TechTalks from event: Technical session talks from ICRA 2012

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Mobile Manipulation: Planning & Control

  • Planning with Adaptive Dimensionality for Mobile Manipulation Authors: Gochev, Kalin; Safonova, Alla; Likhachev, Maxim
    Mobile manipulation planning is a hard problem composed of multiple challenging sub-problems, some of which require searching through large high-dimensional state-spaces. The focus of this work is on computing a trajectory to safely maneuver an object through an environment, given the start and goal configurations. In this work we present a heuristic search-based deterministic mobile manipulation planner, based on our recently-developed algorithm for planning with adaptive dimensionality. Our planner demonstrates reasonable performance, while also providing strong guarantees on completeness and suboptimality bounds with respect to the graph representing the problem.
  • Unifying Perception, Estimation and Action for Mobile Manipulation Via Belief Space Planning Authors: Kaelbling, Leslie; Lozano-Perez, Tomas
    In this paper, we describe an integrated strategy for planning, perception, state-estimation and action in complex mobile manipulation domains. The strategy is based on planning in the belief space of probability distribution over states. Our planning approach is based on hierarchical symbolic regression (pre-image back-chaining). We develop a vocabulary of fluents that describe sets of belief states, which are goals and subgoals in the planning process. We show that a relatively small set of symbolic operators lead to task-oriented perception in support of the manipulation goals.
  • Distributed Cooperative Object Attitude Manipulation Authors: Markdahl, Johan; Karayiannidis, Yiannis; Hu, Xiaoming; Kragic, Danica
    This paper proposes a local information based control law in order to solve the planar manipulation problem of rotating a grasped rigid object to a desired orientation using multiple mobile manipulators. We adopt a multi-agent systems theory approach and assume that: (i) the manipulators (agents) are capable of sensing the relative position to their neighbors at discrete time instances, (ii) neighboring agents may exchange information at discrete time instances, and (iii) the communication topology is connected. Control of the manipulators is carried out at a kinematic level in continuous time and utilizes inverse kinematics. The mobile platforms are assigned trajectory tracking tasks that adjust the positions of the manipulator bases in order to avoid singular arm configurations. Our main result concerns the stability of the proposed control law.
  • A Hybrid Control for Automatic Docking of Electric Vehicles for Recharging Authors: Petrov, Plamen; Boussard, clément; Ammoun, Samer; Nashashibi, Fawzi
    In this paper, we present the architecture of an innovative docking station for electric vehicles recharging and a hybrid control scheme for automatic docking of the vehicles. This work is a part of on-going project concerning the development of a smart charging station for electric vehicles equipped with an automated arm, which connect the vehicle to the charging station, and an infrared beacon system for localizing the automatically maneuvering vehicle in the docking area. The proposed control scheme combines time-optimal (bang-bang) control with continuous time-invariant nonlinear control, which stabilizes the vehicle to a small neighborhood of the docking point. Simulation and experimental results illustrate the effectiveness of the proposed controller
  • On Continuous Null Space Projections for Torque-Based, Hierarchical, Multi-Objective Manipulation Authors: Dietrich, Alexander; Albu-Schäffer, Alin; Hirzinger, Gerd
    The technological progress in the field of robotics results in more and more complex manipulators. However, having an increasing number of degrees of freedom raises the question of how to use them effectively. In turn, establishing manipulators in human environments, e.g., as service robots, calls for the fulfillment of various constraints and tasks at the same time. In the context of torque controlled robotic systems, we provide an approach to simultaneously deal with a multitude of tasks and constraints which are arranged in a hierarchy, utilizing the large number of actuated joints of the manipulator. To this end, we propose a continuous null space projection technique to consider unilateral constraints, singular Jacobian matrices and dynamic variations of the priority order within the hierarchical structure. We show that activating and deactivating tasks as well as crossing singularities does not lead to a discontinuous control law. Simulations and experiments on the humanoid Justin of the German Aerospace Center (DLR) validate our approach. The presented concept is supposed to contribute to whole-body control frameworks.

