TechTalks from event: Technical session talks from ICRA 2012

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Multi-Robot Systems II

  • Distributed Value Functions for Multi-Robot Exploration Authors: Matignon, Laetitia; Jeanpierre, Laurent; Mouaddib, Abdel-Illah
    This paper addresses the problem of exploring an unknown area with a team of autonomous robots using decentralized decision making techniques. The localization aspect is not considered and it is assumed the robots share their positions and have access to a map updated with all explored areas. A key problem is then the coordination of decentralized decision processes: each individual robot must choose appropriate exploration goals so that the team simultaneously explores different locations of the environment. We formalize this problem as a Decentralized Markov Decision Process (Dec-MDP) solved as a set of individual MDPs, where interactions between MDPs are considered in a distributed value function. Thus each robot computes locally a strategy that minimizes the interactions between the robots and maximizes the space coverage of the team. Our technique has been implemented and evaluated in real-world and simulated experiments.
  • Steiner Traveler: Relay Deployment for Remote Sensing in Heterogeneous Multi-Robot Exploration Authors: Pei, Yuanteng; Mutka, Matt
    In the multi-robot exploration task of an unknown environment, human operators often need to control the robots remotely and obtain the sensed information by real-time bandwidth-consuming multimedia streams. The task has military and civilian applications, such as reconnaissance, search and rescue missions in earthquake, radioactive, and other dangerous or hostile regions. Due to the nature of such applications, infrastructure networks or pre-deployed relays are often not available to support the stream transmission. To address this issue, we present a novel exploration scheme called Bandwidth aware Exploration with a Steiner Traveler (BEST). BEST has a heterogeneous robot team with a fixed number of frontier nodes(FNs) to sense the area iteratively. In addition, a relay-deployment node (RDN) tracks the FNs movement and places relays when necessary to support the video/audio streams aggregation to the base station. Therefore, the main problem is to find a minimum path for the relay-deployment robot to travel and the positions to deploy necessary relays to support the stream aggregation in each movement iteration. This problem inherits characteristics of both the Steiner minimum tree and traveling salesman problems. We model the novel problem as the minimum velocity Flow constrained Steiner Traveler problem (FST). Extensive simulations show BEST improves exploration efficiency by 62% on average compared to the state-of-the-art homogeneous robot exploration strategies.
  • Minimal Persistence Control on Dynamic Directed Graphs for Multi-Robot Formation Authors: Wang, Hua; Guo, Yi
    Given a multi-robot system, in order to preserve its geometric shape in a formation, the minimal persistence control addresses questions: (1) what pairwise communication connections have to be prescribed to minimize communication channels, and (2) which orientations of communication links are to be placed between robots. In this paper, we propose a minimal persistence control problem on multi-robot systems with underlying graphs, being directed and dynamically switching. We develop distributed algorithms based on the rank of the rigidity matrix and the pebble game method. The feasibility of the proposed methods is validated by simulations on the robotic simulator Webots and experiments on e-puck robot platform.
  • Distributed Formation Control of Unicycle Robots Authors: Sadowska, Anna; Kostic, Dragan; van de Wouw, Nathan; Huijberts, Henri; Nijmeijer, Hendrik
    In this paper, we consider the problem of distributed formation control for a group of unicycle robots. We propose a control algorithm that solves the formation control problem in that it ensures that robots create a desired time– varying formation shape while the formation as a whole follows a prescribed trajectory. Moreover, we show that it is also possible to obtain coordination of robots in the formation, regardless of the trajectory tracking of the formation. We illustrate the behavior of a group of robots controlled by the formation control algorithm proposed in this paper in a simulation study.
  • Multi-Level Formation Roadmaps for Collision-Free Dynamic Shape Changes with Non-holonomic Teams Authors: Krontiris, Athanasios; Louis, Sushil; Bekris, Kostas E.
