressed by measuring both the differential pressure between external and internal water volume of suction cup (~ 105) and the pull-off force applied to detach the substrates from the suction cup (~ 8N).