IROS 2015 论文列表

In order to maintain balance, the motors of an inverted pendulum-style, two-wheeled self-balancing vehicle need to continuously control its wheels. The vehicle is at risk of falling over if any of its motors does not function properly. Some research in this regard has been devoted to gaining redundancy by using more motors geared to a common shaft, but such technique is not effective for a seizing failure. In this paper, we propose a differential redundant mechanism to prevent a seizing failure in such vehicles. This mechanism unifies plural motors using a differential mechanism, and allows redundant motors to operate at different speeds. Therefore, the vehicle can maintain balance even if a motor fails. Furthermore, our mechanism can form a redundant system of two outputs using only three motors. We also propose a controller for this redundant mechanism that does not require changing the control mode when seizing failures occur.

This paper addresses the problem of segregation of groups of heterogeneous units in robot swarms. We propose a controller that can drive robots in a way that each group composed of robots of a similar type will form clusters while maintaining segregation from other groups. The approach is based on abstractions created to represent each group of robots and an artificial potential function used to segregate the groups. Different from previous works on swarm segregation, we can mathematically guarantee that by using our approach the system will always converge to a state where multiple dissimilar groups are segregated. Moreover, in some situations, our controller does not require all robots to have information about all the other robots in the system. We demonstrate the effectiveness of our controller with simulations with different types of robots and varying number of robots and groups.

Since the Tethered Space Robot (TSR) has been a research focus as an application of the space tether, a wide range of problems arise in the different phases of the capture mission. In this paper, we propose a new control scheme for the retrieval of passive space debris after captured by a TSR. Under this control scheme, target can be retrieved rapidly, and both of oscillations of tether and target are converged well. First, we derive the equations of attitude motions for the compound system when the passive target satellite is captured by Tethered Space Robot, where the base satellite (chaser) and the space debris (target) are modeled as rigid bodies and the attachment points of the tether are offset from the centers of mass of the two bodies. Then based on the specifics of equations, we divide the retrieval into two phases, and set a threshold for the retrieval. In different phases, different priorities are presented, and different control schemes are given. Finally, the simulation results are shown to prove that the target satellite could be retrieved rapidly and smoothly in a small oscillation, and the oscillation of tether is totally converged at the end of retrieval.

High-mobility walking robots offer unique capabilities in complex off-road environments where wheeled vehicles are not able to travel. However, these environments can also pose significant autonomous navigation challenges. Key steps in planning a safe path for the robot autonomously include estimating the height of the support ground surface - which is often occluded by vegetation - and classifying the terrain and obstacles above the ground surface. This paper describes the development and experimental evaluation of a terrain classification and ground surface height estimation system to support autonomous navigation for a high-mobility walking robot. We provide experimental evaluation on an extensive, manually-labeled dataset collected from geographically diverse sites over a 28-month period.

This paper describes work on a distributed framework for collaborative multi-robot localisation and mapping with large teams of Micro Aerial Vehicles (MAVs). We demonstrate the benefits of running both image capture and frame-to-frame tracking on the same device while offloading the more computationally intensive aspects of map creation and optimization to an off-board computer. We show no impact on the accuracy of pose estimates of this distributed approach and indeed demonstrate a robustness to delay that improves localisation performance. The bandwidth requirements of our system are much lower than similar systems which enables us to accommodate larger teams of MAVs. In the results section we demonstrate the performance of our system in both simulated and real-world environments.

This paper proposes a method for safe navigation based on representative sample inputs. The representative inputs are chosen in safe input sets based on their distance from forbidden input sets. The inputs are not only the safest decisions with respect to various unmodeled sources of uncertainties, but are also representatives of groups of nearby input sets resulting in similar maneuvers. This approach provides an obstacle avoidance strategy for the maximum control margins. For computational efficiency, a sampling-based approach is adopted, and its performance in terms of solution quality and computation time is analyzed. The algorithm has been successfully demonstrated with an example of a car-like robot in fields with obstacles. For the multiple-step horizon problem, a best-first search algorithm is proposed with guarantee of optimality, and its computational efficiency is demonstrated.

Task learning in robotics requires repeatedly executing the same actions in different states to learn the model of the task. However, in real-world domains, there are usually sequences of actions that, if executed, may produce unrecoverable errors (e.g. breaking an object). Robots should avoid repeating such errors when learning, and thus explore the state space in a more intelligent way. This requires identifying dangerous action effects to avoid including such actions in the generated plans, while at the same time enforcing that the learned models are complete enough for the planner not to fall into dead-ends.

