IROS 2015 论文列表

This paper presents a novel ontology-based approach that uses Description Logics as a knowledge representation framework for the description, aggregation, propagation, and interlinkage of features pertaining to robots and robot-centric workplaces. We show how different classification systems for capabilities and components can be axiomatically linked together, how features can be propagated along compound components, and how complex features can be computed on the basis of combining role inclusion, role composition, and general concept inclusion axioms. In a second use case that is related to the logical deduction of potential hazards for a given workplace configuration, we show that the presented approach is applicable to similar modeling problems.

In this work we explore the effectiveness of a three-fingered robotic gripper in accurately expressing directional instructions (move up, down, left, right) as gestures emulating human hand gestures. Such gestures can be necessary in noisy manufacturing environments where verbal communication is ineffective. Three studies are conducted. In Study 1 we explore hand configurations that human dyads use for nonverbal instruction (n = 17). In Study 2 we examine which hand-configurations from Study 1 are most accurately understood by observers (n = 140). In Study 3 we compare performance between a robot arm performing similar motions to those of human study participants using either an unposed or posed three-fingered robotic gripper (n =100) to observe the importance of the hand's pose. Recognition rates of directional gestures for both the human and the robot are examined. Results indicate that most gestures are better and more confidently recognized when displayed with the posed robot hand.

In this paper, we resolve the issue of stepping over a large obstacle by exploiting the redundancy of pelvis rotation and the versatility of foot trajectories for the humanoids. The control framework consists of a motion pattern that exploits the redundancy of pelvis rotation to enlarge the kinematic workspace, a generic foot trajectory generation which can be modified by a parametric interface to adapt to a specific task as well as utilizing the hip abduction to avoid obstacle collision. Moreover, the compensation strategies are also presented for reducing the discrepancies to implement the dynamic stepping motion on a real robot. The effectiveness is validated by COMAN's capability of dynamically stepping over a large obstacle of 10cm height by 5cm width which is almost 20% of its leg length in both simulation and experiment.

We propose an automatic, targetless, data-driven, extrinsic calibration method to calibrate push-broom 2D lidars with a multi-camera system. The calibration problem is decoupled into alternating optimisers over two hierarchical levels, where both levels are linked with a penalty term. The lower-level optimises the six degrees-of-freedom (DoF) rigid-body transforms between the lidar and each camera of the multi-camera unit by minimising the Normalised Information Distance between intensity measurements obtained from both sensor modalities. The upper-level minimises a nonlinear least squares error between the lower-level solutions. We describe the theory, implement the method, and provide a detailed performance analysis with experiments on real-world data.

The complete collection of resources from a predefined search area is a challenging task for autonomous robot swarms. Because naturally-occurring resources are likely to be distributed in clusters, foraging robot swarms can identify and exploit these resource clusters to improve collection efficiency. We describe an ant-inspired robot swarm foraging system that searches for and collects resources from a variety of distributions, and a cluster prediction and exploitation algorithm that augments swarm foraging by directing robots to residual resources. By characterizing the cumulative resource collection time for a robot swarm foraging in a variety of clustered resource distributions, we can identify the relationship between the “clusteredness” of the distribution and the change in the resource collection rate over time. Experiments show that collection efficiency is most significantly increased when robots switch from ant-inspired foraging to focused exploitation of clusters after approximately 90% of resources have been collected. Not surprisingly, clustering algorithms are most effective when resources are highly clustered in the environment. This work demonstrates the feasibility of efficient, complete resource collection using simple, range-limited robot swarms programmed with ant-inspired foraging behaviors.

As robots are expected to become more and more available in everyday environments, interaction with humans is assuming a central role. Robots working in populated environments are thus expected to demonstrate socially acceptable behaviors and to follow social norms. However, most of the recent works in this field do not address the problem of explicit representation of the social norms and their integration in the reasoning and the execution components of a cognitive robot. In this paper, we address the design of robotic systems that support some social behavior by implementing social norms. We present a framework for planning and execution of social plans, in which social norms are described in a domain and language independent form. A full implementation of the proposed framework is described and tested in a realistic scenario with non-expert and non-recruited users.

Bipedal walking with humanoid robots requires efficient real-time control. Nowadays, most bipedal robots require to ensure local stability at every instant in time, preventing them from achieving the impressive human walking skills. At the same time, bio-inspired walking controllers are emerging, though they are still mostly explored in simulation studies. However, porting these controllers to real hardware is needed to validate their use on real robots, as well as adapting them to face the world non-idealities. Here, we implemented one of them on a real humanoid robot, namely the COMAN, by conducting dynamic walking experiments. More precisely, we used a muscle-reflex model producing efficient and humanlike gaits. Starting from an off-line optimization performed in simulation, we present the controller implementation, focussing on the additional steps required to port it to real hardware. In our experimental results, we highlight some discrepancies between simulation and reality, together with possible controller extensions to fix them. Despite these differences, the real robot still managed to perform dynamic walking. On top of that, its gait exhibited stretched legs and foot roll at some points of the gait, two human walking features hard to achieve with most robot gaits. We present this on a 50 steps walk where the robot was free to move in the sagittal plane while lateral balance was provided by a human operator.