Simulation and Search in Grasping

  • Simulating Robot Handling of Large Scale Deformable Objects: Manufacturing of Unique Concrete Reinforcement Structures Authors: Cortsen, Jens; Jørgensen, Jimmy Alison; Soelvason, Dorthe; Petersen, Henrik Gordon
    Automatic offline programming of industrial robotic systems is becoming increasingly important due to the larger percentage of desired automation of low volume tasks. Often, such tasks may involve handling of items that can have rather large deflections which are important to take into account when doing offline programming. In this paper such a problem is presented, namely robotic assembly of unique concrete reinforcement structures. Reinforcement bars of 3 meters may deflect up to around 50cm. We illustrate experimentally how the reinforcement bar can be precisely modelled by a structure consisting of rigid parts connected by ”deflection joints”. Such a model can be directly integrated into existing physics simulation engines such as the Open Dynamics Engine (ODE). Finally, we discuss how the simulation will be used for automatic offline programming and present a video with a dynamic simulation of the reinforcement assembly process.
  • Hybrid Physics Simulation of Multi-Fingered Hands for Dexterous In-Hand Manipulation Authors: Hendrich, Norman; Scharfe, Hanno; Zhang, Jianwei
    Dextrous object manipulation with multi-fingered robot hands remains one of the key challenges of service robotics. So far, most theoretical approaches and simulators have concentrated on the search for and evaluation of static stable grasps, but with neither a model of the full hand-arm system nor the system dynamics. GraspIt! is probably the bestknown simulator of this kind. In this work we present a simulator that uses the JBullet physics engine to realistically model grasps with multi-fingered hands. It supports manipulation tasks based on a complete arm and hand system, with full calculation of hand and object dynamics. A hybrid dynamics and kinematics approach avoids the oscillations introduced by the different size scale of the arm and hand, so that force-closure grasps are possible in addition to form-closure grasps. The software includes detailed models of our 24-DOF Shadow Dextrous hand and the 6-DOF Mitsubishi PA-10 robot arm. A real-time interface allows us to prepare or to replay and analyze grasp experiments performed on our real robots.
  • Search-Based Planning for Dual-Arm Manipulation with Upright Orientation Constraints Authors: Cohen, Benjamin; Chitta, Sachin; Likhachev, Maxim
    Dual-arm manipulation is an increasingly important skill for robots operating in home, retail and industrial environments. Dual-arm manipulation is especially essential for tasks involving large objects which are harder to grasp and manipulate using a single arm. In this work, we address dual-arm manipulation of objects in indoor environments. We are particularly focused on tasks that involve an upright orientation constraint on the grasped object. Such constraints are often present in human environments, e.g. when manipulating a tray of food or a container with fluids. In this paper, we present a search-based approach that is capable of planning dual-arm motions, often within one second, in cluttered environments while adhering to the orientation constraints. Our approach systematically constructs a graph in task space and generates motions that are consistent across runs with similar start/goal configurations and are low-cost. These motions come with guarantees on completeness and bounds on the suboptimality with respect to the graph that encodes the planning problem. For many problems, the consistency of the generated motions is important as it helps make the actions of the robot more predictable for a human interacting with the robot.
  • Generalizing Grasps across Partly Similar Objects Authors: Detry, Renaud; Ek, Carl Henrik; Madry, Marianna; Piater, Justus; Kragic, Danica
    The paper starts by reviewing the challenges associated to grasp planning, and previous work on robot grasping. Our review emphasizes the importance of agents that generalize grasping strategies across objects, and that are able to transfer these strategies to novel objects. In the rest of the paper, we then devise a novel approach to the grasp transfer problem, where generalization is achieved by <i>learning</i>, from a set of grasp examples, a dictionary of object parts by which objects are often grasped. We detail the application of dimensionality reduction and unsupervised clustering algorithms to the end of identifying the size and shape of parts that often predict the application of a grasp. The learned dictionary allows our agent to grasp novel objects which share a part with previously seen objects, by matching the learned parts to the current view of the new object, and selecting the grasp associated to the best-fitting part. We present and discuss a proof-of-concept experiment in which a dictionary is learned from a set of synthetic grasp examples. While prior work in this area focused primarily on shape analysis (parts identified, e.g., through visual clustering, or salient structure analysis), the key aspect of this work is the emergence of parts from <i>both</i> object shape <i>and</i> grasp examples. As a result, parts intrinsically encode the intention of executing a grasp.