    Teams of robots can utilize formations to accomplish a task, such as maximizing the observability of an environment while maintaining connectivity. In a cluttered space, however, it might be necessary to automatically change formation to avoid obstacles. This work proposes a path planning approach for non-holonomic robots, where a team dynamically switches formations to reach a goal without collisions. The method introduces a multi-level graph, which can be constructed offline. Each level corresponds to a different formation and edges between levels allow for formation transitions. All edges satisfy curvature bounds and clearance requirements from obstacles. During the online phase, the method returns a path for a virtual leader, as well as the points along the path where the team should switch formations. Individual agents can compute their controls using kinematic formation controllers that operate in curvilinear coordinates. The approach guarantees that it is feasible for the agents to follow the trajectory returned. Simulations show that the online cost of the approach is small. The method returns solutions that maximize the maintenance of a desired formation while allowing the team to rearrange its configuration in the presence of obstacles.
  • An Unscented Model Predictive Control Approach to the Formation Control of Nonholonomic Mobile Robots Authors: Farrokhsiar, Morteza; Najjaran, Homayoun
    Formation control of nonholonomic robots in dynamic unstructured environments is a challenging task yet to be met. This paper presents the unscented model predictive control (UMPC) approach to tackle the formation control of multiple nonholonomic robots in unstructured environments. In unscented predictive control, the uncertainty propagation in the nonholonomic nonlinear motion model is approximated using the unscented transform. The collision avoidance constraints have been introduced as the chance constraints to model predictive control. The UMPC approach enables us to find a closed form of the collision avoidance probabilistic constraints. The desired pose of each robot in the formation is introduced through the local objective function of UMPC of each robot. The simulation results indicate the effective and robust performance of UMPC in unstructured environment in the presence of action disturbance and communication signal noise.

Grasping: Learning and Estimation

  • End-To-End Dexterous Manipulation with Deliberate Interactive Estimation Authors: Hudson, Nicolas; Howard, Tom; Ma, Jeremy; Jain, Abhinandan; Bajracharya, Max; Myint, Steven; Matthies, Larry; Backes, Paul; Hebert, Paul; Fuchs, Thomas; Burdick, Joel
    This paper presents a model based approach to autonomous dexterous manipulation, developed as part of the DARPA Autonomous Robotic Manipulation (ARM) program. The developed autonomy system uses robot, object, and environment models to identify and localize objects, and well as plan and execute required manipulation tasks. Deliberate interaction with objects and the environment increases system knowledge about the combined robot and environmental state, enabling high precision tasks such as key insertion to be performed in a consistent framework. This approach has been demonstrated across a wide range of manipulation tasks, and is the leading manipulation approach in independent DARPA testing.
  • Template-Based Learning of Grasp Selection Authors: Herzog, Alexander; Pastor, Peter; Kalakrishnan, Mrinal; Righetti, Ludovic; Asfour, Tamim; Schaal, Stefan
    The ability to grasp unknown objects is an important skill for personal robots, which has been addressed by many present and past research projects. A crucial aspect of grasping is choosing an appropriate grasp configuration, i.e. the 6d pose of the hand relative to the object and its finger configuration. Finding feasible grasp configurations for novel objects, however, is challenging because of the huge variety in shape and size of these objects and the specific kinematics of the robotic hand in use. In this paper, we introduce a new grasp selection algorithm able to find object grasp poses based on previously demonstrated grasps. Assuming that objects with similar shapes can be grasped in a similar way, we associate to each demonstrated grasp a grasp template. The template is a local shape descriptor for a possible grasp pose and is constructed using 3d information from depth sensors. For each new object to grasp, the algorithm then finds the best grasp candidate in the library of templates. The grasp selection is also able to improve over time using the information of previous grasp attempts to adapt the ranking of the templates. We tested the algorithm on two different platforms, the Willow Garage PR2 and the Barrett WAM arm which have very different hands. Our results show that the algorithm is able to find good grasp configurations for a large set of objects from a relatively small set of demonstrations, and does indeed improve its performance over time.
