TechTalks from event: Technical session talks from ICRA 2012

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Networked Robots

  • Compensation of Packet Loss for a Network-Based Rehabilitation System Authors: Bae, Joonbum; Zhang, Wenlong; Tomizuka, Masayoshi
    In this paper, a network-based rehabilitation system is proposed to increase mobility of a rehabilitation system and to enable tele-rehabilitation. Control algorithms and rehabilitation strategies distributed at the central location (physical therapist) and the local site (patient) communicate over wireless network to realize a network-based rehabilitation system. To deal with possible packet losses over wireless network, a modified linear quadratic Gaussian (LQG) controller and a disturbance observer (DOB) are applied. The performance of the proposed system and control algorithms is verified by simulation and experiment with an actual knee rehabilitation system. The simulation and experiment results show that the network-based rehabilitation system with the proposed control schemes can generate the desired assistive torque accurately in presence of packet losses.
  • Motion Planning for Robust Wireless Networking Authors: Fink, Jonathan; Ribeiro, Alejandro; Kumar, Vijay
    We propose an architecture and algorithms for maintaining end-to-end network connectivity for autonomous teams of robots. By adopting stochastic models of point-to-point wireless communication and computing robust solutions to the network routing problem, we ensure reliable connectivity during robot movement in complex environments. We fully integrate the solution to network routing with the choice of node positions through the use of randomized motion planning techniques. Experiments demonstrate that our method succeeds in navigating a complex environment while ensuring that end-to-end communication rates meet or exceed prescribed values within a target failure tolerance.
  • Decentralised Information Gathering with Communication Costs Authors: Kassir, Abdallah; Fitch, Robert; Sukkarieh, Salah
    Advantages of decentralised decision making systems for multi-agent robotic tasks are limited by the heavy demand they impose on communication. This paper presents an approach to control communication for the LQ team problem, namely a team of agents with linear dynamics and quadratic team cost. Communication costs are added to the objective of the LQ optimal control linear matrix inequality formulation, allowing for a well-defined balancing of communication costs and team performance. Results show a reduction in communication consistent with the specified cost and in a manner that upholds team performance relative to the reduced communication footprint. The applicability of the approach has also been extended to information gathering tasks through local LQ approximations along the agents’ paths. Simulation testing on a sample two-agent problem shows a 40% reduction in communication with negligible impact on performance.
  • Decentralized Connectivity Maintenance for Networked Lagrangian Dynamical Systems Authors: Sabattini, Lorenzo; Secchi, Cristian; Chopra, Nikhil
    In order to accomplish cooperative tasks, multi-robot systems are required to communicate among each other. Thus, maintaining the connectivity of the communication graph is a fundamental issue. Connectivity maintenance has been extensively studied in the last few years, but generally considering only kinematic agents. In this paper we will introduce a control strategy that, exploiting a decentralized procedure for the estimation of the algebraic connectivity of the graph, ensures the connectivity maintenance for groups of Lagrangian systems. The control strategy is validated by means of analytical proofs and simulation results.
  • Multi-Target Tracking Using Distributed SVM Training Over Wireless Sensor Networks Authors: Kim, Woojin; YOO, Jae Hyun; Kim, H. Jin
    In this paper, we propose to use distributed support vector machine (SVM) training to solve a multi-target tracking problem in wireless sensor networks. We employ gossip-based incremental SVM to obtain the discriminant function. By gossiping the support vectors with neighboring sensor nodes, the local SVM training results can achieve the agreement of the sub-optimal discriminant planes. After training the local SVM at each node, we can calculate the posterior probability of the existence of the targets using Platt's method. By maximum a posterior (MAP), the target trajectories are estimated. In order to validate the proposed tracking framework in wireless sensor networks, we perform two different target-tracking experiments. The experimental results demonstrate that the proposed procedure provides a good estimator, and supports the feasibility of applying the distributed SVM training to the target tracking problems.
  • A Dual-Use Visible Light Approach to Integrated Communication and Localization of Underwater Robots with Application to Non-Destructive Nuclear Reactor Inspection Authors: Rust, Ian; Asada, Harry
    Visible light communication systems have gained prominence as a method for wireless underwater communications. This is because these systems are capable of long distance communications in water with high bandwidths. A requirement of visible light systems, however, is consistent line of sight to maintain a communication link. This arises from the directional nature of visible light emitters and detectors. One solution to this problem is to implement feedback control in order to “point” visible light emitters and detectors at one another. This in turn requires precise estimation of the relative locations of these two components as a feedback signal. In this work, a system is presented that uses the modulated light signal both as a medium with which to carry data and as a reference upon which to base the localization of a mobile robot. This is therefore a dual-use system, for both communication and localization. First, this paper presents the architecture of a dual-use visible light communication and localization system. The localization is carried out using an Extended Kalman Filter (EKF) algorithm. Then, a planar version of this dual-use system is tested, demonstrating the feasibility and effectiveness of the dual-use approach.

