ICRA 2017 Papers

A new generation of practical, low-cost indoor robots is now using wide-angle cameras to aid navigation, but usually this is limited to position estimation via sparse feature-based SLAM. Such robots usually have little global sense of the dimensions, demarcation or identities of the rooms they are in, information which would be very useful to enable behaviour with much more high level intelligence. In this paper we show that we can augment an omni-directional SLAM pipeline with straightforward dense stereo estimation and simple and robust room model fitting to obtain rapid and reliable estimation of the global shape of typical rooms from short robot motions. We have tested our method extensively in real homes, offices and on synthetic data. We also give examples of how our method can extend to making composite maps of larger rooms, and detecting room transitions.

We propose two methods to robustify point correspondence based 6D object pose estimation. The first method, curvature filtering, is based on the assumption that low curvature regions provide false matches, and removing points in these regions improves robustness. The second method, region pruning, is more general by making no assumptions about local surface properties. Our region pruning segments a model point cloud into cluster regions and searches good region combinations using a validation set. The robustifying methods are general and can be used with any correspondence based method. For the experiments, we evaluated three correspondence selection methods, Geometric Consistency (GC) [1], Hough Grouping (HG) [2] and Search of Inliers (SI) [3] and report systematic improvements for their robustified versions with two distinct datasets.

This paper discusses the resolved viscoelasticity control (RVC) method that explicitly considers the structure-variability for a humanoid. In a previous report, the author proposed resolving the virtual viscoelasticity at the center of gravity into the joint viscoelasticity considering redundant degrees of freedom, and named this method as RVC. However, the author considered only the single support phase; therefore, the humanoid could be regarded as an open kinematic chain and the RVC was implemented easily. In this paper, the author extends the previous work on the RVC by considering structure-variability - the method now considers an open kinematic chain in the single support phase and a closed kinematic chain in the double support phase. This extension helps realize stable and robust walking motion on uneven terrains. The proposed method is validated using forward dynamics simulations.

Agile robots, such as small Unmanned Aerial Vehicles (UAVs) can have a great impact on the automation of tasks, such as industrial inspection and maintenance or crop monitoring and fertilization in agriculture. Their deploy-ability, however, relies on the UAV's ability to self-localize with precision and exhibit robustness to common sources of uncertainty in real missions. Here, we propose a new system using the UAV's onboard visual-inertial sensor suite to first build a Reference Map of the UAV's workspace during a piloted reconnaissance flight. In subsequent flights over this area, the proposed framework combines keyframe-based visual-inertial odometry with novel geometric image-based localization, to provide a real-time estimate of the UAV's pose with respect to the Reference Map paving the way towards completely automating repeated navigation in this workspace. The stability of the system is ensured by decoupling the local visual-inertial odometry from the global registration to the Reference Map, while GPS feeds are used as a weak prior for suggesting loop closures. The proposed framework is shown to outperform GPS localization significantly and diminishes drift effects via global image-based alignment for consistently robust performance.

We present an illumination-robust visual localization algorithm for Astrobee, a free-flying robot designed to autonomously navigate on the International Space Station (ISS). Astrobee localizes with a monocular camera and a pre-built sparse map composed of natural visual features. Astrobee must perform tasks not only during the day, but also at night when the ISS lights are dimmed. However, the localization performance degrades when the observed lighting conditions differ from the conditions when the sparse map was built. We investigate and quantify the effect of lighting variations on visual feature-based localization systems, and discover that maps built in darker conditions can also be effective in bright conditions, but the reverse is not true. We extend Astrobee's localization algorithm to make it more robust to changing-light environments on the ISS by automatically recognizing the current illumination level, and selecting an appropriate map and camera exposure time. We extensively evaluate the proposed algorithm through experiments on Astrobee.

Traditional stereo matching approaches generally have problems in handling textureless regions, strong occlusions and reflective regions that do not satisfy a Lambertian surface assumption. In this paper, we propose to combine the predicted surface normal by deep learning to overcome these inherent difficulties in stereo matching. With the selected reliable disparities from stereo matching method and effective edge fusion strategy, we can faithfully convert the predicted surface normal map to a disparity map by solving a least squares system which maintains discontinuity on object boundaries and continuity on other regions. Then we refine the disparity map iteratively by bilateral filtering-based completion and edge feature refinement. Experimental results on the Middlebury dataset and our own captured stereo sequences demonstrate the effectiveness of the proposed approach.

We present a simple probabilistic framework for multimodal sensor fusion that allows a mobile robot to reliably locate and approach the most promising interaction partner among a group of people, in an uncontrolled environment. Our demonstration integrates three complementary sensor modalities, each of which detects features of nearby people. The output is an occupancy grid approximation of a probability density function over the locations of people that are actively seeking interaction with the robot. We show empirically that simply driving towards the peak of this distribution is sufficient to allow the robot to correctly engage an interested user in a crowd of bystanders.

