IROS 2016 论文列表

We propose a submap-based technique for mapping of underwater structures with complex geometries. Our approach relies on the use of probabilistic volumetric techniques to create submaps from multibeam sonar scans, as these offer increased outlier robustness. Special attention is paid to the problem of denoising/enhancing sonar data. Pairwise submap alignment constraints are used in a factor graph framework to correct for navigation drift and improve map accuracy. We provide experimental results obtained from the inspection of the running gear and bulbous bow of a 600-foot, Wright-class supply ship.

We present the first demonstration of end-to-end far-to-near situated interaction between an uninstrumented human user and an initially distant outdoor autonomous Unmanned Aerial Vehicle (UAV). The user uses an arm-waving gesture as a signal to attract the UAV's attention from a distance. Once this signal is detected, the UAV approaches the user using appearance-based tracking until it is close enough to detect the human's face. Once in this close-range interaction setting, the user is able to use hand gestures to communicate its commands to the UAV. Throughout the interaction, the UAV uses colored-light-based feedback to communicate its intent to the user. We developed this system to work reliably with a low-cost consumer UAV, with only computation off-board. We describe each component of this interaction system, giving details of the depth estimation strategy and the cascade predictive flight controller for approaching the user. We also present experimental results on the performance of the complete system and its individual components.

In this paper we describe and flight test a novel system architecture for low cost muti-rotor unmanned aerial vehicles (UAVs) for searching, tracking and following a ground target. The UAV uses only on-board sensors for localisation within a GPS-denied space with obstacles. This mission is formulated as a Partially Observable Markov Decision Process (POMDP) and uses a modular framework that runs on the Robotic Operating System (ROS). This system computes a policy for executing actions instead of way-points to navigate and avoid obstacles. Results indicate that the system is robust to overcome uncertainties in localisation of both, the aircraft and the target and avoids collisions with the obstacles.

Electrotactile stimulation has been used to provide sensory information of forearm prosthesis to users. Although conventional sensory feedback method, where one electrode expresses sensory information of only one finger, could provide force information of three fingers by using three electrodes, it showed less cognitive accuracy when more than two electrodes were stimulated simultaneously compared to individual stimulation. To improve the cognitive accuracy, we presented a sensory feedback method called two-channel electrotactile stimulation for the thumb, index and middle fingers using only two electrodes. The force information of the index and middle fingers was delivered into two electrodes, respectively, by intermittent stimulation. The information of the thumb was delivered to the user by inserting additional offset pulses onto the channel of the index finger based on an assumption that the force of thumb is proportional to the sum of the forces of the index and middle fingers. The presence of offset pulses in the channel indicates the binary state of thumb if it contacts with an object or not. We conducted two psychophysical experiments where healthy subjects classified the binary states of each finger and identified intensity from two-channel stimuli. The cognitive accuracies for intensity identification were 78.8% and 62.2% for the intermittent stimulation and conventional method, respectively, and accuracy for classifying the fingers was 93.1% for every combination of the three fingers. The results demonstrated that the proposed two-channel electrotactile stimulation could be an attractive method to express the information of fingers of prosthesis with high accuracy.

This work focuses on the design and predicted performance of a two meter wingspan solar powered unmanned aerial vehicle (UAV). Such a platform would be ideal for distributed robotics applications because it combines the portability and deployment simplicity of a small airframe with the long flight time of a solar UAV. Methods to design and predict properties of a two meter solar UAV are described including airframe type selection, mass estimation, and propulsion requirements. A simplified approach to predict flight time is presented as well as an improved metric for quantifying multiday flight robustness. Maximum flight time for the two meter airframe considered is estimated to be greater than ten hours which is an order of magnitude improvement over reported commercially available options. In terms of multi-day flight capability, total mass is predicted to be within the bounds of a realizable aircraft based on extrapolation from larger experimentally tested multi-day solar UAVs.

In this paper, an ongoing work for verifying the behavior of a twisted string actuator in contact with a sliding surface or guided through a sheath is presented. The twisted string actuation system is particularly suitable for very compact and light-weight robotic devices, like artificial limbs and exoskeletons, since it allows the implementation of powerful tendon-based driving systems, based on small-size DC motors characterized by high speed, low torque and very limited inertia. One of the major limitations of this actuation system is by now related to the fact that the string should not be in contact with any obstacle, because this contact will alter the twisting angle propagation along the string and, eventually, completely stop the string twisting. This design constraint imposes a straight path between the motor and the linear load attached to the other string end. After the presentation of the basic properties of the twisted string actuation system, the model of the twisted string in contact with a sliding surface is discussed. The behavior of the system has been then experimentally verified and discussed. A preliminary evaluation of control strategies for compensating the side effects generated by the contact of the twisted string with the sliding surface is also presented.