Grasping and Manipulation

  • Movement-Aware Action Control - Integrating Symbolic and Control-Theoretic Action Execution Authors: Kresse, Ingo; Beetz, Michael
    In this paper we propose a bridge between a symbolic reasoning system and a task function based controller. We suggest to use modular position- and force constraints, which are represented as action-object-object triples on the symbolic side and as task function parameters on the controller side. This description is a considerably more fine-grained interface than what has been seen in high-level robot control systems before. It can preserve the 'null space' of the task and make it available to the control level. We demonstrate how a symbolic description can be translated to a control-level description that is executable on the robot. We describe the relation to existing robot knowledge bases and indicate information sources for generating constraints on the symbolic side. On the control side we then show how our approach outperforms a traditional controller, by exploiting the task's null space, leading to a significantly extended work space.
  • Physically-Based Grasp Quality Evaluation under Uncertainty Authors: Kim, Junggon; Pollard, Nancy S
    In this paper new grasp quality measures considering both object dynamics and pose uncertainty are proposed. Dynamics of the object is incorporated into our grasping simulation to capture the change of its pose and contact points during grasping. Pose uncertainty is considered by running multiple simulations starting from slightly different initial poses sampled from a probability distribution model. A simple robotic grasping strategy is simulated and the quality score of the resulting grasp is evaluated from the simulation result. The effectiveness of the new quality measures on predicting the actual grasp success rate is shown through a real robot experiment.
  • Bimanual Regrasping from Unimanual Machine Learning Authors: Balaguer, Benjamin; Carpin, Stefano
    While unimanual regrasping has been studied extensively, either by regrasping in-hand or by placing the object on a surface, bimanual regrasping has seen little attention. The recent popularity of simple end-effectors and dual-manipulator platforms makes bimanual regrasping an important behavior for service robots to possess. We solve the challenge of bimanual regrasping by casting it as an optimization problem, where the objective is to minimize execution time. The optimization problem is supplemented by image processing and a unimanual grasping algorithm based on machine learning that jointly identify two good grasping points on the object and the proper orientations for each end-effector. The optimization algorithm exploits this data by finding the proper regrasp location and orientation to minimize execution time. Influenced by human bimanual manipulation, the algorithm only requires a single stereo image as input. The efficacy of the method we propose is demonstrated on a dual manipulator torso equipped with Barrett WAM arms and Barrett Hands.
  • Planar, Bimanual, Whole-Arm Grasping Authors: Seo, Jungwon; Kim, Soonkyum; Kumar, Vijay
    We address the problem of synthesizing planar, bimanual, whole-arm grasps by developing the abstraction of an open chain gripper, an open, planar chain of rigid links and revolute joints contacting a planar, polygonal object, and introducing the concept of a generalized contact. Since two generalized contacts suffice for planar grasps, we leverage previous work on caging and immobilization for two contact grasps to construct an algorithm which synthesizes contact configurations for stable grasping. Simulations show that our methodology can be applied to grasp a wide range of planar objects without relying on special-purpose end-effectors. Representative experiments with the PR2 humanoid robot illustrate that this approach is practical.
  • Identification of Contact Formations: Resolving Ambiguous Force Torque Information Authors: Hertkorn, Katharina; Preusche, Carsten; Roa, Maximo A.
    This paper presents the identification of contact formations using force torque information. As force torque measurements do not map uniquely to their corresponding contact formations, three steps are performed: A contact formation graph is augmented with a similarity index that reflects the similarity of contact formations with respect to their spanned wrench spaces. Prior to that, the wrench space for each contact formation is computed automatically. A particle filter is used to represent the likeliness of a contact formation given a force torque measurement. Finally, this probability distribution is resolved taking the similarity index, the transitions of the contact formation graph and the history of identified contact formations into account. This allows to recognize the order of demonstrated contact formations by a measured set of forces and torques. The approach is verified by experiments.