This work studies the design of reliable control laws of robotic systems operating in uncertain environments. We introduce a new approach to stochastic policy optimization based on probably approximately correct (PAC) bounds on the expected performance of control policies. An algorithm is constructed which directly minimizes an upper confidence bound on the expected cost of trajectories instead of employing a standard approach based on the expected cost itself. This algorithm thus has built-in robustness to uncertainty, since the bound can be regarded as a certificate for guaranteed future performance. The approach is evaluated on two challenging robot control scenarios in simulation: a car with side slip and a quadrotor navigating through obstacle-ridden environments. We show that the bound accurately predicts future performance and results in improved robustness measured by lower average cost and lower probability of collision. The performance of the technique is studied empirically and compared to several existing policy search algorithms.

We present a framework for online generation of robust motion plans for robotic systems with nonlinear dynamics subject to bounded disturbances, control constraints, and online state constraints such as obstacles. In an offline phase, one computes the structure of a feedback controller that can be efficiently implemented online to track any feasible nominal trajectory. The offline phase leverages contraction theory and convex optimization to characterize a fixed-size “tube” that the state is guaranteed to remain within while tracking a nominal trajectory (representing the center of the tube). In the online phase, when the robot is faced with obstacles, a motion planner uses such a tube as a robustness margin for collision checking, yielding nominal trajectories that can be safely executed, i.e., tracked without collisions under disturbances. In contrast to recent work on robust online planning using funnel libraries, our approach is not restricted to a fixed library of maneuvers computed offline and is thus particularly well-suited to applications such as UAV flight in densely cluttered environments where complex maneuvers may be required to reach a goal. We demonstrate our approach through simulations of a 6-state planar quadrotor navigating cluttered environments in the presence of a cross-wind. We also discuss applications of our approach to Tube Model Predictive Control (TMPC) and compare the merits of our method with state-of-the-art nonlinear TMPC techniques.

This work addresses the problem of motion planning among obstacles for quadrotor platforms under external disturbances and with model uncertainty. A novel Nonlinear Model Predictive Control (NMPC) optimization technique is proposed which incorporates specified uncertainties into the planned trajectories. At the core of the procedure lies the propagation of model parameter uncertainty and initial state uncertainty as high-confidence ellipsoids in pose space. The quadrotor trajectories are then computed to avoid obstacles by a required safety margin, expressed as ellipsoid penetration while minimizing control effort and achieving a user-specified goal location. Combining this technique with online model identification results in robust obstacle avoidance behavior. Experiments in outdoor scenarios with virtual obstacles show that the quadrotor can avoid obstacles robustly, even under the influence of external disturbances.

This paper presents a robust localization approach that fuses measurements from inertial measurement unit (IMU) and a rotating laser scanner. An Error State Kalman Filter (ESKF) is used for sensor fusion and is combined with a Gaussian Particle Filter (GPF) for measurements update. We experimentally demonstrated the robustness of this implementation in various challenging situations such as kidnapped robot situation, laser range reduction and various environment scales and characteristics. Additionally, we propose a new method to evaluate localizability of a given 3D map and show that the computed localizability can precisely predict localization errors, thus helps to find safe routes during flight.

In this paper, previous works on the Model Predictive Control (MPC) and the Divergent Component of Motion (DCM) for bipedal walking control are extended. To this end, we employ a single MPC which uses a combination of Center of Pressure (CoP) manipulation, step adjustment, and Centroidal Moment Pivot (CMP) modulation to design a robust walking controller. Furthermore, we exploit the concept of time-varying DCM to generalize our walking controller for walking in uneven surfaces. Using our scheme, a general and robust walking controller is designed which can be implemented on robots with different control authorities, for walking on various environments, e.g. uneven terrains or surfaces with a very limited feasible area for stepping. The effectiveness of the proposed approach is verified through simulations on different scenarios and comparison to the state of the art.

Marine robots and unmanned surface vehicles will increasingly be deployed in rivers and riverine environments. The structure produced by flowing waters may be exploited for purposes of estimation, planning, and control. This paper adopts a widely acknowledged model for the geometry of watercourse channels, namely sine-generated curves, as a basis for estimators that predict the shape of the yet unseen portion of the river. Predictions of this sort help a robot anticipate the future, for example, in throttling speeds as it rounds a bend. After examining how to reparameterize standard filters to incorporate this model, we compare the performance of three Gaussian filters and show that nonideality and theoretical challenges (of non-linearity, multi-modality/periodicity) degrade the performance of standard Kalman filters severely, but can be successfully mitigated by imposing an interval constraint. Thereafter, we present results of a constrained interval Kalman filter on data from three natural rivers. The results we report show the effectiveness of our method on the estimation of meander parameters. The results we report, including data from simulation, from maps, and from GPS tracks of a boat on the Colorado river, show the effectiveness of our method on the estimation of meander parameters.

