ICRA 2019 论文列表

This paper presents a path following controller that is suitable for asymmetrical planar robots with significant mass and limited motor torques. the controller is resistant against environmental forces, and inaccurate estimates of robot's inertia, by estimating their effects with unscented kalman filter. the controller outputs wheel torque commands which take in account the motor torque limits and given relative priority of internal control elements. the method presented is thoroughly explained and the simulation results demonstrate the performance of the controller.

The paper presents a passivity-based control law for an antagonistic tendon-driven mechanism. It is proven, by using the passivity theorem, that the proposed control law is able to achieve two properties such as the passivity of interconnected subsystems when the external torque is applied and the global asymptotic stability during free motion when the external force is absent. The proposed controller is simple to be implemented for a complex tendon-driven mechanism because it requires only gravity compensation. In addition, it brings a robustness to the entire control system. And finally, the control strategy can be treated as one of the impedance control schemes so as to achieve the desired performance efficiently.

Robot teleoperation is widely used for several hazardous applications. To increase teleoperator capabilities shared-control methods can be employed. In this paper, we present a passive task-prioritized shared-control method for remote telemanipulation of redundant robots. The proposed method fuses the task-prioritized control architecture with haptic guidance techniques to realize a shared-control framework for teleoperation systems. To preserve the semi-autonomous telerobotic system safety, passivity is analyzed and an energy-tanks passivity-based controller is developed. The proposed theoretical results are validated through experiments involving a real haptic device and a simulated slave robot.

This paper presents a novel robotic manipulation technique for transporting objects on the ground in a passive dynamic, nonprehensile manner. The object is manipulated to rock from side to side repeatedly; in the meantime, the force of gravity enables the object to roll along a zigzag path that is eventually heading forward. We call it rock-and-walk object locomotion. First, we examine the kinematics and dynamics of the rocking motion to understand how the states of the object evolve. We then discuss how to control the robot to connect individual rocking motions into a stable gait of the object. Our rock-and-walk object transportation technique is implemented using a conventional manipulator arm and a simple end-effector, interacting with the object in a nonprehensile manner in favor of the passive dynamics of the object. A set of experiments demonstrates successful object locomotion.

Successfully tracking the human body is an important perceptual challenge for robots that must work around people. Existing methods fall into two broad categories: geometric tracking and direct pose estimation using machine learning. While recent work has shown direct estimation techniques can be quite powerful, geometric tracking methods using point clouds can provide a very high level of 3D accuracy which is necessary for many robotic applications. However these approaches can have difficulty in clutter when large portions of the subject are occluded. To overcome this limitation, we propose a solution based on fully convolutional neural networks (FCN). We develop an optimized Fast-FCN network architecture for our application which allows us to filter observed point clouds and improve tracking accuracy while maintaining interactive frame rates. We also show that this model can be trained with a limited number of examples and almost no manual labelling by using an existing geometric tracker and data augmentation to automatically generate segmentation maps. We demonstrate the accuracy of our full system by comparing it against an existing geometric tracker, and show significant improvement in these challenging scenarios.

Parity-Based methodologies for fault diagnosis in UAVs often result in nonlinear residual generators. Still, a systematic framework to perform detectability and robustness analyses of residual generators does not exist. In this work, detectability and robustness metrics for static and dynamic residuals are presented, while numerical methods, specifically Particle Swarm Optimization, are employed to calculate them. The results are used to characterize the performance of a fault detection system. An application on a UAV model is shown, based on real flight data.

The pavement cleaning is essential to maintain urban hygiene and keep the long stretch of pavements spick and span. This paper reports on the development of novel reconfigurable pavement cleaning robot named Panthera. Reconfiguration in Panthera is gained by the expansion and contraction of the body frame using a single lead screw shaft and linkages mechanism. It gives the capability to reshape itself based on factors like pavement width and pedestrian density. The independent steering action is derived using two in-wheels motors for each steering axis. This imparts the flexibility in motion and make system omnidirectional and allows the convenient movement of the robot in any direction along the pavement. It is powered using onboard batteries that generate lesser noise compared to the existing solution powered with gasoline. The modeling and steering kinematics is presented along with experimental results of the path followed and discussion supporting the robot's capability.