Symbolic motion planning for robots is the process of specifying and planning robot tasks in a discrete space, then carrying them out in a continuous space in a manner that preserves the discrete-level task specifications. Despite progress in symbolic motion planning, many challenges remain, including addressing scalability for multi-robot systems and improving solutions by incorporating human intelligence in an adaptive fashion. In this paper, we use local communication, observation, control protocols, and compositional reasoning approaches to decompose the planning problem to address scalability. To address solution quality and adaptability, we use a dynamic and computational trust model to aid this decomposition and to implement real-time switching between automated and human motion planning. A simulation is provided demonstrating the successful implementation of these methods.

Humanoid robot has the potential to manipulate wide range of tools in daily life. Arms and legs of humanoid robot contribute this ability. Above all, manipulation tasks for vehicles which are the same size as a life-sized humanoid or larger size than it require the operational motion by both arms and legs of humanoid robot. In addition to the arms and legs cooperative motion control, it is also important for humanoid robot to stabilize self posture during driving vehicle. In this research, we focus on the arms-legs-integrated manipulation task for tricycle controlled by humanoid robot. We propose dual manipulators control law that is defined as active manipulator which works movable objects such as handle and crank, and passive manipulator which follows the movement of this objects. We discuss the self stabilizing strategy for humanoid robot by both active manipulating legs as well as manipulation strategy for objects. Furthermore, this paper contributes the strategy of recognition and planning for outside obstacle situations and configures the tricycle manipulation system. Applying this proposed system, we show the experimental result for tricycle manipulation by life-sized humanoid robot HRP2-JSK on obstacle-mixed situation.

We present an aerial robotic platform for remote tree cavity inspection, based on a hexacopter Micro-Aerial vehicle (MAV) equipped with a dexterous manipulator. The goal is to make the inspection process safer and more efficient and facilitate data collection about tree cavities, which are important for the conservation of biodiversity in forest ecosystems. This work focuses on two key enabling technologies, namely a vision-based cavity detection system and strategies for high level control of the MAV and manipulator. The results of both simulation and real-world experiments are discussed at the end of the paper and demonstrate the effectiveness of our approach.

This paper presents and discusses the trajectory tracking control design with friction compensation for a three wheeled omnidirectional mobile robot. Firstly, a dynamic model without considering the friction forces is derived. Part of the control effort is used to compensate the friction effects, which is estimated by an extended state observer without using a friction model. Then traditional resolved acceleration control is applied to the robot, based on the derived dynamic model. In addition, stability analysis of the controller and observer is presented. Experimental results show that the proposed control design is efficient in compensating the friction effects and in improving the tracking control performances.

An effective robot trajectory representation should encode all relevant aspects of the desired motion. For kinematic representations, this means that both the spatial course of the trajectory and its speed profile must be specified. The concept of dynamic movement primitives (DMP) provides a kinematic representation that fully specifies these two aspects of motion. They are, however, not separated from each other within the DMP representation. This can be problematic when movements with significant speed variations are compared within movement recognition and skill learning algorithms. In such comparisons it is often important to distinguish between the spatial and temporal aspects of motion. In this paper we propose a new representation based on dynamic movement primitives, where spatial and temporal aspects are well separated. We demonstrate the effectiveness of the proposed representation for statistical learning of robot skills and movement recognition and compare the performance with standard DMPs, where temporal and spatial aspects of motion are intertwined.

This work proposes a framework that enables arbitrary robots with unknown kinematics models to imitate human demonstrations to acquire a skill, and reproduce it in real-time. The diversity of robots active in non-laboratory environments is growing constantly, and to this end we present an approach for users to be able to easily teach a skill to a robot with any body configuration. Our proposed method requires a motion trajectory obtained from human demonstrations via a Kinect sensor, which is then projected onto a corresponding human skeleton model. The kinematics mapping between the robot and the human model is learned by employing Local Procrustes Analysis, which enables the transfer of the demonstrated trajectory from the human model to the robot. Finally, the transferred trajectory is modeled using Dynamic Movement Primitives, allowing it to be reproduced in real time. Experiments in simulation on a 4 degree of freedom robot show that our method is able to correctly imitate various skills demonstrated by a human.

This paper introduces a motion planning method to generate ladder climbing motion for a four-limbed robot. This method contains the following points: (1) independent planning of path and time in 3 dimensional space for trajectory planning; (2) path length minimization according to given midpoints. In trajectory planning, arc-length parameterization is used to separate path planning and time planning so that they can be done independently. After path is planned, time planning along the planned path can be given freely to meet our requirement, such as speed and acceleration adjustment for the protection of motors, optimization for dynamics analysis, dynamic obstacle avoidance and so on. Results from simulations and experiments authenticate the validity of our motion generation method.

This work demonstrates fast motion planning for robot locomotion that is optimized for terrain with complex dynamics, specifically, rapid penetration of granular media. Gait planning is critical for many legged locomotion control approaches, but they typically assume rigid ground contact. We aim to extend these planning methods to include terrain dynamics we see in the natural world, like sand and dirt, which can both deform and fluidize. Using an added-mass description of collective grain motion, we formulated a model of hydrostatic and hydrodynamic terrain effects that is both principled and representable with closed-form dynamics. As a result, we present a model and fast optimization formulation which solves accurate motion plans on granular media with tractable solving times (6.4–3.8 seconds). For validation, we optimized open-loop motor trajectories for a testbed jumping robot to jump to a target apex height from a bed a loosely packed poppy seeds, a model granular medium. While jumps optimized for rigid ground were anemic on granular media, terrain-aware trajectories hit within 6% of their target. This demonstrates the potential for robot locomotion which meets practical task demands, all while being aware of the terrain beneath it.

