ICRA 2015 Papers

This paper describes Smartpig, an algorithm for the iterative mosaicking of images of a planar surface using a unique parameterization which decomposes inter-image projective warps into camera intrinsics, fronto-parallel projections, and inter-image similarities. The constraints resulting from the inter-image alignments within an image set are stored in an undirected graph structure allowing efficient optimization of image projections on the plane. Camera pose is also directly recoverable from the graph, making Smartpig a feasible solution to the problem of simultaneous location and mapping (SLAM). Smartpig is demonstrated on a set of 144 high resolution aerial images and evaluated with a number of metrics against ground control.

We introduce a new declarative language called SELECTSCRIPT. As its name suggests, it is a scripting language inspired primarily by SQL and its relational algebra. It is intended to be used for complex queries on different kinds of world models. Scripts can be dynamically generated and executed, or embedded into the code of foreign programming languages. A first interpreter was therefore developed for Python. Adapting the ideas of language-oriented programming, which enables developers to create their own domain-specific language, we developed a language stub that can be easily adapted and extended to comply with any (discrete) robotic world model or robotic simulator. We will further show how simple SELECT-statements can be used to extract any kind of valuable information in various return formats, thereby going beyond traditional SQL capabilities.

A series elastic actuator (SEA) consists of a system with an intrinsically compliant mechanism, and introduces the possibility of external force/torque sensing and control. However, this mechanism is also known to reduce torque servo-control bandwidth and to separate link-side dynamics from actuator-side dynamics. The conventional solution to overcoming reduced actuation bandwidth and separated link motion problems is to increase the control gain, which is limited by stability problems and actuator saturation. Here, we demonstrate that treating the actuator side as a near-ideal position source gives the compliance-independent actuation bandwidth of an SEA, so that separated link-side motion can be simply but effectively compensated. Based on these observations, we developed an SEA force/torque servo controller that consists of actuator-side robust motion control and link-side motion feedback. We performed comparison experiments with conventional cascaded two-loop SEA torque servo control using a rotational flexible-joint test-bed, and the results demonstrated that the proposed control strategy yields a wider bandwidth and more robust link motion.

The X2-VelociRoACH is a 54 gram experimental legged robot which was developed to test hypotheses about running with unnaturally high stride frequencies. It is capable of running at stride frequencies up to 45 Hz, and velocities up to 4.9 m/s, making it the fastest legged robot relative to size. The top speed of the robot was limited by structural failure. We present new methods and materials to make more robust folded robotic structures. High-frequency running experiments with the robot shows that the power required to cycle its running appendages increase cubically with the stride rate. Our findings show that although it is possible to further increase the maximum velocity of a legged robot with the simple strategy of increasing stride frequency, considerations must be made for the energetic demands of high stride rates.

In this paper, a role adaptation method is developed for human-robot collaboration based on game theory. This role adaptation is engaged whenever the interaction force changes, causing the proportion of control sharing between human and robot to vary. In one boundary condition, the robot takes full control of the system when there is no human intervention. In the other boundary condition, it becomes a follower when the human exhibits strong intention to lead the task. Experimental results show that the proposed method yields better overall performance than fixed-role interactions.

We introduce the modelling and control of a rolling microrobot. The microrobot is capable of manipulating micro-objects through the use of a magnetic visual control system. This system consists of a rod-shaped microrobot, a magnetic actuation system and a visual control system. Motion of the rolling microrobot on a supporting surface is induced by a rotating magnetic field. As the robot is submerged in a liquid this motion creates a rising flow in front, a sinking flow behind, and a vortex above the robot, thus enabling non-contact transportation of micro-objects. Besides this fluid-vortex approach, the microrobot is also able to manipulate micro-objects via a pushing strategy. We present the design and modelling of the 50×60×300 μm micro-agent, the visual control system, and an experimental analysis of the micromanipulation and control methods.

Throwing an object by a powered robot system is of great importance in unmanned environments. In this paper, we consider the problem of throwing a point-mass object to minimize uncertainty in the object's landing position, given uncertainty in (1) the robot's initial configuration and (2) friction at the joints. Our analysis assumes that the robot's throw is executed using open-loop torque commands, and it relies on the linearized sensitivities of (a) landing location with respect to release state, (b) release state with respect to initial robot configuration and (c) joint friction. Moreover, the effectiveness of the proposed method is evaluated by Monte-Carlo simulations.

