IROS 2018 论文列表

In multi-robot systems, although the idea of behaviors allows for an efficient solution to low-level tasks, high-level missions can rarely be achieved by the execution of a single behavior. In contrast to this, a sequence of behaviors would provide the requisite expressiveness, but there are no a priori guarantees that the sequence is composable in the sense that the robots can actually execute it. In order to guarantee a provably correct composition of behaviors, Finite-Time Convergence Control Barrier Functions are introduced in this paper to guarantee the terminal configuration of one behavior is a valid initial configuration for the following one. Nominal control inputs prescribed by the behaviors are modified in a minimally invasive fashion, in order to establish the information-exchange network required by the following behavior. The effectiveness of the proposed composition strategy is validated on a team of mobile robots.

The enhanced dexterity and manipulability offered by continuum manipulators makes them the robots of choice for complex procedures inside the human body. However, without tailored analytical tools to evaluate their manipulability, many capabilities of continuum robots such as safe and effective manipulation will remain largely inaccessible. This paper presents a quantifiable measure for analysing force/velocity manipulability of continuum robots. We expand classical measures of manipulability for rigid robots to introduce three types of manipulability indices to continuum robots, namely, velocity, compliance, and unified force-velocity manipulability. We provide a specific case study using the proposed method to analyse the force/velocity manipulability for a concentric-tube robot. We investigate the application of the manipulability measures to compare performance of continuum robots in terms of compliance and force-velocity manipulability. The proposed manipulability measures enable future research on design and optimal path planning for continuum robots.

In this paper, we introduce the achievement of the flight motion to pass through small opening by the multilinked and transformable aerial robot. Previous works about such motion are based on under-actuated multirotors, indicating that aggressive maneuvering is necessary condition. This involves two crucial problems: i) enough free space for deceleration is necessary, otherwise the robot would collide with unknown obstacle after exiting opening; ii) the multirotor can not traverse the openings that are smaller than the robot body. The proposed transformable aerial robot in our work can solve these problems, since the multilinked model can not only guarantee the near-hover condition during the whole motion sequence, but also slowly traverse relative small openings by changing its form like a snake. We first propose an improved dynamics derivation and flight control method for this multilinked aerial robot based on our previous work. Then, we present the path planning method which takes the flight stability in the near-hover condition into account. Finally we demonstrate the experimental results of the motion to pass through a horizontal and small opening which also involves the borders (the floor and the ceiling).

A new flexible fabric actuator realizing three dimensional movements for wearable devices is proposed in this paper. Such actuators must be lightweight and highly flexible, producing movements with high degree of freedom to assist/follow human natural motions. Improving the structure and control system of a fabric actuator from the previous research, the flexible fabric actuator with continuous control is developed. This paper presents new configuration of thin artificial muscles on a flexible rubber swath, a continuous control system to control the fabric actuator smoothly, and control methods to realize six basic movements of human body (forward and backward bends, left and right bends, left and right twists)with less number of muscles. The experiment results indicate the possibility that the proposed fabric actuator woven thin McKibben artificial muscles is a viable technology for use in wearable devices.

The use of model predictive control for quadro-tor applications requires balancing trajectory tracking performance and constraint satisfaction with fast computation. This paper proposes a Flatness-based Model Predictive Control (FMPC) approach that can be applied to quadrotors, and more generally, differentially flat nonlinear systems. Our proposed FMPC couples feedback model predictive control with feedforward linearization. The proposed approach has the computational advantage that, similar to linear model predictive control, it only requires solving a convex quadratic program instead of a nonlinear program. However, unlike linear model predictive control, we still account for the nonlinearity in the model through the use of an inverse term. In simulation, we demonstrate improved robustness over approaches that couple model predictive control with feedback linearization. In experiments using quadrotor vehicles, we also demonstrate improved trajectory tracking compared to classical linear and nonlinear model predictive control approaches.

