IROS 2018 Papers

Encoding desired motions into dynamic movement primitives (DMPs) is a common way for generating compact task representations that are able to handle sensor-based goal adaptations. At the same time, a robot should not only express adaptive motion capabilities at planning level, but use also contact wrench feedback in the adaptation and learning process of the DMP. Despite first approaches exist in this direction, no fully integrated approach has been proposed so far. In this paper, we introduce a new class of admittance-coupled DMPs that addresses environmental changes by including contact wrench feedback dynamics into the DMP formalism. Moreover, a novel iterative learning approach is devised that is based on monitoring the overall system passivity analysis in terms of reference power tracking. Simulations and experimental results with the Kuka LWR robot maintaining a non-rigid contact with the environment (wiping a surface) are shown for supporting the validity of our approach.

Autonomous underwater vehicles (AUVs) pose significant communication challenges: vehicles are submerged for periods of time in which speed-of-light communication is impossible. This is a particular problem on low-cost AUV platforms, on which acoustic modems are not available to get vehicle state or provide re-deploy commands. We investigate one possible method of providing operators with a communication line to these vehicles by using noise underwater to both classify behavior of submerged vehicles and to command them. In this scheme, processing of data from hydrophone arrays provide operators with AUV mode estimates and AUVs with surface vehicle behavior updates. Simulation studies were used to characterize trajectories for simple transect versus loiter behaviors based on the bearing and time to intercept (TTI). A classifier based on K-nearest-neighbor with dynamic time warping as a distance metric was used to classify simulation data. The simulation-based classifier was then applied to classify bearing tracking data from passive tracking of a loitering AUV and bearing and TTI data from passive tracking of a transecting boat based on field array data. Experiment data was classified with 76 % accuracy using bearing-only data, 96% accuracy for TTI -only data and 99 % accuracy for combined classification. The techniques developed here could be used for AUV cuing by surface vessels and monitoring of AUV behavior.

This paper describes a procedure to design a passive nonlinear impedance control for port-Hamiltonian systems. By expressing the system with the port-Hamiltonian system, the proposed method can be applied to the nonholonomic system as well as fully actuated mechanical systems. The feedback controller for nonlinear impedance control is acquired by utilizing the results of generalized canonical transformation for port-Hamiltonian system. In addition, we investigate the passivity of the closed loop system and discuss the characteristics of the controlled system. A numerical simulation of two-wheeled vehicle shows the effectiveness of the proposed control method.

This paper presents a passive compliance control for aerial manipulators to achieve stable environmental interactions. The main challenge is the absence of actuation along body-planar directions of the aerial vehicle which might be required during the interaction to preserve passivity. The controller proposed in this paper guarantees passivity of the manipulator through a proper choice of end-effector coordinates, and that of vehicle fuselage is guaranteed by exploiting time domain passivity technique. Simulation studies validate the proposed approach.

Many robotics applications involve navigating to positions specified in terms of their semantic significance. A robot operating in a hotel may need to deliver room service to a named room. In a hospital, it may need to deliver medication to a patient's room. The Building-Wide Intelligence Project at UT Austin has been developing a fleet of autonomous mobile robots, called BWIBots, which perform tasks in the computer science department. Tasks include guiding a person, delivering a message, or bringing an object to a location such as an office, lecture hall, or classroom. The process of constructing a map that a robot can use for navigation has been simplified by modern SLAM algorithms. The attachment of semantics to map data, however, remains a tedious manual process of labeling locations in otherwise automatically generated maps. This paper introduces a system called PRISM to automate a step in this process by enabling a robot to localize door signs - a semantic markup intended to aid the human occupants of a building - and to annotate these locations in its map.

Soft robotics is a swiftly growing research area these days. Modeling continuum robots accurately is still a demanding field. The paper aims to propose a shape reconstruction method and the estimation of the kinematic behavior of heterogeneous continuum robot in closed loop kinematic configuration, by using Pythagorean Hodograph (PH) curves. The validation of the model approach has been tested on cooperative continuum robots, namely Compact Bionic Handling Arms (CBHA), driving an intermediate flexible rope (a passive flexible link), by using a 3D tracking system. Experimental comparison of the proposed approach with the existing approaches is performed in terms of accuracy as well as the time cost.

Point cloud registration is a popular research topic and has been widely used in many tasks, such as robot mapping and localization. It is a challenging problem when the overlap is small, or the rotation is large. The problem has not been well solved by existing methods such as the iterative closest point (ICP) and its variants. In this paper, a novel method named principal coordinate alignment with overlap tuning (PCAOT) is proposed based on the Manhattan world assumption. It solves two key problems together, the transformation estimation and the overlap estimation. The overlap is represented by a 3D cuboid and the transformation is computed only within the overlap region. Instead of finding point correspondence as in traditional methods, we estimate the rotation by principal coordinates alignment, which is faster and less sensitive than ICP and its variants to small overlaps and large rotations. Evaluations demonstrate that our method achieves much better results than the ICP and its variants when the overlap ratio is smaller than 50%, or the rotation angle is larger than 60°. Especially, it is effective when the overlap ratio is less than 30%, or the rotation angle is larger than 90°.