Helical structure assembled by hydrogel microfibers is significant for culture of smooth muscle cells. However, the helical structure is only fabricated at the macroscale, while the fabrication of helical microstructure is still a challenge due to the lack of assembly method. In this paper, we propose a robotics-based assembly method to handle such challenge. An electromagnetic needle (EMN) is employed as end-effector to magnetically reel the microfiber encapsulating magnetic nanoparticles around a micropillar, and a dual-ring structure is designed to keep the microfiber being attracted at the EMN tip. For enhancing the stability of tip attraction, the manipulation mode of anticlockwise pushing microfiber is established. Moreover, the interaction mechanism between EMN tip and microfiber is analyzed by developing a static force model, and then the key condition of stably reeling microfiber is concluded. Furthermore, a robotics-based motion trajectory of EMN tip is planned to achieve a smooth reeling process. Based on such planning, the size of dual-ring structure is further optimized to improve the success rate of reeling. Finally, the helical microstructure with there-turn coils is successfully fabricated.

Water is a highly valuable resource so asset management of associated infrastructure is of critical importance. Water distribution pipe networks are usually buried, and so are difficult to access. Robots are therefore appealing for performing inspection and detecting damage to target repairs. However, robot mapping and localisation of buried water pipes has not been widely investigated to date, and is challenging because pipes tend to be relatively featureless. In this paper we propose a mapping and localisation algorithm for metal water pipes with two key novelties: the development of a new type of map based on hydrophone induced vibration signals of metal pipes, and a mapping algorithm based on spatial warping and averaging of dead reckoning signals used to calibrate the map (using dynamic time warping). Localisation is performed using both terrain-based extended Kalman filtering and also particle filtering. We successfully demonstrate and evaluate the approach on a combination of experimental and simulation data, showing improved localisation compared to dead reckoning.

The objective of this paper is to present a percussive massage based on robotic tapping motion, and it is also the first paper to discuss the massage technique for this kind. The tapping motion is different from the common contact motion, it satisfies with the short-time contact on human muscles, and the robot end-effector itself has an initial velocity and acceleration to make an impulse contact. After contacting with human muscles, the robot manipulator stops and end-effector repeatedly performs the tapping motion. To tackle with the tapping motion problems, we utilize the online trajectory generator (OTG) based on the impedance control, which ensures the safety of human without any possible accident which can hurt human. We implement the tapping motion with a dual arm robot developed at the iCeiRA lab in NTU. By the Cartesian space teach function and the concept of virtual point, the tapping force and contact position can be changed adaptively to meet different needs.

Robot Coverage Path planning (i.e., the process of providing full coverage of a given domain by one or multiple robots) is a classical problem in the field of robotics and motion planning. The goal of such planning is to provide nearly full coverage while also minimize duplicately visited area. In this paper, we focus on the scenario of path planning on general surface, including planar domains with complex topology, complex terrain, and general surface in 3D space. Our approach described in this paper adopts a natural, intrinsic and global parametrization of the surface for robot path planning, namely the holomorphic quadratic differentials. We give each point on the surface a uv-coordinates naturally represented by a complex number, except for a small number of zero points (singularities). We show that natural, efficient robot paths can be obtained by using such coordinate systems. The method is based on intrinsic geometry and thus can be adapted to general surface exploration in 3D.

Minimally Invasive Surgery (MIS) can reduce iatrogenic injury and decrease the possibility of surgical complications. This paper presents a novel flexible and bendable endoscopic device, “Roboscope”, which delivers two instruments, two miniature scanning fiber endoscopes, and a suction/irrigation port to the operation site through a single portal. Compared with existing bendable and steerable robotic surgical systems, Roboscope provides two bending degrees of freedom for its outer sheath and two insertion degrees of freedom, while simultaneously delivering two instruments and two endoscopes to the surgical site. Each bending axis and insertion freedom of Roboscope is independently controllable via an external actuation pack. Surgical tools can be changed without retracting the robot arm. This paper presents the design of the Roboscope mechanical system, electrical system, and control and software systems, design requirements and prototyping validation as well as analysis of Roboscope workspece.

This paper presents a robotic system - RoboFDM that targets at printing 3D models without support-structures, which is considered as the major restriction to the flexibility of 3D printing. The hardware of RoboFDM consists of a robotic arm providing 6-DOF motion to the platform of material accumulation and an extruder forming molten filaments of polylactic acid (PLA). The fabrication of 3D models in this system follows the principle of fused decomposition modeling (FDM). Different from conventional FDM, an input model fabricated by RoboFDM is printed along different directions at different places. A new algorithm is developed to decompose models into support-free parts that can be printed one by one in a collision-free sequence. The printing directions of all parts are also determined during the computation of model decomposition. Experiments have been successfully taken on our RoboFDM system to print general freeform objects in a support-free manner.