IROS 2015 Papers

This paper proposes a methodology for visual tracking of a dynamic generalized subject within an unknown map, by relying on its perception as a separate entity which can be distinguished spatially and visually from its environment. To this purpose, a 3D-representation of the visible scenery is examined, and the subject is spatially identified by its externally viewed hull via a mesh-connection algorithm aided by visual cues, and visually identified by distinct feature tracking based on an incrementally built list of key-aspects. These two processes operate in closed-loop, and employing a set of assumptions regarding the subject's structural/temporal invariance the tracking health state can be determined. This work additionally presents the framework for the deployment of this scheme for autonomous aerial robotic subject tracking, employing the dynamic subject/environment distinction to obtain knowledge of the environment structure, and collision-free trajectory generation algorithms to achieve mobile tracking.

This paper presents an aerial manipulator consisting of a multirotor equipped with a robotic multi-link arm attached to the top of the multirotor body. This setup has strong potentialities for inspection of structures, since the arm is able to safely touching the structure surface with a sensor while flying, taking measurements from the underside for example in bridges. The paper presents the dynamic model of the system and the derivation of a nonlinear controller, which is tested in simulation. First flight experiments with prototype of the system are also presented.

We describe an aerial robot equipped with an upward directed hand on top of a quadrotor airframe for realizing an aerial manipulation task at high altitude, considering a work space above the robot. The aerial robot in this study consists of a small quadrotor platform, a robotic hand module including a gripper and slider mechanism, and an embedded vision system. Positioning of the hand is autonomously performed through a vision-based control system. In this system, position and orientation of a bar-like object is measured and detected through a monocular camera image and FPGA-based on-board processing. Through experiments, the grasping of a bar located above the robot, keeping the grasp with shut off propellers and succeeding take off by releasing a bar was achieved.

In this paper, we consider the problem of guarding a valuable naval asset from a highly maneuverable threat via the use of autonomous unmanned surface vehicles (USVs) as dynamic obstacles. The objective of the defending agent is to maximize the amount of time it takes an intruder boat to enter the restricted area. Here we introduce a set of active blocking strategies which allow the defender to influence the intruder's actions through exploitation of its obstacle avoidance. By applying a mirror transformation to the intruder's current state, a pursuit policy can be formed that always places the defender between the intruder and the asset. To apply these strategies to delayed or imperfect information environments, we present a model-predictive method of calculating defender motion goals and estimating future intruder motions. This predictive estimator is capable of generating a meta-model for the intruder's behavior and returning a probability distribution of intruder control actions based on a reduced set of observable spatial features.

An assistive rehabilitation strategy for a lower-limb wearable robot is proposed and evaluated. The control strategy monitors the human-orthosis interaction torques and modifies the orthosis operation mode depending on its evolution with respect to a normal gait pattern. The control algorithm relies on the adaptation of the joints stiffness in function of these interaction torques and to the deviation from the desired trajectory. A walking pattern, an average of recorded gaits, is used as reference input. The human-orthosis interaction torques are used to define the time instant when robot assistance is needed and its degree. The objective of this work is to demonstrate the feasibility of ensuring a dynamic stability by means of an efficient real-time stiffness adaptation for multiple joints and simultaneously maintaining their synchronization. The algorithm has been tested with five healthy subjects showing its efficient behavior in maintaining the equilibrium while walking in presence of external forces. The work is performed as a preliminary study to assist patients suffering from Spinal cord injury and Stroke.

This paper presents a trifocal tensor based approach for the visual trajectory tracking task of a nonholonomic mobile robot, which is equipped with a roughly installed monocular camera. A set of pre-recorded images are used to express the desired trajectory, and the robot is regulated to track the desired trajectory using visual feedback. Trifocal tensor is exploited to obtain the orientation and scaled position information, which is used in the control system. Conventional methods require that there exist enough corresponding feature points in the start, current and final images, while this requirement can be easily violated in large workspace, especially for perspective cameras with limited field of view. In this paper, key frame strategy is proposed to loosen this requirement, extending the workspace of the system. Considering the unknown depth and extrinsic parameters, an adaptive controller is developed based on Lyapunov methods. Simulation results are provided to show the effectiveness of the proposed approach.

In this paper, we propose an adaptive optimal control for a robot to collaborate with a human. Game theory and policy iteration are employed to analyze the interactive behaviors of the human and the robot in physical interactions. The human's control objective is estimated and it is used to adapt the robot's own objective, such that human-robot coordination can be achieved. An optimal control is developed to guarantee that the robot's control objective is realized. The validity of the proposed method is verified through rigorous analysis and experiment studies.