  • Learning Hardware Agnostic Grasps for a Universal Jamming Gripper Authors: Jiang, Yun; Amend, John; Lipson, Hod; Saxena, Ashutosh
    Grasping has been studied from various perspectives including planning, control, and learning. In this paper, we take a learning approach to predict successful grasps for a universal jamming gripper. A jamming gripper is comprised of a flexible membrane filled with granular material, and it can quickly harden or soften to grip objects of varying shape by modulating the air pressure within the membrane. Although this gripper is easy to control, it is difficult to develop a physical model of its gripping mechanism because it undergoes significant deformation during use. Thus, many grasping approaches based on physical models (such as based on form- and force-closure) would be challenging to apply to a jamming gripper. Here we instead use a supervised learning algorithm and design both visual and shape features for capturing the property of good grasps. We show that given a RGB image and a point cloud of the target object, our algorithm can predict successful grasps for the jamming gripper without requiring a physical model. It can therefore be applied to both a parallel plate gripper and a jamming gripper without modification. We demonstrate that our learning algorithm enables both grippers to pick up a wide variety of objects, and through robotic experiments we are able to define the type of objects each gripper is best suited for handling.
  • Learning Grasp Stability Authors: Dang, Hao; Allen, Peter
    We deal with the problem of blind grasping where we use tactile feedback to predict the stability of a robotic grasp given no visual or geometric information about the object being grasped. We first simulated tactile feedback using a soft finger contact model in GraspIt! and computed tactile contacts of thousands of grasps with a robotic hand using the Columbia Grasp Database. We used the K-means clustering method to learn a contact dictionary from the tactile contacts, which is a codebook that models the contact space. The feature vector for a grasp is a histogram computed based on the distribution of its contacts over the contact space defined by the dictionary. An SVM is then trained to predict the stability of a robotic grasp given this feature vector. Experiments indicate that this model which requires low-dimension feature input is useful in predicting the stability of a grasp.
  • Learning to Slide a Magnetic Card through a Card Reader Authors: Sukhoy, Vladimir; Georgiev, Veselin; Wegter, Todd; Sweidan, Ramy; Stoytchev, Alexander
    This paper describes a set of experiments in which an upper-torso humanoid robot learned to slide a card through a card reader. The small size and the flexibility of the card presented a number of manipulation challenges for the robot. First, because most of the card is occluded by the card reader and the robot's hand during the sliding process, visual feedback is useless for this task. Second, because the card bends easily, it is difficult to distinguish between bending and hitting an obstacle in order to correct the sliding trajectory. To solve these manipulation challenges this paper proposes a method for constraint detection that uses only proprioceptive data. The method uses dynamic joint torque thresholds that are calibrated using the robot's movements in free space. The experimental results show that using this method the robot can detect when the movement of the card is constrained and modify the sliding trajectory in real time, which makes solving this task possible.

Non-Holonomic Motion Planning

  • Model Predictive Navigation for Position and Orientation Control of Nonholonomic Vehicles Authors: Karydis, Konstantinos; Valbuena, Luis; Tanner, Herbert G.
    In this paper we consider a nonholonomic system in the form of a unicycle and steer it to the origin so that both position and orientation converge to zero while avoiding obstacles. We introduce an artificial reference field, propose a discontinuous control policy consisting of a receding horizon strategy and implement the resulting field-based controller in a way that theoretically guarantees for collision avoidance; convergence of both position and orientation can also be established. The analysis integrates an invariance principle for differential inclusions with model predictive control. In this approach there is no need for the terminal cost in receding horizon optimization to be a positive definite function.