Motion Planning II

  • Modelling Search with a Binary Sensor Utilizing Self-Conjugacy of the Exponential Family Authors: Bonnie, Devin; Candido, Salvatore; Bretl, Timothy; Hutchinson, Seth
    In this paper, we consider the problem of an autonomous robot searching for a target object whose position is characterized by a prior probability distribution over the workspace (the object prior). We consider the case of a continuous search domain, and a robot equipped with a single binary sensor whose ability to recognize the target object varies probabilistically as a function of the distance from the robot to the target (the sensor model). We show that when the object prior and sensor model are taken from the exponential family of distributions, the searcher's posterior probability map for the object location belongs to a finitely parameterizable class of functions, admitting an exact representation of the searcher's evolving belief. Unfortunately, the cost of the representation grows exponentially with the number of stages in the search. For this reason, we develop an approximation scheme that exploits regularized particle filtering methods. We present simulation studies for several scenarios to demonstrate the effectiveness of our approach using a simple, greedy search strategy.
  • On the Probabilistic Completeness of the Sampling-Based Feedback Motion Planners in Belief Space Authors: Agha-mohammadi, Ali-akbar; Chakravorty, Suman; Amato, Nancy
    This paper extends the concept of “probabilistic completeness” defined for the motion planners in the state space (or configuration space) to the concept of “probabilistic completeness under uncertainty” for the motion planners in the belief space. Accordingly, an approach is proposed to verify the probabilistic completeness of the sampling-based planners in the belief space. Finally, through the proposed approach, it is shown that under mild conditions the samplingbased method constructed based on the abstract framework of FIRM (Feedback-based Information-state Roadmap Method) are probabilistically complete under uncertainty.
  • Egress: An Online Path Planning Algorithm for Boundary Exploration Authors: Guruprasad, KR; Dasgupta, Raj
    We consider the problem of navigating a mobile robot that is located at any arbitrary point within a bounded environment, to a point on the environment's outer boundary and then, using the robot to explore the perimeter of the boundary. The environment can have obstacles in it and the location and size of these obstacles are not provided a priori to the robot. We present an online path planning algorithm to solve this problem that requires very simple behaviors and computation on the robot. We analytically prove that by using our algorithm, the robot is guaranteed to reach and explore the outer boundary of the environment within a finite time.
  • Shortest Paths for Visibility-Based Pursuit-Evasion Authors: Stiffler, Nicholas; O'Kane, Jason
    We present an algorithm that computes a minimal-cost pursuer trajectory for a single pursuer to solve the visibility-based pursuit-evasion problem in a simply-connected two dimensional environment. This algorithm improves upon the known algorithm of Guibas, Latombe, LaValle, Lin, and Motwani, which is complete but not optimal. Our algorithm uses a Tour of Segments (ToS) subroutine to construct a pursuer path that minimizes the distance traveled by the pursuer while guaranteeing that all evaders in the environment will be captured. We have implemented our algorithm in simulation and provide results.
  • Hierarchical Motion Planning with Kinodynamic Feasibility Guarantees: Local Trajectory Planning Via Model Predictive Control Authors: Cowlagi, Raghvendra; Tsiotras, Panagiotis
    Motion planners for autonomous vehicles often involve a two-level hierarchical structure consisting of a high-level, discrete planner and a low-level trajectory generation scheme. To ensure compatibility between these two levels of planning, we previously introduced a motion planning framework based on multiple-edge transition costs in the graph used by the discrete planner. This framework is enabled by a special local trajectory generation problem, which we address in this paper. In particular, we discuss a trajectory planner based on model predictive control for complex vehicle dynamical models. We demonstrate the efficacy of our overall motion planning approach via examples involving non-trivial vehicle models and complex environments, and we offer comparisons of our motion planner with state-of-the-art randomized sampling-based motion planners.
  • Using State Dominance for Path Planning in Dynamic Environments with Moving Obstacles Authors: Gonzalez, Juan Pablo; Dornbush, Andrew; Likhachev, Maxim
    Path planning in dynamic environments with moving obstacles is computationally complex since it requires modeling time as an additional dimension. While in other domains there are state dominance relationships that can significantly reduce the complexity of the search, in dynamic environments such relationships do not exist. This paper presents a novel state dominance relationship tailored specifically for dynamic environments, and presents a planner that uses that property to plan paths over ten times faster than without using state dominance.