TechTalks from event: Technical session talks from ICRA 2012

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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.

Estimation and Control for UAVs

  • Autonomous Indoor 3D Exploration with a Micro-Aerial Vehicle Authors: Shen, Shaojie; Michael, Nathan; Kumar, Vijay
    In this paper, we propose a stochastic differential equation-based exploration algorithm to enable exploration in three-dimensional indoor environments with a payload constrained micro-aerial vehicle (MAV). We are able to address computation, memory, and sensor limitations by considering only the known occupied space in the current map. We determine regions for further exploration based on the evolution of a stochastic differential equation that simulates the expansion of a particle system with Langevin dynamics. The regions of most significant particle expansion correlate to unexplored space. After identifying and processing these regions, the autonomous MAV navigates to these locations to enable fully autonomous exploration. The performance of the approach is demonstrated through numerical simulations and experimental results in single- and multi-floor indoor experiments.
  • Wind Field Estimation for Autonomous Dynamic Soaring Authors: Langelaan, Jack W.; Spletzer, John; Montella, Corey; Grenestedt, Joachim
    A method for distributed parameter estimation of a previously unknown wind field is described. The application is dynamic soaring for small unmanned air vehicles, which severely constrains available computing while simultaneously requiring updates that are fast compared with a typical dynamic soaring cycle. A polynomial parameterization of the wind field is used, allowing implementation of a linear Kalman filter for parameter estimation. Results of Monte Carlo simulations show the effectiveness of the approach. In addition, in-flight measurements of wind speeds are compared with data obtained from video tracking of balloon launches to assess the accuracy of wind field estimates obtained using commercial autopilot modules.
  • Decentralized Formation Control with Variable Shapes for Aerial Robots Authors: Turpin, Matthew; Michael, Nathan; Kumar, Vijay
    We address formation control for a team of quadrotors in which the robots follow a specified group trajectory while safely changing the shape of the formation according to specifications. The formation is prescribed by shape vectors which dictate the relative separations and bearings between the robots, while the group trajectory is specified as the desired trajectory of a leader or a virtual robot in the group. Each robot plans its trajectory independently based on its local information of neighboring robots which includes both the neighbor's planned trajectory and an estimate of its state. We show that the decentralized trajectory planners (a) result in consensus on the planned trajectory for predefined shapes and (b) achieve safe reconfiguration when changing shapes.
  • Versatile Distributed Pose Estimation and Sensor Self-Calibration for an Autonomous MAV Authors: Weiss, Stephan; Achtelik, Markus W.; Chli, Margarita; Siegwart, Roland
    In this paper, we present a versatile framework to enable autonomous flights of a Micro Aerial Vehicle (MAV) which has only slow, noisy, delayed and possibly arbitrar- ily scaled measurements available. Using such measurements directly for position control would be practically impossible as MAVs exhibit great agility in motion. In addition, these measurements often come from a selection of different onboard sensors, hence accurate calibration is crucial to the robustness of the estimation processes. Here, we address these problems using an EKF formulation which fuses these measurements with inertial sensors. Compared to existing approaches we do not only estimate pose and velocity of the MAV, but also states such as sensor biases, scale of the position estimate and self (inter- sensor) calibration in real-time. Furthermore, we show that it is possible to obtain a yaw estimate from position measurements only. We demonstrate that the proposed framework is capable of running entirely onboard a MAV boosting its autonomy, performing state prediction at the rate of 1 kHz. Our results illustrate that this approach is able to handle measurement delays (up to 500ms), noise (std. deviation up to 20 cm) and slow update rates (as low as 1 Hz) while dynamic maneuvers are still possible. We present a detailed quantitative performance evaluation of the real system under the influence of different disturbance parameters and different sensor setups to highlight the versatility of our approach
  • Probabilistic Velocity Estimation for Autonomous Miniature Airships Using Thermal Air Flow Sensors Authors: Mueller, Joerg; Paul, Oliver; Burgard, Wolfram
    Recently, autonomous miniature airships have become a growing research field. Whereas airships are attractive as they can move freely in the three-dimensional space, their high-dimensional state space and the restriction to small and lightweight sensors are demanding constraints with respect to self-localization. Furthermore, their complex second-order kinematics makes the estimation of their pose and velocity through dead reckoning odometry difficult and imprecise. In this paper, we consider the problem of estimating the velocity of a miniature blimp with lightweight air flow sensors. We present a probabilistic sensor model that accurately models the uncertainty of the flow sensors and thus allows for robust state estimation using a particle filter. In experiments carried out with a real airship we demonstrate that our method precisely estimates the velocity of the blimp and outperforms the standard velocity estimates of the motion model as applied in many existent autonomous blimp navigation systems.
  • Efficient Human-Like Walking for the COmpliant Humanoid COMAN Based on Kinematic Motion Primitives (kMPs) Authors: Moro, Federico Lorenzo; Tsagarakis, Nikolaos; Caldwell, Darwin G.
    Research in humanoid robotics in recent years has led to significant advances in terms of the ability to walk and even run. Yet, despite the general achievements in locomotion and control, energy efficiency is still one important area that requires further attention, especially as it is one of the major steeping stones leading to increased autonomy. This paper examines, and quantifies, the energetic benefits of introducing passive compliance into bipedal locomotion using COMAN, an intrinsically COmpliant huMANoid robot. The novelty of the method proposed consists of: i) the use of a method of gait synthesis based on kinematic Motion Primitives (kMPs) extracted from human, ii) the frequency tuning of the resultant trajectories, to excite the physical elasticity of the system, and the subsequent analysis of the energetic performance of the robot. The motivation is to assess the possible effects of using dynamic human-like, and human derived, trajectories, with significant Center of Mass (CoM) vertical displacement, regulated in frequency around the frequency band of the system resonances, on the excitation of the compliant actuators, and subsequently to measure and verify any energetic benefit. Experimental results show that if the gait frequency is close to one of the main resonant frequencies of the robot, then the total work contribution of the elastic compliant element to the overall motion of the robot is positive (15% of the work required is generated by the springs).
  • State Estimation for Aggressive Flight in GPS-Denied Environments Using Onboard Sensing Authors: Bry, Adam (Massachusetts Institute of Technology), Bachrach, Abraham (Massachusetts Institute of Technology,), Roy, Nicholas (Massachusetts Institute of Technology)
    In this paper we present a state estimation method based on an inertial measurement unit (IMU) and a planar laser range finder suitable for use in real-time on a fixed-wing micro air vehicle (MAV). The algorithm is capable of maintaing accurate state estimates during aggressive flight in unstructured 3D environments without the use of an external positioning system. Our localization algorithm is based on an extension of the Gaussian Particle Filter. We partition the state according to measurement independence relationships and then calculate a pseudo-linear update which allows us to use 20x fewer particles than a naive implementation to achieve similar accuracy in the state estimate. We also propose a multi-step forward fitting method to identify the noise parameters of the IMU and compare results with and without accurate position measurements. Our process and measurement models integrate naturally with an exponential coordinates representation of the attitude uncertainty. We demonstrate our algorithms experimentally on a fixed-wing vehicle flying in a challenging indoor environment.