A Spanish fan is a hand held traditional fan which is used as an accessory and also by Flamenco dancers. The manipulation of the fan is quite difficult as it involves dynamic motion which includes opening, flapping and closing the fan along a pivotal point. The key points include the motion to be quick and the fan to be opened to the maximum degree possible without human intervention. A robotic arm with 7 Degrees of Freedom (DOF) is used to manipulate the autonomous motion. The fan placed on the table is localized and detected using a camera by background subtraction, masking and filtering; post which the contour of the fan is detected. The pixels obtained is then transformed into real life coordinates. The Dynamixel motors then traverses to the coordinates of the fan's position to grasp, open, flap, close and put the fan down.

Human hands can not only grasp objects of various shape and size and manipulate them in hands but also exert such a large gripping force that they can support the body in the situations such as dangling a bar and climbing a ladder. On the other hand, it is difficult for most robot hands to manage both. Therefore in this paper we developed the hand which can grasp various objects and exert large gripping force. To develop such hand, we focused on the thumb CM joint with wide range of motion and the MP joints of four fingers with the DOF of abduction and adduction. Based on the hand with large gripping force and flexibility using machined spring, we applied above mentioned joint mechanism to the hand. The thumb CM joint has wide range of motion because of the combination of three machined springs and MP joints of four fingers have variable rigidity mechanism instead of driving each joint independently in order to move joint in limited space and by limited actuators. Using the developed hand, we achieved the grasping of various objects, supporting a large load and several motions with an arm.

This paper presents the motion planning of a novel aerial robotic system with a long-bar extension and two arms for long-reach manipulation in cluttered environments. The novel aerial long-reach manipulator includes a passive revolute joint between the aerial platform and the dual arm. This feature minimises the torque induced to the aerial system in case of unexpected collisions of the manipulator. The motion planning problem is addressed considering jointly the complete set of configuration variables for the aerial platform and the dual arm. Furthermore, the planner has been built over the fundamentals of RRT* algorithms in order to optimise the performance of the trajectories in terms of energy and time. The proposed planning method has been experimentally validated in a realistic industrial scenario, the transportation of a long bar through a cluttered environment consisting of several pipe structures.

Prescribed fires can lessen wildfire severity and control invasive species, but they can also be risky and costly. Unmanned aerial systems can reduce those drawbacks by, for example, dropping ignition spheres to ignite the most hazardous areas. Existing systems, however, lack awareness of the fire vectors to operate autonomously, safely, and efficiently. In this work we address that limitation, introducing an approach that integrates a lightweight fire simulator and a planner for trajectories and ignition sphere drop waypoints. Both components are unique in that they are amenable to input from the system's sensors and the fire crew to increase fire awareness. We conducted a preliminary study that confirms that such inputs improve the accuracy of the fire simulation to counter the unpredictability of the target environment. The field study of the system showed that the fire-aware planner generated safe trajectories with effective ignitions leveraging the fire simulator predictions.

Robust grasping is a major, and still unsolved, problem in robotics. Information about the 3D shape of an object can be obtained either from prior knowledge (e.g., accurate models of known objects or approximate models of familiar objects) or real-time sensing (e.g., partial point clouds of unknown objects) and can be used to identify good potential grasps. However, due to modeling and sensing inaccuracies, local exploration is often needed to refine such grasps and successfully apply them in the real world. The recently proposed unscented Bayesian optimization technique can make such exploration safer by selecting grasps that are robust to uncertainty in the input space (e.g., inaccuracies in the grasp execution). Extending our previous work on 2D optimization, in this paper we propose a 3D haptic exploration strategy that combines unscented Bayesian optimization with a novel collision penalty heuristic to find safe grasps in a very efficient way: while by augmenting the search-space to 3D we are able to find better grasps, the collision penalty heuristic allows us to do so without increasing the number of exploration steps.