This paper studies cages of convex polygonal objects using three point fingers. The fingers are said to cage the object when it is impossible to move the object arbitrarily far from its initial placement without penetrating the fingers. We consider a three-parameter model of the relative position of the fingers, which gives complete generality for three point fingers in the plane. We consider robustness of caging grasps - what variations in the relative position of the fingers is allowed without breaking the cage. Using a simple decomposition of free space around the polygon, we present an algorithm which gives all caging placements of the fingers and a characterization of the robustness of these cages, albeit at the cost of significant computational complexity.

In this work, we present a model-based estimation scheme for multi-rotor Micro Aerial Vehicles (MAVs). Although modeling approaches for MAVs have been presented in the past, these models have rarely been used for real-time state estimation onboard MAVs. Building on this work, we identify the most dominant effects and propose an easy-to-use calibration scheme for estimation of the model parameters. Given the calibration estimates for these parameters, we derive a state estimator where the state prediction of the indirect Extended Kalman Filter (EKF) is driven by a MAV model. Solely using measurements from the Inertial Measurement Unit (IMU) and a barometric pressure sensor - both available on almost every MAV - our model-based formulation keeps the estimated velocity of the MAV bounded in all directions, as opposed to state of the art IMU-model driven state estimators onboard MAVs. This is crucial for keeping MAVs airborne safely, for instance in the case of failures or re-initialization of vision based localization systems.

This paper investigates robust asymptotic stabilization of rigid body attitude dynamics evolving on the tangent bundle of SO(3) using geometric stochastic feedback control, where the system is subject to a stochastic input torque. To start with, the attitude dynamics is interpreted in the Ito sense. However, due to evolution of the kinematic differential equation of the system on SO(3), analyzing the stochastic system on TSO(3) is non-trivial. To address this challenging problem of robust asymptotic stabilization of attitude dynamics, the back-stepping method along with a suitable Morse-Lyapunov (M-L) function candidate with constant control gain parameters are used to obtain a nonlinear stochastic feedback control law. The control gain matrix and the M-L function control gain can be obtained by solving a feasible LMI, which can guarantee the robust asymptotic stability of the rigid body on TSO(3). Numerical simulations are performed to demonstrate and validate the effectiveness of the proposed controller in the state space of rigid body attitude motion in TSO(3).

The analysis of the conceptual spring mass model for running reveals swing-leg placement policies that generate very robust locomotion in unobserved terrain with large changes in ground height. However, while this theoretical result suggests a potential for large improvements on the robustness of running machines, it has so far not been demonstrated on a physical robot. Here we address this implementation and verification step for a human-sized bipedal robot platform confined to a boom. We detail challenges and solutions for the implementation of the control approach and show that it leads to very robust running (ground changes ±20% of leg length) over unobserved ground in a high fidelity simulation of the robot platform. We also present initial tests on the actual robot hardware, which indicate the feasibility of the approach. If it can be generalized to 3D running, it could trigger running machines with largely improved robustness.

In this paper, we consider the formation control problem for multi-agent systems with unknown nonlinearities and disturbances, under an undirected communication protocol. Exploiting the recently developed prescribed performance control methodology, a robust distributed control scheme of minimal complexity is proposed that achieves and maintains arbitrarily fast and accurately the desired formation. No information regarding the agents' dynamic model is employed in the design procedure. Moreover, contrary to the related works on multi-agent systems, the transient and steady state response is fully decoupled by the underlying graph topology, the control gains selection and the agents' model uncertainties. In particular, the achieved performance is a priori and explicitly imposed by certain designer-specified performance functions. Finally, the theoretical findings are clarified and verified by an extensive simulation study.

Inference of three-dimensional motion from the fusion of inertial and visual sensory data has to contend with the preponderance of outliers in the latter. Robust filtering deals with the joint inference and classification task of selecting which data fits the model, and estimating its state. We derive the optimal discriminant and propose several approximations, some used in the literature, others new. We compare them analytically, by pointing to the assumptions underlying their approximations, and empirically. We show that the best performing method improves the performance of state-of-the-art visual-inertial sensor fusion systems, while retaining the same computational complexity.