Pose estimation is one of the most important problems in computer vision. It can be divided in two different categories - absolute and relative - and may involve two different types of camera models: central and non-central. State-of-the-art methods have been designed to solve separately these problems. This paper presents a unified framework that is able to solve any pose problem by alternating optimization techniques between two set of parameters, rotation and translation. In order to make this possible, it is necessary to define an objective function that captures the problem at hand. Since the objective function will depend on the rotation and translation it is not possible to solve it as a simple minimization problem. Hence the use of Alternating Minimization methods, in which the function will be alternatively minimized with respect to the rotation and the translation. We show how to use our framework in three distinct pose problems. Our methods are then benchmarked with both synthetic and real data, showing their better balance between computational time and accuracy.

Accurate positioning of cells is a fundamental task for many procedures in assisted reproductive technologies (e.g. intracytoplasmic sperm injection or preimplantation genetic diagnosis) to extract or insert materials into and from the cell without causing damage to it. The current method of manual manipulation is based on a trial and error procedure performed by skilled embryologists, where they use two different micropipettes to rotate the cell and immobilize it in the desired orientation. This procedure is time consuming, inconsistent and has a low efficiency. Attempts to automate the process presented in the literature have not yet been implemented in IVF clinics because their high degree of automation requires extensive changes to the current systems used in the clinics. We designed a system that can easily be integrated into standard equipment of IVF clinics and allows automated as well as manual manipulation of cells. The system uses vibrations induced by a surface transducer at the pipette holder to rotate the cell around the pipette tip axis, resulting in 2D motion. To detect if the polar body is in the desired position after a vibration burst, we developed a polar body detection algorithm. We performed simulations and experiments to confirm that vibrations at the natural frequencies of the system cause rotation around the pipette tip axis. Experimental results show that the system is capable of positioning the polar body in plane in less than 5.41 seconds.

Plane detection in 3D point clouds is a crucial pre-processing step for applications such as point cloud segmentation, semantic mapping and SLAM. In contrast to many recent plane detection methods that are only applicable on organized point clouds, our work is targeted to unorganized point clouds that do not permit a 2D parametrization. We compare three methods for detecting planes in point clouds efficiently. One is a novel method proposed in this paper that generates plane hypotheses by sampling from a set of points with estimated normals. We named this method Oriented Point Sampling (OPS) to contrast with more conventional techniques that require the sampling of three unoriented points to generate plane hypotheses. We also implemented an efficient plane detection method based on local sampling of three unoriented points and compared it with OPS and the 3D-KHT algorithm, which is based on octrees, on the detection of planes on 10,000 point clouds from the SUN RGB-D dataset.

In this work, a hybrid control scheme is presented in order to address the navigation problem for a planar robotic platform of arbitrary shape that is moving inside an obstacle cluttered workspace. Given an initial and desired robot configuration, we propose a methodology based on approximate configuration space decomposition techniques that makes use of heuristics to adaptively refine a partition of the configuration space into non-overlapping, adjacent slices. Furthermore, we employ appropriate workspace transformations and adaptive potential field based control laws that integrate elegantly with the type of configuration space representation used, in order to safely navigate within a given cell and successfully cross over to the next, for almost all initial configurations, until the desired configuration is reached. Finally, we present simulation results that demonstrate the efficacy of the proposed control scheme.