This paper describes the implementation of a multi-vehicle supervisor to prevent collisions at intersections. The experiments are performed on an intersection testbed consisting of three RC cars. Here, we account for uncertainty in car dynamics and state measurement, and the presence of an uncontrolled car, which is human-driven. The supervisor overrides the controlled cars only when necessary to avoid a possible future collision. From the experiments, we demonstrate that intersection collisions are averted, that is, the cars continuously and safely run on the paths without stopping 92.8% of the times.

Kinematically complex robots such as legged robots provide a large degree of mobility and flexibility, but demand a sophisticated motion control, which has more tunable parameters than a general planning and decision layer should take into consideration. A lot of parameterizations exist which produce locomotion behaviors that fulfill the desired action but with varying performance, e.g., stability or efficiency. In addition, the performance of a locomotion behavior at any given time is highly depending on the current environmental context. Consequently, a complex mapping is required that closes the gap between robot-independent actions and robot-specific control parameters considering the environmental context and a given prioritization of performance indices.

Large-scale SLAM demands for scalable techniques in which the computational burden and the memory consumption is shared among many processing units. While recent literature offers competitive approaches for scalable mapping, these usually involve approximations to preserve sparsity of the resulting subproblems. We present an approach to scalable SLAM that is exactly sparse. The main insight is that rather than eliminating variables (which induces dense cliques), we split the separators connecting subgraphs. Then, we enforce consistency of the separators in different subgraphs using hard constraints. The resulting constrained optimization problem can be solved in a decentralized manner using the multi-block Alternating Direction Method of Multipliers (ADMM). Our framework is appealing since (i) it preserves the sparsity structure of the original problem, (ii) it has a straightforward implementation, (iii) it allows to easily trade-off between computation time and accuracy. While our approach is currently slower than competitors, it is more accurate than other memory efficient alternatives. Moreover, we believe that the proposed framework can be of interest on its own as it draws connections with recent literature on decentralized optimization.

We propose a novel method to calculate scalar version of the grasp quality measure proposed, which is the magnitude of minimal disturbance force applied to the object that breaks the grasp. This measurement is adopted in several works but all of them are essentially calculated by enumerating over discretized directions of forces. Our proposed method does not need to discretize the direction, rendering the method more complete. The key idea of our method is that the measure is formulated as a function which depends on object vertices, disturbance force direction and hyperplanes of Unit grasp wrench space(UGWS). We proved that the reciprocal of this function is indeed concave over disturbance force direction, for a fixed object and fixed hyperplane of UGWS. Hence, we can derive analytical solution that find the global minimum of this function. A numerical example was carried out and the result shows that our proposed method can outperform the original computation in term of accuracy and efficiency.

In this paper we propose event-based signaling for large-scale artificial robotic skin to reduce bandwidth requirements on data transmission and processing power. We use the send-on-delta principle to trigger the event generation only when tactile sensors are stimulated and transduce novel information. To compare the standard non-event based method with the proposed event-based method we present a comprehensive analysis of large-scale artificial skin systems for different test applications. For this purpose we collect data of 260 CellulARSkin cells on an UR-5 arm and calculate the events off-line. We determine the optimal packet size for event-based signaling and we show that the event-based system reduces the data rate with respect to the non-event based system for an unstimulated skin cell network to 16.45% and for a heavily stimulated skin cell network to 47.69%. The obtained results show that the event-based system reduces the data redundancy and the required transmission rates without loosing information.

Animals and robots balance dynamically by placing their feet into proper ground targets. While foot placement controls exist for both fully robotic systems and powered prostheses, none enable the dynamism and reactiveness of able-bodied humans. A control approach was recently developed for an ideal double pendulum dynamical system that places feet into ground targets for a wide range of initial conditions and in the presence of significant locomotion disturbances. While its performance in simulation make it an attractive candidate to control legged robotic systems, it is unclear if the approach can be used on real-world systems. In this paper we transfer and evaluate the approach on robotic hardware. Our results show that the controller can be transferred to robotic systems and allow them to achieve comparable foot placement accuracies to an ideal double pendulum simulation, both when motion is undisturbed, as well as when obstacles are encountered in early, mid, and late swing. These results suggest that the proposed approach is a potential alternative control method for legged robots and powered prostheses, enabling recovery from sudden swing-leg disturbances such as trips and unexpected obstacle encounters. The results also point out the need for additional considerations when tuning the controller in order to generate human-like swing trajectories and durations.