Micro Aerial Vehicles (MAVs) are becoming ubiquitous, but most experiments and demonstrations have been limited to slow flight except in open environments or in laboratories with motion capture systems. In this paper, we develop representations and algorithms for aggressive flight in cluttered environments. We incorporate the specific dynamics of the system and generate safe, feasible trajectories for fast navigation in real time. Specifically, we use a polyhedral decomposition of the visible free space and address the generation of safe trajectories that are within the space. Because of the limited field of view, we adopt a receding horizon control policy (RHCP) for planning over a finite time horizon, but with the guarantee that there exists a safe stopping control policy over a second time horizon from the planned state at the end of the horizon. Thus, the robot planning occurs over two horizons. While the robot executes the planned trajectory over the first time horizon, the map of obstacles is refreshed allowing the planner to generate a refined plan, once again over two time horizons. The key algorithmic contribution of the paper lies in the fast planning algorithm that is able to incorporate robot dynamics while guaranteeing safety. The algorithm is also optimal in the sense that the receding horizon control policy is based on minimizing the trajectory snap. Central to the algorithm is a novel polyhedral representation that allows us to abstract the trajectory planning problem as a problem of finding a path through a sequence of convex regions in configuration space.

Sensing of environment geometry and texture is a key requirement for mobile robotic systems. In the underwater domain, difficult environmental conditions restrict the applicability of many existing methods for 3D sensing. A new method is proposed, which uses a visible laser line projected onto a monocular camera image to perform 3D scene reconstruction. The method fuses Structured Light with Structure from Motion in an integrated process, which allows for the capturing of dense 3D point clouds on moving systems in situations with low texture and minimal scene structure. The system is evaluated in three experiment scenarios and provides an average translation error of 2% in the KITTI Benchmark for monocular visual odometry and an average model error of 1:7mm when compared with a tabletop structured light system.

Inspired by biological systems, compliant elements are introduced in the driving train of legged robots with the purpose of recycling energy. This paper presents the design and control concept of the monopedal robot SPEAR, driven by a novel implementation of a Switchable Parallel Elastic Actuator (S-PEA). At the stance phase, the parallel spring in S-PEA works with the actuator to support body weight and recover energy. The spring is removed from the system at the flight phase to gain precise joint control. The stiffness of the whole leg is also adjustable online either by active control of the motor or by changing the knee angle at touchdown. Experimental results show that the spring recycles part of energy during hopping, reducing the peak torque and peak power of the motor.

3D Object recognition is one of the big problems in Computer Vision which has a direct impact in Robotics. There have been great advances in the last decade thanks to point cloud descriptors. These descriptors do very well at recognizing object instances in a wide variety of situations. Of great interest is also to know how descriptors perform in object classification tasks. With that idea in mind, we introduce a descriptor designed for the representation of object classes. Our descriptor, named SCurV, exploits 3D shape information and is inspired by recent findings from neurophysiology. We compute and incorporate surface curvatures and distributions of local surface point projections that represent flatness, concavity and convexity in a 3D object-centered and view-dependent descriptor. These different sources of information are combined in a novel and simple, yet effective, way of combining different features to improve classification results which can be extended to the combination of any type of descriptor. Our experimental setup compares SCurV with other recent descriptors on a large classification task. Using a large and heterogeneous database of 3D objects, we perform our experiments both on a classical, flat classification task and within a novel framework for hierarchical classification. On both tasks, the SCurV descriptor outperformed all other 3D descriptors tested.

Human running motion can be modeled using a spring-loaded inverted pendulum (SLIP), where the linear-spring-like motion of the standing leg is produced by the joint stiffness of the knee and ankle. To use running speed control in the SLIP model, we should only decide the landing placement of the leg. However, for using running speed control with a multi-joint leg, we should also decide the joint angle and joint stiffness of the standing leg because these affect the direction of the ground reaction force. In this study, we develop a running control method for a human-like multi-joint leg. To achieve a running motion, we developed a running control method including pelvis oscillation control for attaining jumping power with the joint stiffness of the leg and running speed control by changing the landing placement of the leg. For using running speed control, we estimated the ground reaction force using the equation of motion and detected the joint angles of the leg for directing the ground reaction force toward the center of mass. To evaluate the proposed control methods, we compared the estimated ground reaction force with the force measured by the real robot. Moreover, we performed a running experiment with the developed running robot. By using ground reaction force estimation, this robot could accomplish the running motion with pelvic oscillation for attaining jumping power and running speed control.

We present a design for an energetically autonomous artificial organism, combining two subsystems; a bio-inspired energy source and bio-inspired actuation. The work is the first demonstration of energetically autonomy in a microbial fuel cell (MFC)-powered, swimming robot taking energy from it's surrounding, aqueous environment. In contrast to previous work using stacked MFC power sources, the Row-bot employs a single microbial fuel cell as an artificial stomach and uses commercially available voltage step-up hardware to produce usable voltages. The energy generated exceeds the energy requirement to complete the mechanical actuation needed to refuel. Energy production and actuation are demonstrated separately with the results showing that the combination of these subsystems will produce closed-loop energetic autonomy. The work shows a crucial step in the development of autonomous robots capable of long term self-power. Bio-inspiration for the design of the Row-bot was taken from the water boatman beetle. This proof of concept study opens many avenues for the further development of the subsystems comprising the Row-bot, and the functionality of the robot itself.