Estimating the ground plane is often one of the first steps in geometric reasoning processes as it offers easily accessible context knowledge. Especially unconstrained platforms that capture video from egocentric viewpoints can benefit from such knowledge in various ways. A key requirement here is keeping orientation, which can be greatly achieved by keeping track of the ground. We present an approach to keep track of the ground plane in cluttered inner-urban environments using stereo vision in real-time. We fuse a planar model fit in low-resolution disparity data with the direction of the vertical vanishing point. Our experiments show how this effectively decreases the error of plane attitude estimation compared to classic least-squares fitting and allows to track the plane with camera configurations in which the ground is not visible. We evaluate the approach using ground-truth from an inertial measurement unit and demonstrate long-term stability on a dataset of challenging inner city scenes.

The goal of integrating drones into the civil airspace requires a technical system which robustly detects, tracks and finally avoids aerial objects. Electro-optical cameras have proven to be an adequate sensor to detect traffic, especially for smaller aircraft, gliders or paragliders. However the very challenging environmental conditions and image artifacts such as lens flares often result in a high number of false detections. Depending on the solar radiation lens flares are very common in aerial images and hard to distinguish from aerial objects on a collision course due to their similar size, shape, brightness and trajectories. In this paper we present an efficient method to detect lens flares within aerial images based on the position of the sun with respect to the observer. Using the date, time, position and attitude of the observer we predict the lens flare direction within the image. Once the direction is known the position, size and shape of the lens flares are extracted. Experiments show that our approach is able to compensate for errors in the parameters influencing the calculation of the lens flare direction. We further integrate the lens flare detection into an aerial object tracking framework. A detailed evaluation of the framework with and without lens flare filter shows that false tracks due to lens flares are successfully suppressed without degrading the overall tracking system performance.

In this paper, we deal with the stability and tracking control problem of quadrotor unmanned aerial vehicle (UAV) in the presence of the modeling error and disturbance uncertainty. The flight tracking control system combines classical proportional-derivative (PD) like term with robust and adaptive control term. Lyapunov method is used to design and show the asymptotic behavior of the linear and angular states of the vehicle. In contrast with other existing adaptive backstepping design, the proposed design is very simple and easy to implement as it does not require multiple design steps without using augmented signals and known bound of the uncertainty. Various experimental results on quadrotor UAV system are presented to demonstrate the effectiveness of the proposed design for real-time application.

Visual tracking of unknown objects is an essential task in robotic perception, of importance to a wide range of applications. In the general scenario, the robot has no full 3D model of the object beforehand, just the partial view of the object visible in the first video frame. A tracker with this information only will inevitably lose track of the object after occlusions or large out-of-plane rotations. The way to overcome this is to incrementally learn the appearances of new views of the object. However, this bootstrapping approach is sensitive to drifting due to occasional inclusion of the background into the model.

In this paper, a robotic wheelchair using only a pan-tilt-zoom camera is proposed for developing a robotic system which enables a user to navigate in complex environment at low cost. For realizing the proposed system, a high accuracy visual marker, LentiBar which is capable of achieving a high level of precision in positioning and orienting a robotic wheelchair is utilized and its application to door crossing for a robotic wheelchair is presented. Since the visual marker is attached on the top of the target door through which the wheelchair is going to pass, the typical problems for achieving geometric features of the environment can be easily addressed. Door crossing navigation methods are also explained, which enable automatic and hybrid navigation of the robotic wheelchair. Finally, experimental results show the feasibility of the proposed navigation method and the robotic wheelchair system.

Using autonomous mobile robots to dynamically track oil plume propagation in ocean environments is challenging. Based on a model of advection-diffusion equation that describes point-source pollution propagation in marine environments, we have previously proposed a model-based estimator-controller design for autonomous robots to dynamically track plume front propagation. In this paper we study the robustness of the controller in a robot simulator, and evaluate it's performance in a realistic environmental model setup. A probabilistic Lagrangian environmental model is used that can capture both the time-averaged, idealized structure and the instantaneous, realistic structure of a dynamic plume. The controller is implemented on the Field Robotics Lab Vehicle Software, which uses Lightweight Communication and Marshalling library to facilitate process communication and for easy transit to field testing. Simulation results are shown with discussions and lessons learned to guide future field experiments.