Despite the benefits that the spring based gravity compensation mechanism has brought to the field of rigid robotic manipulators, there have been no substantial efforts toward transferring these developments to the field of flexible link robotics. In this paper, we present an input state feedback linearization controller for the tip positioning of a flexible link arm with a gravity compensation system based on springs. The controller is implemented into a double loop control scheme, in which the inner loop addresses the motor position control in presence of joint friction, and the outer loop deals with vibration cancellation and the tracking of fourth-order trajectories for the tip position of the flexible arm. Taking into considerations the interacting forces between the flexible link and the gravity compensation mechanism, and also the characteristics of the control law, we propose a sensory system to measure all the relevant signals. The proposed controller is tested on an experimental prototype built in our laboratory.

Bipedal locomotion skills are challenging to develop. Control strategies often use local linearization of the dynamics in conjunction with reduced-order abstractions to yield tractable solutions. In these model-based control strategies, the controller is often not fully aware of many details, including torque limits, joint limits, and other non-linearities that are necessarily excluded from the control computations for simplicity. Deep reinforcement learning (DRL) offers a promising model-free approach for controlling bipedal locomotion which can more fully exploit the dynamics. However, current results in the machine learning literature are often based on ad-hoc simulation models that are not based on corresponding hardware. Thus it remains unclear how well DRL will succeed on realizable bipedal robots. In this paper, we demonstrate the effectiveness of DRL using a realistic model of Cassie, a bipedal robot. By formulating a feedback control problem as finding the optimal policy for a Markov Decision Process, we are able to learn robust walking controllers that imitate a reference motion with DRL. Controllers for different walking speeds are learned by imitating simple time-scaled versions of the original reference motion. Controller robustness is demonstrated through several challenging tests, including sensory delay, walking blindly on irregular terrain and unexpected pushes at the pelvis. We also show we can interpolate between individual policies and that robustness can be improved with an interpolated policy.

Robot-assisted therapy is an emerging form of rehabilitation treatment for motor recovery of the upper limbs after neurological injuries such as stroke or spinal cord injury. Robotic rehabilitation devices have the potential to reduce the physical strain put on therapists due to the high-effort one-to-one interactions between the therapist and patient involving repetitive high-intensity movements to restore arm and hand functions. Numerous custom robotic devices have been developed in recent years to aid in physical rehabilitation of stroke patients, but most commercially available systems are high-cost devices because of low production volumes and high development costs. In this paper, we analyse the safety and functionality of the UR5 collaborative industrial robot from universal Robots equipped with an external force/torque sensor in a real-time control system for typical rehabilitation exercises. The aim of the paper is to show that a new class of general-purpose industrial robots designed for human-robot collaboration may prove a viable alternative to custom designs. Experiments show that robotic rehabilitation of the upper limbs using a standard industrial robot manipulator UR5 may be feasible. Results have the potential to make robotic rehabilitation more available as a high-quality therapeutic treatment for more patients.

In this paper, we describe how collision checking for car-like robots can be sped up utilizing system knowledge. Their non-holonomic motion, while being a challenge for motion planning, is utilized here to place discs which are used as an approximation of the robot's shape in a predictive manner. For ease of comparison, we assume the robot to be rectangular, i. e., we use bounding boxes. Our algorithm is compared to a widely-used baseline and shows similar performance in terms of under- and oversampling while being approximately 20-40 % faster. Another feature of the algorithm is its predictive nature: with the frontal disc, we already check for collisions that would occur with the rear disc in the next sample, assuming near-constant curvature. While this might be conservative in some cases where large steering rates are necessary, in our evaluation even tight corridors could be navigated without negative effects.