In this paper, we present a haptic guidance policy to steer the user along predefined paths, and we evaluate a predictive approach to compensate actuation delays that humans have when they are guided along a given trajectory via sensory stimuli. The proposed navigation policy exploits the nonholonomic nature of human locomotion in goal directed paths, which leads to a very simple guidance mechanism. The proposed method has been evaluated in a real scenario where seven human subjects were asked to walk along a set of predefined paths, and were guided via vibrotactile cues. Their poses as well as the related distances from the path have been recorded using an accurate optical tracking system. Results revealed that an average error of 0.24 m is achieved by using the proposed haptic policy, and that the predictive approach does not bring significant improvements to the path following problem for what concerns the distance error. On the contrary, the predictive approach achieved a definitely lower activation time of the haptic interfaces.

The purpose of this study is to establish a quantitative method that can evaluate the motor function of human lower limbs in dynamic situations. As an index of motor function, we focus on the mechanical impedance characteristics of the lower limbs. By controlling these characteristics, humans can improve motion stability and adapt their movement to disturbances in the environment. In this paper, we propose a method that can estimate the impedance characteristics of the lower limbs of a human subject applying torque perturbations during a pedaling exercise. To improve the accuracy of the proposed estimation method, the effects of inertia and gravity in the measurement data were considered by representing the lower limbs by a multi-link model. Experiments were performed in two participants to estimate the stiffness and viscosity at different phases of the periodic movement. The distributions of these parameters showed symmetric properties for both legs in a pedaling cycle-a tendency that was observed in both participants. This agreement suggests that our method could extract a component of human motor control strategy that adjust mechanical impedances during pedaling exercises.

We present a behavioral approach for autonomous robotic exploration of marine habitat with collision avoidance given little or no prior information. In our previous work, a vision-based reactive navigation paradigm with a predefined forward direction allowed an underwater robot to avoid unexpected obstacles. In this work, we have now incorporated visual perceptive invariants to guide the navigation as a human diver would do, thus eliminating the need for a predefined trajectory or exact pose estimation, i.e., we are adding behavioral patterns to turn the navigation into an exploration. Our exploration architecture consists of a tracker of regions of interest and, a collision avoidance behavior. During the exploration, the robot's navigation system is guided towards the most relevant region while continuously checking for free space (water) to navigate. The proposed framework successfully combines both behaviors despite their opposite nature (moving towards a region to explore it while avoiding collisions). We have implemented our approach on an environment-friendly robot, which uses fins instead of propellers to allow for a non-invasive and cautious exploration. Results of sea trials performed at different locations and depths demonstrate the feasibility of our approach.

A humanoid robot is underactuated and only relies on contacts with environment to move in the space. The ability to measure contact forces and torques enables then to predict the robot dynamics including balance. In classical cases, a humanoid robot is considered as a multi-body system with rigid limbs and joints and interactions with the environment are modeled as stiff contacts. Forces and torques at contacts are generally estimated with sensors which are expensive and sensitive to calibration errors. However, a robot is not perfectly rigid and contacts may have flexibilities. Therefore, external forces create geometric deformations of the body or its environment. These deformations may modify the robot dynamics and produce unwanted and unbalanced motions. Nonetheless, if we have a model of contact stiffness and are able to reconstruct reliably the geometric deformation, we can reconstruct forces and torques at contact. This study aims at estimating contact forces and torques and to observe the body kinematics of the robot with only an Inertial Measurements Unit (IMU). We show that we are able to reconstruct efficiently the position of the Center of Pressure (CoP) of the robot with only the IMU and proprioceptive data from the robot.

One of the main challenges when using multiple robots to explore unknown environments is the allocation of target points or regions to the individual units. To date, commonly used approaches produce uneven assignment of targets in situations where multiple targets have to be assigned to each robot. This imbalance can lead to increased overall exploration time and poses an interesting task for further investigation. Based on insights from Multi-Robot Routing and Traveling Salesman Problem research, we propose a specific algorithm for target point allocation that has advantageous properties in highly dynamic applications like exploration. The presented pairwise optimization procedure is suitable for application in distributed and challenging settings, not requiring central coordination or all to all communication, making our exploration strategy robust and flexible. We provide a theoretical analysis and statistical evaluations. Comparisons with representative approaches from the literature show that our algorithm is competitive with the best performing centralized approach.

Although many state-of-the-art visual SLAM algorithms use keyframes to help alleviate the computational requirements of performing online bundle adjustment, little consideration is taken for specific keyframe selection. In this work, we propose two entropy based methods which aim to insert keyframes that will directly improve the system's ability to localize. The first approach inserts keyframes based on the cumulative point entropy reduction in the existing map, while the second approach uses the predicted point flow discrepancy to select keyframes which best initializes new features for the camera to track against in the future. We implement the proposed methods within the Multi-Camera Parallel Mapping and Tracking framework, and demonstrate the effectiveness of our methods using ground truth data collected using an indoor positioning system.