  • Regularity Properties and Deformation of Wheeled Robots Trajectories Authors: Pham, Quang-Cuong; Nakamura, Yoshihiko
    Our contribution in this article is twofold. First, we identify the regularity properties of the trajectories of planar wheeled mobile robots. By regularity properties of a trajectory we mean whether this trajectory, or a function computed from it, belongs to a certain class <i>C<sup>n</sup></i> (the class of functions that are differentiable <i>n</i> times with a continuous <i>n</i><sup>th</sup> derivative). We show that, under some generic assumptions about the rotation and steering velocities of the wheels, any non-degenerate wheeled robot belongs to one of the two following classes. Class I comprises those robots whose admissible trajectories in the plane are <i>C</i><sup>1</sup> and piecewise <i>C</i><sup>2</sup>; and class II comprises those robots whose admissible trajectories are <i>C</i><sup>1</sup>, piecewise <i>C</i><sup>2</sup> and, in addition, curvature-continuous. Second, based on this characterization, we derive new feedback control and gap filling algorithms for wheeled mobile robots using the recently-developed affine trajectory deformation framework.
  • A Homicidal Differential Drive Robot Authors: Ruiz, Ubaldo; Murrieta-Cid, Rafael
    In this paper, we consider the problem of capturing an omnidirectional evader using a Differential Drive Robot in an obstacle free environment. At the beginning of the game the evader is at a distance L>l from the pursuer. The pursuer goal is to get closer from the evader than the capture distance l. The goal of the evader is to keep the pursuer at all time farther from it than this capture distance. In this paper, we found closed-form representations of the motion primitives and time-optimal strategies for each player. These strategies are in Nash Equilibrium, meaning that any unilateral deviation of each player from these strategies does not provide to such player benefit toward the goal of winning the game. We also present the condition defining the winner of the game and we construct a solution over the entire reduced space.
  • On the Dynamic Model and Motion Planning for a Class of Spherical Rolling Robots Authors: Svinin, Mikhail; Yamamoto, Motoji
    The paper deals with the dynamics and motion planning for a spherical rolling robot actuated by internal rotors that are placed on orthogonal axes. The driving principle for such a robot exploits non-holonomic constraints to propel the rolling carrier. The full mathematical model as well as its reduced version are derived, and the inverse dynamics is addressed. It is shown that if the rotors are mounted on three orthogonal axes, any feasible kinematic trajectory of the rolling robot is dynamically realizable. For the case of only two orthogonal axes of the actuation the condition of dynamic realizability of a feasible kinematic trajectory is established. The implication of this condition to motion planning in dynamic formulation is explored under a case study. It is shown there that in maneuvering the robot by tracing circles on the sphere surface the dynamically realizable trajectories are essentially different from those resulted from kinematic models.
  • Control of Nonprehensile Rolling Manipulation: Balancing a Disk on a Disk Authors: Ryu, Ji-Chul; Ruggiero, Fabio; Lynch, Kevin
    This paper presents stabilization control of a rolling manipulation system called the disk-on-disk. The system consists of two disks in which the upper disk (object) is free to roll on the lower disk (hand) under the influence of gravity. The goal is to stabilize the object at the unstable upright position directly above the hand. We use backstepping to derive a control law yielding global asymptotic stability. We present simulation as well as experimental results demonstrating the controller.
  • Estimating Probability of Collision for Safe Motion Planning under Gaussian Motion and Sensing Uncertainty Authors: Patil, Sachin; van den Berg, Jur; Alterovitz, Ron
    We present a fast, analytical method for estimating the probability of collision of a motion plan for a mobile robot operating under the assumptions of Gaussian motion and sensing uncertainty. Estimating the probability of collision is an integral step in many algorithms for motion planning under uncertainty and is crucial for characterizing the safety of motion plans. Our method is computationally fast, enabling its use in online motion planning, and provides conservative estimates to promote safety. To improve accuracy, we use a novel method to truncate estimated a priori state distributions to account for the fact that the probability of collision at each stage along a plan is conditioned on the previous stages being collision free. Our method can be directly applied within a variety of existing motion planners to improve their performance and the quality of computed plans. We apply our method to a car-like mobile robot with second order dynamics and to a steerable medical needle in 3D and demonstrate that our method for estimating the probability of collision is orders of magnitude faster than naive Monte Carlo sampling methods and reduces estimation error by more than 25% compared to prior methods.