We present the first perception-aware model predictive control framework for quadrotors that unifies control and planning with respect to action and perception objectives. Our framework leverages numerical optimization to compute trajectories that satisfy the system dynamics and require control inputs within the limits of the platform. Simultaneously, it optimizes perception objectives for robust and reliable sensing by maximizing the visibility of a point of interest and minimizing its velocity in the image plane. Considering both perception and action objectives for motion planning and control is challenging due to the possible conflicts arising from their respective requirements. For example, for a quadrotor to track a reference trajectory, it needs to rotate to align its thrust with the direction of the desired acceleration. However, the perception objective might require to minimize such rotation to maximize the visibility of a point of interest. A model-based optimization framework, able to consider both perception and action objectives and couple them through the system dynamics, is therefore necessary. Our perception-aware model predictive control framework works in a receding-horizon fashion by iteratively solving a non-linear optimization problem. It is capable of running in real-time, fully onboard our lightweight, small-scale quadrotor using a low-power ARM computer, together with a visual-inertial odometry pipeline. We validate our approach in experiments demonstrating (I) the conflict between perception and action objectives, and (II) improved behavior in extremely challenging lighting conditions.

We propose a novel method to enable fast autonomous flight in cluttered environments. Typically, autonomous navigation through a complex environment requires a continuous heuristic search on a graph generated by a k-connected grid or a probabilistic scheme. As the vehicle progresses, modification of the graph with data from onboard sensors is expensive as is search on the graph, especially if the paths must be kino-dynamically feasible. We suggest that computation needed to find safe paths during fast flight can be greatly reduced if we precompute and carefully arrange a dense set of alternative paths before the flight. Any prior map information can be used to prune the alternative paths to come up with a data structure that enables very fast online computation to deal with obstacles that are not on the map but only detected by onboard sensors. To test this idea, we have conducted a large number of flight experiments in structured (large industrial facilities) and unstructured (forests-like) environments. We show that even in the most unstructured environments, this method enables flight at a speed up to 10m/s while avoiding obstacles detected from onboard sensors.

In many robotics tasks successful execution requires high precision pose estimates of the objects in the workcell. When the object pose is provided by a computer vision system it is therefore crucial that the vision system is configured such that the required precision is achieved. An important part of the configuration is the sensor placement, however, most work in the field of sensor placement does not take the random, semi-constrained nature of the initial object pose into account. This paper presents a framework which uses an analysis of object stable poses together with dynamic simulation to predict the probability distribution of initial object poses. The framework is highly modular and uses precomputed pose uncertainties and a mixture model to make the integration over all possible stable poses feasible. This makes the framework applicable to a wide range of sensors and uncertainty models. The framework is evaluated in simulation for a concrete example: A single PrimeSense Carmine to be placed at an optimal elevation angle in a table picking scenario where pose uncertainties are modeled using Gaussians.

Multi-beam lidar (MBL) scanners are compact, light, and accessible 3D sensors with high data rates, but they offer limited vertical resolution and field of view (FOV). Some recent robotics research has profited from the addition of a degree-of-freedom (DOF) to an MBL to build rotating multibeam lidars (RMBL) that can achieve high-resolution scans with full spherical FOV. In a previous work, we offered a methodology to analyze the complex 3D scan measurement distributions produced by RMBLs with a rolling DOF and no pitching. In this paper, we investigate the effect of introducing constant pitch angles in the construction of the RMBLs with the purpose of finding a kinematic configuration that optimizes scan homogeneity with a spherical FOV. To this end, we propose a scalar index of 3D sensor homogeneity that is based on the spherical formulation of Ripley's K function. The optimization is performed for the widely used Puck (VLP-16) and HDL-32 sensors by Velodyne.

Current ergonomic assessment procedures require observation and manual annotation of postures by an expert, after which ergonomic scores are inferred from these annotations. Our aim is to automate this procedure and to enable robots to optimize their behavior with respect to such scores. A particular challenge is that ergonomic scoring requires accurate biomechanical simulations which are computationally too expensive to use in robot control loops or optimization. To address this, we learn Contextual Ergonomics Models, which are Gaussian Process Latent Variable Models that have been trained with full musculoskeletal simulations for specific tasks contexts. Contextual Ergonomics Models enable search in a low-dimensional latent space, whilst the cost function can be defined in terms of the full high-dimensional musculoskeletal model, which can be quickly reconstructed from the latent space. We demonstrate how optimizing Contextual Ergonomics Models leads to significantly reduced muscle activation in an experiment with eight subjects performing a drilling task.