The Bio-inspired Behavior-Based Bipedal Locomotion Control (B4LC) system consists of control units encapsulating feed-forward and feedback mechanisms, namely motor patterns and reflexes. To optimize the performance of motor patterns and reflexes in terms of stable locomotion on both even and uneven terrains, we present a learning scheme embedded in the B4LC system. By combining the Particle Swarm Optimization (PSO) method and the Expectation-maximization based Reinforcement Learning (EM-RL) method, a learning unit is comprised of an optimization module and a learning module embedded in the hierarchical control structure. The optimization module optimizes the motor patterns at hip and ankle joints with respect to energy consumption, stability and velocity control. The learning module generates compensating torques against disturbances at the ankle joints by combining the basis function derived from state information and the policy parameters. The optimization and learning procedures are conducted on a simulated robot with 21 DoFs. The simulation results show that the robot with optimized motor patterns and learned reflexes performs a more robust and stable locomotion on even and uneven terrains.

This paper deals with motion control of under-actuated aerial robotic manipulators composed of multi-rotor Unmanned Aerial Vehicles and multi-link serial robotic arms. Kinematics and dynamics analysis of the multi-body mechanical system are performed. Using the method of virtual decomposition, adaptive motion control laws are proposed based on rigid-body inverse dynamics with feedback compensation. Underactuation imposes second-order nonholonomic constraints on the system dynamics, which are taken into account in the control algorithm. System stability and convergence of the tracking errors are proven using a Lyapunov analysis. A simulation case study demonstrates the effectiveness of the proposed model-based controller compared to a conventional controller.

A pixel-by-pixel scanning that is usually performed by a single point-like sensor or probe is being widely used in the applications such as scanning probe microscopy techniques. Typically, their scanning time is several seconds to minutes long due to a raster scanning that needs to be conducted for capturing every single point on the surface of the sample area. To improve the scanning efficiency, recent research has been focused on investigating effective scanning patterns and methods. This work presents an adaptive local scanning method for efficiently sampling indiscrete objects like string-like one-piece connected objects under the microscopy. An initial scanning pattern is firstly investigated. Once the initial scanning reaches the object, an adaptive sinusoidal scanning method that can on-line adjust its scanning frequency and amplitude by predicting both the curvatures and the shape of the object is employed. The method also addresses scanning intersections and bifurcations associated with objects. Based on extensive implementations, it is validated that our method has high performance as it has high scanning efficiency and the scanned results match objects with high precision and high accuracy.

Most current approaches for simulating robots in 3D with rigid body dynamics and contact operate by taking fixed, first order integration steps. In this paper, we investigate the viability of various integration approaches (semi implicit and fully explicit first-order, fourth order Runge Kutta, and variable step first order) for maximizing computational efficiency (accuracy and stability vs. running time) on typical robotics manipulation and locomotion applications. After arguing that first-order accuracy to the dynamics models is sufficient for most manipulator and locomotion robotics applications, we focus our investigation on methods for efficient, stable dynamic simulations. We describe a novel algorithm that attempts to provide smooth convergence to the true dynamics solution, which allows us to estimate the error in kinetic energy around an integration step size (as a proxy for the simulation stability). We use this algorithm to evaluate multiple hypotheses using simulation-based experiments with two virtual robot models toward developing methods for faster dynamic simulations.

In this paper, we address the positioning problem of remote centre of motion (RCM) mechanisms with uncalibrated image feedback from a monocular camera. Nowadays, RCM mechanisms are widely used in minimally invasive robotic surgery due to their ability to distally rotate a tool around a fixed entry port; note that in most surgical applications, the tools are typically controlled by manual/teleoperated motion commands given by a human user. In this paper, we depart from the traditional manual control scheme and derive sensor-based methods to automatically position the manipulated tool using real-time image feedback. To this end, we first characterise the mechanism's 3-DOF configuration with the angle of the image projected tool and scalar distances between feature points. To cope with uncertainty in the camera's calibration parameters, we propose two gradient descent estimators that adaptively compute the unknown Jacobian matrix; the stability of these algorithms is proved with Lyapunov theory. Finally, we derive a kinematic image-based controller and evaluate its performance with several positioning experiments.

There are few mathematical indices to quantize the adaptability, although it is mentioned repeatedly as a highlight of underactuated mechanisms in literatures. This paper discusses the grasp adaptability of underactuated mechanisms. Different from compliance, namely the reciprocal of stiffness, the adaptability is presented as the ability of adaptive motion. Two measures of adaptability are proposed from two aspects respectively: the impact on the grasped object and the enforcement cost of the underactuated mechanism. Based on the measures, the trend of the adaptive grasping process is predicted in enveloping grasp with a simple gripper, and a corresponding experimental prototype is set up to verify the calculated results. As an application, we apply the two measures to analyze and compare the adaptability of three typical underactuated mechanisms.