We address the challenging problem of tracking a moving target in cluttered environments using a quadrotor. Our online trajectory planning method generates smooth, dynamically feasible, and collision-free polynomial trajectories that follow a visually-tracked moving target. As visual observations of the target are obtained, the target trajectory can be estimated and used to predict the target motion for a short time horizon. We propose a formulation to embed both limited horizon tracking error and quadrotor control costs in the cost function for a quadratic programming (QP), while encoding both collision avoidance and dynamical feasibility as linear inequality constraints for the QP. Our method generates tracking trajectories in the order of milliseconds and is therefore suitable for online target tracking with a limited sensing range. We implement our approach on-board a quadrotor testbed equipped with cameras, a laser range finder, an IMU, and onboard computing. Statistical analysis, simulation, and real-world experiments are conducted to demonstrate the effectiveness of our approach.

Learning of inverse dynamics modeling errors is key for compliant or force control when analytical models are only rough approximations. Thus, designing real time capable function approximation algorithms has been a necessary focus towards the goal of online model learning. However, because these approaches learn a mapping from actual state and acceleration to torque, good tracking is required to observe data points on the desired path. Recently it has been shown how online gradient descent on a simple modeling error offset term to minimize tracking at acceleration level can address this issue. However, to adapt to larger errors a high learning rate of the online learner is required, resulting in reduced compliancy. Thus, here we propose to combine both approaches: The online adapted offset term ensures good tracking such that a nonlinear function approximator is able to learn an error model on the desired trajectory. This, in turn, reduces the load on the adaptive feedback, enabling it to use a lower learning rate. Combined this creates a controller with variable feedback and low gains, and a feedforward model that can account for larger modeling errors. We demonstrate the effectiveness of this framework, in simulation and on a real system.

Achieving real-time perception is critical to developing a fully autonomous system that can sense, navigate, and interact with its environment. Perception tasks such as online 3D reconstruction and mapping have been intensely studied for terrestrial robotics applications. However, characteristics of the underwater domain such as light attenuation and light scattering violate the brightness constancy constraint, which is an underlying assumption in methods developed for land-based applications. Furthermore, the complex nature of light propagation underwater limits or even prevents subsea use of real-time depth sensors used in state-of-the-art terrestrial mapping techniques. There have been recent advances in the development of plenoptic (also known as light field) cameras, which use an array of micro lenses capturing both intensity and ray direction to enable color and depth measurement from a single passive sensor. This paper presents an end-to-end system to harness these cameras to produce real-time 3D reconstructions underwater. Our system builds upon the state-of-the-art in online terrestrial 3D reconstruction, transferring these approaches to the underwater domain by gathering real-time color and depth (RGB-D) data underwater using a plenoptic camera, and performing dense 3D reconstruction while compensating for attenuation effects of the underwater environment simultaneously, using a graphics processing unit (GPU) to achieve real-time performance. Results are presented for data gathered in a water tank and the proposed technique is validated quantitatively through comparison with a ground truth 3D model gathered in air to demonstrate that the proposed approach can generate accurate 3D models of objects underwater in real-time.

Autonomous platforms are confronted by a diversity of challenges in unstructured environments, which make monitoring performance a non-trivial task. Some of these environments are so complex that they preclude persistent, nearby operator oversight. This absence of oversight motivates the need for an online monitoring system, specifically for robots operating in difficult environments. We develop a test methodology and set of online monitoring metrics by extending methods for characterizing robotic-systems using offline metrics. We implement this test methodology in an unstructured, outdoor environment and show the resulting performance information gained from our online monitoring solution. This online monitoring approach is generalizable such that it characterizes any robotic system that meets our set of hardware and software criteria.

In industrial environments, such as metallurgic facilities, human operators are exposed to harsh conditions where ambient air is often polluted with quartz, dust, lead debris and toxic fumes. Constant exposure to respirable particles can cause irreversible health damages and thus it is of high interest for occupational health experts to monitor the air quality on a regular basis. However, current monitoring procedures are carried out sparsely, with data collected in single day campaigns limited to few measurement locations. In this paper we explore the use and present first experimental results of a novel heterogeneous approach that uses a mobile robot and a network of low cost sensing nodes. The proposed system aims to address the spatial and temporal limitations of current monitoring techniques. The mobile robot, along with standard localization and mapping algorithms, allows to produce short term, spatially dense representations of the environment where dust, gas, ambient temperature and airflow information can be modelled. The sensing nodes on the other hand, can collect temporally dense (and usually spatially sparse) information during long periods of time, allowing in this way to register for example, daily variations in the pollution levels. Using data collected with the proposed system in an steel foundry, we show that a heterogeneous approach provides dense spatio-temporal information that can be used to improve the working conditions in industrial facilities.