IROS 2016 Papers

Facing the new challenges of the spacecraft developing, the concept of cellular space robot (CSR) for both space-craft system construction and on-orbit operation is presented in this paper. The system description and design principles are introduced to ensure the flexibility of the system. And dynamics model for takeover control is developed. After that, the regression models for the parameter identification are deduced based on the dynamics model. An interaction model identification algorithm is presented to solve the parameter identification problem for the distributed cells. Besides, the interactive model identification is validated by simulations. The simulations show that the interactive model identification method can achieve the consensus and convergence.

Cats have protractible claws to fold their tips to keep them sharp. They protract claws while hunting and pawing on slippery surfaces. Protracted claws by tendons and muscles of toes can help cats anchoring themselves steady while their locomotion trends to slip and releasing the hold while they retract claws intentionally. This research proposes a kind of modularized self-adaptive toe mechanism inspired by cat claws to improve the extremities' contact performance for legged robot. The mechanism is constructed with four-bar linkage actuated by contact reaction force and retracted by applied spring tension. A feasible mechanical design based on several essential parameters is introduced and an integrated Sole-Toe prototype is built for experimental evaluation. Mechanical self-adaption and actual contact performance on specific surface have been evaluated respectively on a biped walking platform and a bench-top mechanical testing.

This paper presents a 3D vision system for robotic harvesting of broccoli using low-cost RGB-D sensors. The presented method addresses the tasks of detecting mature broccoli heads in the field and providing their 3D locations relative to the vehicle. The paper evaluates different 3D features, machine learning and temporal filtering methods for detection of broccoli heads. Our experiments show that a combination of Viewpoint Feature Histograms, Support Vector Machine classifier and a temporal filter to track the detected heads results in a system that detects broccoli heads with 95.2% precision. We also show that the temporal filtering can be used to generate a 3D map of the broccoli head positions in the field.

This paper presents a novel rotation estimation approach based on 3D mesh representation of spherical images. Indeed, unit sphere is becoming a natural space for projecting images captured from the central cameras (conventional and non-conventional cameras, as omnidirectional camera) and obtaining the spherical images. The proposed method relies on representing the spherical images into a 3D space based on image intensities. Spherical harmonic coefficients are then calculated for the 3D mesh surfaces and used to estimate an initial rotation between the underlying spherical images in spectral domain. The optimal rotation is then refined through the ICP algorithm. Experimental results, using synthetic and real image dataset, demonstrate the effectiveness of the proposed approach for rotation estimation, as well as its robustness against real conditions and images occlusion. A comparison between the proposed method and competitive ones, is performed.

Multi-camera clusters used for visual SLAM assume a fixed calibration between the cameras, which places many limitations on its performance, and directly excludes all configurations where a camera in the cluster is mounted to a moving component. In this work, we present a calibration method for dynamic multi-camera clusters, where one or more of the cluster cameras is mounted to an actuated mechanism, such as a gimbal or robotic manipulator. Our calibration approach parametrizes the actuated mechanism using the Denavit-Hartenberg convention, then determines the calibration parameters which allow for the estimation of the time varying extrinsic transformations between camera frames. We validate our calibration approach using a dynamic camera cluster consisting of a static camera and a camera mounted to a pan-tilt unit, and demonstrate that the dynamic camera cluster can provide accurate tracking when used to perform SLAM.

Cheap RGB-D sensors are ubiquitous in robotics. They typically contain a consumer-grade color camera that suffers from significant optical nonlinearities, often referred to as vignetting effects. For example, in Asus Xtion Live Pro cameras the pixels in the corners are two times darker than those in the center of the image. This deteriorates the visual appearance of 3D maps built with such cameras. We propose a simple calibration method that only requires a sheet of white paper as a calibration object and allows to reliably recover the vignetting response of a camera. We demonstrate calibration results for multiple popular RGB-D sensors and show that removal of vignetting effects using a nonparametric response model results in improved color coherence of the reconstructed maps. Furthermore, we show how to effectively compensate color variations caused by automatic white balance and exposure time control of the camera.

The study of cable-driven parallel robots (CDPRs) has attracted much attention in recent years. However, to the best of the authors' knowledge, no single software platform exists for researchers to perform different types of analyses for CDPRs of arbitrary structure. In this paper, the Cable-robot Analysis and Simulation Platform for Research (CASPR) of CDPRs is introduced. Using this platform, arbitrary types and structures of CDPRs, such as single and multi-link CDPRs, can be studied for a wide range of analyses, including kinematics, dynamics, control and workspace analysis. CASPR achieves this using a general CDPR model representation and an abstracted software architecture. Moveover, CDPRs can be defined using Extensible Markup Language (XML) with out-of-the-box availability of an extensive range of robots and analysis tools. The open-source platform aims to provide both a communal environment for the researchers to use and add models and algorithms to. The example case studies demonstrate the potential to perform analysis on CDPRs, directly compare algorithms and conveniently add new models and analyses.