Incremental Structure from Motion (SfM) algorithms require, in general, precise knowledge of the camera linear and angular velocities in the camera frame for estimating the 3D structure of the scene. Since an accurate measurement of the camera own motion may be a non-trivial task in several robotics applications (for instance when the camera is onboard a UAV), we propose in this paper an active SfM scheme fully independent from the camera angular velocity. This is achieved by considering, as visual features, some rotational invariants obtained from the projection of the perceived 3D points onto a virtual unitary sphere (unified camera model). This feature set is then exploited for designing a rotation-free active SfM algorithm able to optimize online the direction of the camera linear velocity for improving the convergence of the structure estimation task. As case study, we apply our framework to the depth estimation of a set of 3D points and discuss several simulations and experimental results for illustrating the approach.

We present a descriptor estimator for surface-based 3D input data for coarse localization of mobile robots. From the input pointclouds surfaces are reconstructed and simplified to detect stable keypoints which are used to evaluate rotation and translation invariant features. The invariance is achieved by transforming the triangulated input data into the frequency domain by Fourier transformation and spherical harmonics. The pipeline was evaluated against state of the art algorithms and tested to localize a mobile robot. The source code is publicly available.

The Harvard Robobee is a fly-sized aerial vehicle that can perform controlled flight maneuvers. But this robot is unable to control its yaw or heading angle to a desired value. Motivated by this deficiency, we propose a new method to produce yaw-axis rotations. Termed wriggle-steering, it consists of driving body oscillations around its two other rotational axes. Because no torque is applied directly around the controlled axis, it therefore constitutes an alternative control method for under-actuated designs. Oscillations are driven around pitch and roll axes at the same frequency but 90 degrees out of phase, resulting in a small change in yaw angle after each cycle because of nonlinearity in attitude dynamics. We propose two wing kinematics perturbations that produce the necessary actuation. The predictions are validated with a quasi-steady aerodynamics model, free-body simulations, and flight tests on a fly-sized hovering aerial robot. The results suggest that wriggle-steering can save mass and reduce complexity by eliminating the need for additional actuators in flapping-wing robots or other aircraft.

To build conversational robots, roboticists are required to have deep knowledge of both robotics and spoken dialogue systems. Although they can use existing cloud services that were built for other services, e.g., voice search, it will be difficult to share robotics-specific speech corpora obtained as server logs, because they will get buried in non-robotics-related logs. Building a cloud platform especially for the robotics community will benefit not only individual robot developers but also the robotics community since we can share the log corpus collected by it. This is challenging because we need to build a wide variety of functionalities ranging from a stable cloud platform to high-quality multilingual speech recognition and synthesis engines. In this paper, we propose “rospeex,” which is a cloud robotics platform for multilingual spoken dialogues with robots. We analyze the logs we have collected by operating rospeex for more than a year. Our key contribution lies in building a cloud robotics platform and allowing the robotics community to use it without payment or authentication.

This paper presents the results of a study on the effect of in-series compliance on the locomotion of a simulated 8-DoF Lola-OP™ Modular Snake Robot with added compliant elements. We explore whether there is an optimal stiffness for gait, terrain type, or several gaits and several terrains (i.e. a good “general-purpose” stiffness). Compliance was simulated using ball joints with eight different levels of stiffness. Two snake locomotion gaits (rolling and sidewinding) were tested over flat ground and three different types of rough terrains. We performed grid search and Particle Swarm Optimization to identify the locomotion parameters leading to fast locomotion and analyzed the best candidates in terms of locomotion speed and energy efficiency (cost of transport). Contrary to our expectations, we did not observe a clear trend that would favor the use of compliant elements over rigid structures. For sidewinding, compliant and stiff elements lead to comparable performances. For rolling gait, the general rule seems to be “the stiffer, the better”.

Large lighting variation challenges all visual odometry methods, even with RGB-D cameras. Here we propose a line segment-based RGB-D indoor odometry algorithm robust to lighting variation. We know line segments are abundant indoors and less sensitive to lighting change than point features. However, depth data are often noisy, corrupted or even missing for line segments which are often found on object boundaries where significant depth discontinuities occur. Our algorithm samples depth data along line segments, and uses a random sample consensus approach to identify correct depth and estimate 3D line segments. We analyze 3D line segment uncertainties and estimate camera motion by minimizing the Mahalanobis distance. In experiments we compare our method with two state-of-the-art methods including a keypoint-based approach and a dense visual odometry algorithm, under both constant and varying lighting. Our method demonstrates superior robustness to lighting change by outperforming the competing methods on 6 out of 8 long indoor sequences under varying lighting. Meanwhile our method also achieves improved accuracy even under constant lighting when tested using public data.