  • A Grasp Strategy with the Geometric Centroid of a Groped Object Shape Derived from Contact Spots Authors: Bae, Ji-Hun; Park, Sung-Woo; Kim, Doik
    This paper proposes a strategy for grasp and manipulation of unknown objects. In order to derive force relations of fingers, the groped shape of soft fingers is introduced. The groped shape is not equal to the real object shape, but is tightly related to the force equilibrium of fingers. By considering contact forces of the groped shape, a simple control parameters can be derived. The manipulation of an object is easily accomplished by embedding the concept of virtual centroid into the grasp control to redistribute internal forces. With these concepts, an object can be easily translated, rotated, and manipulated by relocating fingers. The proposed method is verified with experiments.
  • The Application of Particle Filtering to Grasping Acquisition with Visual Occlusion and Tactile Sensing Authors: Zhang, Li; Trinkle, Jeff
    Advanced grasp control algorithms could benefit greatly from accurate tracking of the object as well as an accurate all-around knowledge of the system when the robot attempts a grasp. This motivates our study of the G-SL(AM)2 problem, in which two goals are simultaneously pursued: object tracking relative to the hand and estimation of parameters of the dynamic model. We view the G-SL(AM)2 problem as a filtering problem. Because of stick-slip friction and collisions between the object and hand, suitable dynamic models exhibit strong nonlinearities and jump discontinuities. This fact makes Kalman filters (which assume linearity) and extended Kalman filters (which assume differentiability) inapplicable, and leads us to develop a particle filter. An important practical problem that arises during grasping is occlusion of the view of the object by the robot’s hand. To combat the resulting loss of visual tracking fidelity, we designed a particle filter that incorporates tactile sensor data. The filter is evaluated off-line with data gathered in advance from grasp acquisition experiments conducted with a planar test rig. The results show that our particle filter performs quite well, especially during periods of visual occlusion, in which it is much better than the same filter without tactile data.

Control of UAVs

  • Modeling and Control of a Quadrotor UAV with Tilting Propellers Authors: Ryll, Markus; Buelthoff, Heinrich H.; Robuffo Giordano, Paolo
    Standard quadrotor UAVs possess a limited mobility because of their inherent underactuation, i.e., availability of 4 independent control inputs (the 4 propeller spinning velocities) vs. the 6 dofs parameterizing the quadrotor position/orientation in space. As a consequence, the quadrotor pose cannot track an arbitrary trajectory over time (e.g., it can hover on the spot only when horizontal). In this paper, we propose a novel actuation concept in which the quadrotor propellers are allowed to tilt about their axes w.r.t. the main quadrotor body. This introduces an additional set of 4 control inputs which provides full actuation to the quadrotor position/orientation. After deriving the dynamical model of the proposed quadrotor, we formally discuss its controllability properties and propose a nonlinear trajectory tracking controller based on dynamic feedback linearization techniques. The soundness of our approach is validated by means of simulation results.