This paper presents Bézier curve based passive neural control applied in bio-inspired locomotion in order to decrease the computational cost implemented for 4 legged animal robot which has 3 joints in each leg. Neural oscillator model is applied for generating the walking pattern in bio-inspired locomotion. Bézier curve based optimization represents passive neural control supported by evolutionary algorithm for representing the relationship equation between neuron signal and reference joint signal. Passive neural control is implemented in order to reduce the neuron complexity in neuro-based locomotion by controlling 3 joints with one signal without decreasing the performance both in walking pattern and in its stability level, whereas one leg is represented by one motor neuron. Therefore, the 4 legged robot is controlled by 4 motor neurons which have feedback connection with ground and inertial sensor. In order to prove the effectiveness, we implemented the model in computer simulation and in a small 4 legged robot. This model can decrease the computational cost so it is possible to apply the model in either animal or humanoid robot with low frequency processor.

We present Buzz, a novel programming language for heterogeneous robot swarms. Buzz advocates a compositional approach, offering primitives to define swarm behaviors both from the perspective of the single robot and of the overall swarm. Single-robot primitives include robot-specific instructions and manipulation of neighborhood data. Swarm-based primitives allow for the dynamic management of robot teams, and for sharing information globally across the swarm. Self-organization stems from the completely decentralized mechanisms upon which the Buzz run-time platform is based. The language can be extended to add new primitives (thus supporting heterogeneous robot swarms), and its run-time platform is designed to be laid on top of other frameworks, such as the Robot Operating System. We showcase the capabilities of Buzz by providing code examples, and analyze scalability and robustness of the run-time platform through realistic simulated experiments with representative swarm algorithms.

Vision-based, route-following algorithms enable autonomous robots to repeat manually taught paths over long distances using inexpensive vision sensors. However, these methods struggle with long-term, outdoor operation due to the challenges of environmental appearance change caused by lighting, weather, and seasons. While techniques exist to address appearance change by using multiple experiences over different environmental conditions, they either provide topological-only localization, require several manually taught experiences in different conditions, or require extensive offline mapping to produce metric localization. For real-world use, we would like to localize metrically to a single manually taught route and gather additional visual experiences during autonomous operations. Accordingly, we propose a novel multi-experience localization (MEL) algorithm developed specifically for route-following applications; it provides continuous, six-degree-of-freedom (6DoF) localization with relative uncertainty to a privileged (manually taught) path using several experiences simultaneously. We validate our algorithm through two experiments: i) an offline performance analysis on a 9km subset of a challenging 27km route-traversal dataset and ii) an online field trial where we demonstrate autonomy on a small 250m loop over the course of a sunny day. Both exhibit significant appearance change due to lighting variation. Through these experiments we show that safe localization can be achieved by bridging the appearance gap.

Unmanned rescue, observation and/or research vehicles with high terrain adaptability, high speed, and high reliability are needed to reach difficult locations. However, most vehicles achieve improved performance over rough terrain at the expense of low speed and/or complex mechanisms. We developed a blade-type crawler robot with a very simple and reliable mechanism, capable of traversing uneven terrain at high speed, using aerodynamic devices. As these small devices are in the low Reynolds number region, we tested a wing that made use of the ground effect. We experimentally confirmed the success of this approach in improving the traveling speed and ability to traverse uneven terrain. The robot with aerodynamic lift was climbed 1.5 times higher obstacle than without wings.

For a biped robot to keep walking, basic walking patterns are needed. One method to generate these patterns is to use an extrapolated center of mass (XcoM) concept and constant zero-moment point (ZMP) patterns. However, center of mass (CoM) patterns generated from such a method have discontinuous ZMP and CoM accelerations. For solving this problem, we propose a method in which continuous ZMP patterns can be simply generated from constant ones. In addition, experimental results of forward walking are presented. The results demonstrate that this pattern generation method can give a starting point for walking robots.

Protecting robot from broken of falling is always a challenge issue for a bipedal humanoid robot in dealing with various locomotion related tasks to serve human society, especially as the assigned tasks turns increasingly complicated and the corresponding real environment gets more and more complex. Unlike several previous successful approaches on humanoid falling control, in this study, a new approach is suggested in the light of how human do when a fall happens. The proposed approach takes a tripod-like falling controller followed by a human-inspired active compliance strategy using joints' active flexion and torque increment. Therefore, robot falling action covers both stages of a fall, i.e. before and after landing impact, so that to reduce the fall damage as far as possible. And the tripod like posture prevents accumulation of kinetic energy, while the active compliance absorbs the impact energy in a tender way. Meanwhile, considering the complexity of robot dynamics, other than taking expert experiences, the proposed human-inspired falling control strategy is parametrically modelled and optimized with policy gradient reinforcement learning. Experiments on both simulation and real robot PKU-HR5.1 are performed, and the results demonstrate this approach is effective and promising.