Robustness of indirect SLAM techniques to light changing conditions remains a central issue in the robotics community. With the change in the illumination of a scene, feature points are either not extracted properly due to low contrasts, or not matched due to large differences in descriptors. In this paper, we propose a multi-layered image representation (MLI) in which each layer holds a contrast enhanced version of the current image in the tracking process in order to improve detection and matching. We show how Mutual Information can be used to compute dynamic contrast enhancements on each layer. We demonstrate how this approach dramatically improves the robustness in dynamic light changing conditions on both synthetic and real environments compared to default ORB-SLAM. This work focalises on the specific case of SLAM relocalisation in which a first pass on a reference video constructs a map, and a second pass with a light changed condition relocalizes the camera in the map.

This paper develops new methods to design high performance rotary series elastic actuator springs for robotics applications. The approach is based on a spring arm mathematical model that was previously introduced by the authors. The key contribution is the development of an optimization-based design method which maximizes the springs' overall torque density through optimization of the arm profile. An improved analysis algorithm allows for rapid torsional loading response simulation with possible internal contacts between the spring arms. The proposed design and analysis algorithms are validated through FEA and prototype mechanical testing.

In this paper, we present a framework to do optimal time allocation for quadrotor trajectory generation. Using this method, we can generate minimum-time piecewise polynomial trajectories for quadrotor flights. We decouple the quadrotor trajectory generation problem into two folds. Firstly we generate a smooth and safe curve which is parameterized by a virtual variable. This curve named spatial trajectory is independent of time and has fixed spatial properties. Then a mapping function which decides how the quadrotor moves along the spatial trajectory respecting kinodynamic limits is found by minimizing total trajectory time. The mapping function maps the virtual variable to time is named temporal trajectory. We formulate the minimum-time temporal trajectory generation problem as a convex program which can be efficiently solved. We show that the proposed method can corporate with various types of previous trajectory generation method to obtain the optimal time allocation. The proposed method is integrated into a customized light-weight quadrotor platform and is validated by presenting autonomous flights in indoor and outdoor environments. We release our code for time optimization as an open-source ros-package.

In this paper, we consider the problem of maintaining and restoring connectivity among a set of agents (humans or robots) by incrementally redeploying a team of mobile robots acting as communication relays. This problem is relevant in numerous scenarios where humans and robots are jointly deployed for tasks like urban search and rescue, surveillance, and the like. In this case, as the humans move in the environment, connectivity may be broken, and consequently, robots need to reposition themselves to restore it. We study the computational complexity of the problem, also in terms of approximation hardness, and present an Integer Linear Programming formulation to compute optimal solutions. We then analyze the performance of the proposed resolution approach against a heuristic algorithm taken from the literature, and we demonstrate how our method favorably compares in terms of solution quality and scalability.

Precisely manipulating the center of mass (CoM) of the underactuated locomotion robot can't be easily achieved by common control mechanisms which apply only joint torques. A novel and indirect method has been recently introduced using an active wobbling mass attached to limit cycle walkers. The next important issue is to design an optimal control input to reduce the forcing energy. In this paper, we use combined rimless wheels as a simplified example to apply our method, which is based on the theory of phase oscillators. First, we introduce the typical modeling and control of this underactuated robot. Second, we obtain the phase response curve by numerically applying perturbations at different phases of the walker's gait interval and calculating the deviations from the unperturbed. Third, we analytically derive an optimal forcing waveform for the wobbling mass to entrain the combined rimless wheel based on the phase response curve. As an ecological extension, an ideal forcing waveform for m: 1 entrainment was further generated. Finally, the proposed method was evaluated by locking range of the Arnold tongues. The results show that the optimal forcing waveform we derived achieves the best performance for 1:1 entrainment among all the candidates. One of the strongest advantages of our method is the easiness of its implementation, prompting its applicability to a wide variety of locomotion systems.

A number of researches have been focusing on the development and control of robots with soft structures such as flexible musculoskeletal systems. Thus far, it has been reported that these robots can achieve high adaptability to environments despite their extremely simple controllers. However, because these robots are difficult to model mathematically, there is still no systematic design policy, in which control theory has been playing a role in conventional robotics, for constituting simple controllers. To tackle this problem, we propose a new approach using a neural network to obtain mathematical models. In particular, with this method, the control theory is applied to linear system models extracted from a network trained to express the forward dynamics of a robot. Through simulations, the validity and advantage of the proposed method was successfully confirmed.

In this paper, we present a trajectory generation method for quadrotors based on the optimal smoothing B-spline. Compared to existing methods which rely on polynomial splines or time optimal control techniques, our method systematically addresses the issue of axes-coupled and interval-wise constraints. These constraints can be used to construct safe flying zones and satisfy vehicle's physical limits. The proposed approach has also been extended to generate trajectories from the nominal plan which consists of not only points but also lines and planes, opening a door for new improvements and applications. Moreover, a closed-form solution can be obtained for cases without inequality constraints. Such a solution is numerically stable for the large-scale fitting problem, which allows us to directly fit the human sketching input from the touch device and capture all subtle details. Our approach is verified by various real flight experiments..