We propose an efficient and scalable method for incrementally building a dense, semantically annotated 3D map in real-time. The proposed method assigns class probabilities to each region, not each element (e.g., surfel and voxel), of the 3D map which is built up through a robust SLAM framework and incrementally segmented with a geometric-based segmentation method. Differently from all other approaches, our method has a capability of running at over 30Hz while performing all processing components, including SLAM, segmentation, 2D recognition, and updating class probabilities of each segmentation label at every incoming frame, thanks to the high efficiency that characterizes the computationally intensive stages of our framework. By utilizing a specifically designed CNN to improve the frame-wise segmentation result, we can also achieve high accuracy. We validate our method on the NYUv2 dataset by comparing with the state of the art in terms of accuracy and computational efficiency, and by means of an analysis in terms of time and space complexity.

There is a category of biped robots that are equipped with passive or un-actuated ankles, which we call Passive-Ankle Walkers (PAWs). Lack of actuation at ankles is a disadvantage in the fast walking of PAWs. We started this study with an intuitive hypothesis that rhythmic sway of torso may enable faster walking in PAWs. To test this hypothesis, firstly, we optimized the rhythmic sway of torso of a simulated PAW model for fast walking speed, and analyzed the robustness of the optimal trajectories. Then we implemented the optimal trajectories on a real robot. Both the simulation analysis and the experimental results indicated that optimized torso-swaying can greatly increase the walking speed by 40%. By analyzing the walking patterns of the simulated model and the real robot, we identified the reason for the faster walking with swaying-torso: The rhythmic sway of torso enables the robot to walk with a relatively large step-length while still keeninu a hizh sten-frenuencv.

Motion trajectory planning is one crucial aspect for automated vehicles, as it governs the own future behavior in a dynamically changing environment. A good utilization of a vehicle's characteristics requires the consideration of the nonlinear system dynamics within the optimization problem to be solved. In particular, real-time feasibility is essential for automated driving, in order to account for the fast changing surrounding, e.g. for moving objects. The key contributions of this paper are the presentation of a fast optimization algorithm for trajectory planning including the nonlinear system model. Further, a new concurrent operation scheme for two optimization algorithms is derived and investigated. The proposed algorithm operates in the submillisecond range on a standard PC. As an exemplary scenario, the task of driving along a challenging reference course is demonstrated.

In recent years, various shadow detection methods from a single image have been proposed and used in vision systems; however, most of them are not appropriate for the robotic applications due to the expensive time complexity. This paper introduces a fast shadow detection method using a deep learning framework, with a time cost that is appropriate for robotic applications. In our solution, we first obtain a shadow prior map with the help of multi-class support vector machine using statistical features. Then, we use a semantic-aware patch-level Convolutional Neural Network that efficiently trains on shadow examples by combining the original image and the shadow prior map. Experiments on benchmark datasets demonstrate the proposed method significantly decreases the time complexity of shadow detection, by one or two orders of magnitude compared with state-of-the-art methods, without losing accuracy.

In this paper, we present a sampling-based kino-dynamic planning framework for a bipedal robot in complex environments. Unlike other footstep planning algorithms which typically plan footstep locations and the biped dynamics in separate steps, we handle both simultaneously. Three primary advantages of this approach are (1) the ability to differentiate alternate routes while selecting footstep locations based on the temporal duration of the route as determined by the Linear Inverted Pendulum Model (LIPM) dynamics, (2) the ability to perform collision checking through time so that collisions with moving obstacles are prevented without avoiding their entire trajectory, and (3) the ability to specify a minimum forward velocity for the biped. To generate a dynamically consistent description of the walking behavior, we exploit the Phase Space Planner (PSP) [1] [2]. To plan a collision-free route toward the goal, we adapt planning strategies from non-holonomic wheeled robots to gather a sequence of inputs for the PSP. This allows us to efficiently approximate dynamic and kinematic constraints on bipedal motion, to apply a sampling-based planning algorithm such as RRT or RRT*, and to use the Dubin's path [3] as the steering method to connect two points in the configuration space. The results of the algorithm are sent to a Whole Body Controller [1] to generate full body dynamic walking behavior. Our planning algorithm is tested in a 3D physics-based simulation of the humanoid robot Valkyrie.