Dielectric elastomer actuators (DEAs), a soft actuator technology, hold great promise for biomimetic underwater robots. The high-voltages required to drive DEAs can however make them challenging to use in water. This paper demonstrates a method to create DEA-based biomimetic swimming robots that operate reliably even in conductive liquids. We ensure the insulation of the high-voltage DEA electrodes without degrading actuation performance by laminating silicone layers. A fish and a jellyfish were fabricated and tested in water. The fish robot has a length of 120 mm and a mass of 3.8 g. The jellyfish robot has a 61 mm diameter for a mass of 2.6 g. The measured swimming speeds for a periodic 3 kV drive voltage were ∼8 mm/s for the fish robot, and ∼1.5 mm/s for the jellyfish robot.

This paper presents a planning algorithm for a 2-DOF robotic arm to bat a flying 2D object to a targeted location. Impact dynamics are combined with trajectory kinematics and manipulator dynamics to compute the evolving set of states (poses and velocities) of the arm able to achieve the task as the object is flying. Planning is conducted under the arm's dynamic and kinematic constraints. At the time of hit, the robot executes an action to minimize its total energy. Simulation and Experiments have been conducted using a Whole Arm Manipulator (WAM) from Barrett Technology, Inc.

This paper addresses the motion planning problem of a jumping point-robot. Each jump consists in a ballistic motion linking two positions in contact with obstacle surfaces. A solution path is thus a sequence of parabola arcs. The originality of the approach is to consider non-sliding constraints at contact points: slipping avoidance is handled by constraining takeoff and landing velocity vectors to 3D friction cones. Furthermore the magnitude of these velocities is bounded. The ballistic motion lying in a vertical plane, we transform the 3D problem into a 2D one. We then solve the motion equations. The solution gives rise to an exact steering method computing a jump path between two contact points while respecting all constraints. The method is integrated into a standard probabilistic roadmap planner. Probabilistic completeness is proven. Simulations illustrate the performance of the approach.

The work presented herein, attempts to address the problem of designing stepping recovery controllers for compliantly actuated humanoid robots. Based on the decomposition of the stepping procedure into three distinct phases, which are characterized by unique combinations of configurations and impedance levels, the contrivance of a Linear Quadratic Regulator (LQR) optimization process allows for the production of a corresponding number of controllers. The penalties associated with the proposed cost functions, which account for compliant dynamics and balance-related parameters alike, are selected in a systematic manner that facilitates the generation of the appropriate impedance levels required for each particular phase of the stepping motion. Subsequently, the superimposition of gravity compensation control, onto the original LQR controllers, renders them nonlinear and theoretically capable of tracking referential stepping trajectories. The associated referential motor positions are then generated by exploiting a formula relating the Centre-of-Pressure (CoP) to the compliant ankle dynamics, thereby satisfying the balancing constraints whilst also accounting for the system's inherent under-actuation. Thus, the technique's novelty stems from its explicit consideration of flexible joint dynamics and deflection torques, for the design of the desired impedance levels and joint stepping trajectories. The Series Elastic Actuator (SEA) powered COmpliant huMANoid (COMAN), has served as an avatar of the stepping recovery methodology that is expounded in the paper.

This paper deals with a trajectory tracking problem for the ball-pendulum system, a spherical rolling robot driven by a two degree of freedom pendulum. The backstepping technique is applied and first tested on the hoop-pendulum system, a planar case of the ball-pendulum. By mimicking the backstepping process of the planar case, a feedback controller for the ball-pendulum system is then proposed, tracking motion trajectories for both the position and orientation of the spherical shell of the rolling robot. The validity of the constructed tracking controller is demonstrated by establishing the asymptotic stability of the error dynamics for the closed-loop system. The performance of the controller is verified under simulations for tracking linear and circular motions.

Mobile manipulators installed in warehouses and factories for conveying goods between working stations need to meet the requirements of time-critical workflows. Moreover, the systems are expected to deal with changing tasks, cluttered environments and constraints imposed by the goods to be delivered. In this paper, we present a novel planning framework for generating asymptotically optimal paths for mobile manipulators subject to task constraints. Our approach introduces the Bidirectional Informed RRT* (BI2RRT*) that extends the Informed RRT* [1] towards bidirectional search and satisfaction of end-effector task constraints. In various experiments, we demonstrate the efficiency of BI2RRT* for both unconstrained and constrained mobile manipulation planning problems. As the results show, our planning framework finds better solutions than Informed RRT* and Bidirectional RRT* in less planning.