In human-robot interaction (HRI) and especially in close or physical interaction, it is essential to ensure the human's safety. This is achieved by introducing virtual constraints defining a region, in which the robot is allowed to move safely. These safety regions may change over time during human-robot interaction, which may be either due to human motion or changed environmental conditions. In consequence it is important for the applied control scheme to handle dynamic boundaries. This work proposes an invariance-based control approach, which enforces adherence to boundaries with dynamic parameters. We extend the invariance control approach, which provides a computationally efficient and systematic method for defining constraints on system states and outputs, such that it handles the constraint dynamics. Stability and invariance properties are analyzed and validated in an experimental evaluation on a 7-DoF anthropomorphic manipulator.

This paper deals with the active fault-tolerant control for quadrotors which are subjected to a total rotor failure. Previous studies assume that the fault has been detected and isolated and then design a fault-tolerant controller. The present paper proposes a complete active fault-tolerant control system which copes with not only fault detection and isolation but also fault-tolerant control. A novel and efficient fault detection and isolation approach is proposed for the total rotor failure case. An incremental nonlinear dynamic inversion approach is introduced to design the fault-tolerant controller for the quadrotor in the presence of the fault. The complete active fault-tolerant control system enables the quadrotor to achieve any position even after the complete loss of one rotor.

We propose a novel control framework to demonstrate a unique foot tilting maneuver based on ankle torque control for humanoid balance recovery. The framework consists of the variable impedance regulation at the center of mass of the robot based on the ankle torque control, the virtual stoppers to prevent over tilting of the feet, and the body attitude control. The scope of our paper focuses on the sagittal scenario as the first proof of concept on the balance recovery by means of active foot tilting without losing stability. Our study demonstrates the success of the control implementation for the humanoid push recovery and the feasibility of having actively controlled foot tilting. The experimental data are presented and analyzed.

Many mobile manipulation tasks require the robot to be accurately localized with respect to the object where the manipulation has to be executed. These tasks include autonomous docking and positioning as well as pick and place or logistics tasks. State-of-the-art approaches to the problem commonly assume that the environment is static and localize the robot with respect to predetermined locations. In this paper, we present an approach that relaxes the static assumption and enables a robot to accurately localize with respect to a reference object that could be moved in the environment. The core of the paper is an extension of the generalized ICP method to handle multiple rigid bodies that move independently to each others. Experiments in both simulated and real world scenarios show that our approach is able to localize the robot with respect to a moved object with an accuracy of less than one centimeter.

Accurate localization in indoor environments is widely regarded as a key opener for various location-based services. Despite tremendous advancements in the development of innovative sensor concepts, the most effective and accurate solutions to this problem make use of a map computed from sensory data. In this paper, we present an efficient approach to localize an RGB-D smartphone or tablet that only makes use of a two-dimensional outline of the environment as a map as it is typically available from architectural drawings. Our technique employs a particle filter to estimate the 6DoF pose. We propose a sensor model that robustly estimates the likelihood of measurements and accommodates the disagreements between floor plans and real world data. In extensive experiments, we demonstrate that our approach is able to globally localize a user in a given 2D floor plan using a Google Tango device and to accurately track the user in such an environment.

Human-demonstrated motion transferred to a robotic platform often needs to be adapted to the current state of the environment or to modified task requirements. Adaptation, i. e. learning of a modified behavior, needs to be fast to enable quick utilization of the robot either in industry or in future household-assistant tasks. In this paper we show how to accelerate trajectory adaptation based on learning of coupling terms in the framework of dynamic movement primitives (DMPs). Our method applies ideas from feedback error learning to iterative learning control (ILC). By taking into account the actual physical constraints of the synchronous motion - through synchronization of both positions (or forces) and velocities - it is not only a more faithful representation of actual real-world processes, but it also accelerates the speed of convergence. To show the applicability of the approach in the framework of DMPs, we tested it on a formulation which encodes an initial discrete motion, followed by a periodic behavior, all in a single system. Modifications of the original discrete-periodic formulation now also allow for the use of DMP temporal scaling property. In the paper we also show how the DMP coupling can be implemented in joint space, whereas the measured forces and previous approaches always remained in the task space. We applied our approach to an example dual-arm synchronization task on Sarcos humanoid robot CB-i.