  • Bilateral Teleoperation of Underactuated Unmanned Aerial Vehicles: The Virtual Slave Concept Authors: Mersha, Abeje Y.; Stramigioli, Stefano; Carloni, Raffaella
    In this paper, we present haptic teleoperation of underactuated unmanned aerial vehicles by providing a multidimensional generalization of the virtual slave concept. The proposed control architecture is composed of high-level and low-level controllers. The high-level controller commands the vehicle to accomplish specific tasks and renders both the state and the environment of the vehicle to the operator through haptic feedback. The low-level controller interprets the command signals from the operator, regulates the dynamics of the vehicle and feeds back its state to the high-level loop. Passivity of the teleoperation loop is always ensured independently of the choice of implementation of the low-level controller and the configuration of the flying hardware by a passivity-enforcing supervisor, which associates every action of the slave with an energy expense that can only be made available from a multi-state energy tank. The effectiveness of the proposed algorithm is illustrated with simulations and experimental tests.
  • Tunable Impedance: A Semi-Passive Approach to Practical Motion Control of Insect-Inspired MAVs Authors: Mahjoubi, Hosein; Byl, Katie
    Research on insect-inspired flapping-wing micro-aerial vehicles (FWMAV) has grown steadily in the past decade, aiming to address unique challenges in morphological construction, force production, and control strategy. In particular, effective methods for motion control still remain an open problem. This paper analyzes the mechanical impedance properties of the joint and their role in rotation of the wing and force production. The results suggest that in addition to previously observed relationship between set point and drag [1], the average lift force is also related to the stiffness of the joint. Furthermore, as long as changes in impedance properties are small, net lift and drag production are almost independent. These relationships are the basis of ‘tunable impedance’ technique, a new approach to force/motion control in FWMAVs. A simple controller designed based on this method is used to simulate various flight maneuvers. The simulated MAV demonstrates exceptional performance, even in presence of measurement noise. This technique requires a fixed stroke profile for both wings, thus allowing to use a single stroke actuator – in a real MAV – with a bandwidth as low as the frequency of flapping. Impedance actuators also prove to have low bandwidth requirements.
  • Learning Hover with Scarce Samples Authors: Lau, Tak Kit; Liu, Yunhui
    Indoor aerial robots are useful in many applications due to their size, agility and ability to hover. However, tweaking a state-feedback controller to fly stably takes either intensive human supervision, or extensive modeling and identification, hence has never been trivial. In this paper, we give a successful flight controller design that can learn from a single demonstration performed by human and hover indoor aerial robots autonomously on maiden flight.
  • A Bio-inspired Active Tail Control Actuator for Nano Air Vehicles Authors: Penskiy, Ivan; Samuel, Paul; Humbert, James Sean; Bergbreiter, Sarah
    The goal of this research is to develop a lightweight, high bandwidth control actuator that can be integrated on a flapping wing nano air vehicle (NAV). Traditional control actuators for air vehicles including DC servomotors and shape memory alloy are either too heavy or too slow to control a fast moving NAV. This paper develops a new bio-inspired active tail mechanism to stabilize an inverted pendulum with the same mass and inertia as the NAV. An analysis of the dynamic model shows a critical angle at which the control actuator can no longer stabilize the pendulum varies significantly with link lengths and mass ratios. Based on this dynamic model, an LQR controller is developed and implemented as a state space controller on a microcontroller based test setup. Using a gyroscope to measure the pendulum’s angular velocity and estimate the angle, the active tail mechanism was able to stabilize the pendulum for over five minutes before falling due to drift in the gyroscope sensor.
  • Indoor Navigation with a Swarm of Flying Robots Authors: Stirling, Timothy; Roberts, James F.; Zufferey, Jean-Christophe; Floreano, Dario
    Swarms of flying robots are promising in many applications due to rapid terrain coverage. However, there are numerous challenges in realising autonomous operation in unknown indoor environments. A new autonomous flight methodology is presented using relative positioning sensors in reference to nearby static robots. The entirely decentralised approach relies solely on local sensing without requiring absolute positioning, environment maps, powerful computation or long-range communication. The swarm deploys as a robotic network facilitating navigation and goal directed flight. Initial validation tests with quadrotors demonstrated autonomous flight within a confined indoor environment, indicating that they could traverse a large network of static robots across expansive environments.