Mobility-on-demand (MoD) systems are revolutionizing urban transit with the introduction of ride-sharing. Such systems have the potential to reduce vehicle congestion and improve accessibility of a city's transportation infrastructure. Recently developed algorithms can compute routes for vehicles in real-time for a city-scale volume of requests while allowing vehicles to carry multiple passengers at the same time. However, these algorithms focus on optimizing the performance for a given fleet of vehicles and do not tell us how many vehicles are needed to service all the requests. In this paper, we present an offline method to optimize the vehicle distributions and fleet sizes on historical demand data for MoD systems that allow passengers to share vehicles. We present an algorithm to determine how many vehicles are needed, where they should be initialized, and how they should be routed to service all the travel demand for a given period of time. Evaluation using 23,529,740 historical taxi requests from one month in Manhattan shows that on average 2864 four passenger vehicles are needed to service all of the taxi demand in a day with an average added travel delay of 2.8 mins.

This paper presents a novel methodology for implementing optimized jumping behavior on quadruped robots. Our method includes efficient trajectory optimization, precise high-frequency tracking controller and robust landing controller for stabilizing the robot body position and orientation after impact. Experimental validation was successfully conducted on the MIT Cheetah 3, enabling the robot to repeatably jump onto and jump down from a desk with the height of 30" (0.76 m). The result demonstrates the advantages of the approach as well as the capability of the robot hardware itself.

Accurate environment representation is one of the key challenges in autonomous ground vehicle navigation in unstructured environments. We propose a real-time optimization-based approach to terrain modeling and path planning in off-road and rough environments. Our method uses an irregular, hierarchical, graph-like environment model. A space-dividing tree is used to define a compact data structure capturing vertex positions and establishing connectivity. The same unique underlying data structure is used for both terrain modeling and path planning without memory reallocation. Local plans are generated by graph search algorithms and are continuously regenerated for on-the-fly obstacle avoidance inside the scope of the local terrain map. We show that implementing a hierarchical model over a regular space division reduces graph edge expansions by up to 84%. We illustrate the applicability of the method through experiments with an unmanned ground vehicle in both structured and unstructured environments.

The coexistence of humans and robots in the future production plants is one of the pillars of Industry 4.0. Humans and robots will collaborate to accomplish common tasks in order to mutually compensate their deficiencies. In recent years, many efforts have been spent to develop safe motion planning strategies, designed to prevent robots from injuring humans. Most of the previous techniques are classifiable as reactive, since the considered motion controllers impose some local corrective actions in order to dodge the space occupied by the human. In this paper, a proactive approach is adopted, optimizing robotic paths according to a prediction of the volume occupied by the human when collaborating with the robot. The validity of the approach is shown in a realistic use-case involving the collaboration of a human operator with a 7 degrees robotic arm, the ABB YuMi.

This paper presents an abstraction-refinement based framework for optimal controller synthesis of discrete-time systems with respect to ω-regular objectives. It first abstracts the discrete-time “concrete” system into a finite weighted transition system using a finite partition of the state-space. Then, a two-player mean payoff parity game is solved on the product of the abstract system and the Büchi automaton corresponding to the ω-regular objective, to obtain an optimal “abstract” controller that satisfies the ω-regular objective. The abstract controller is guaranteed to be implementable in the concrete discrete-time system, with a sub-optimal cost. The abstraction is refined with finer partitions to reduce the suboptimality. In contrast to existing formal controller synthesis algorithms based on abstractions, this technique provides an upper bound on the trajectory cost when implementing the suboptimal controller. A robot surveillance scenario is presented to illustrate the feasibility of the approach.

An optimal leg sequence selection method is developed, which maximizes hexapod robot stability, considering feasible gaits, motion modes, and terrain slope. A novel and fast search method is employed to find the most stable leg sequence for a given gait; if no such sequence exists, the next fastest stable gait is chosen and the most stable leg sequence for this gait is selected. The method can be based on any stability measure; here the Force-Angle Stability Margin criterion is employed that is sensitive to top-heaviness, and inertial and external forces. Results show that the developed method senses instabilities accurately and selects the best leg sequence for maximum stability far faster than exhaustive searches, offering distinct advantages when varied external forces are applied.

This paper evaluates the feasibility of a novel optical sensing concept to measure forces applied at the tip of daVinci EndoWrist instruments. An optical slit is clamped onto the instrument shaft, in-line with an infrared LED-bicell pair. Deflection of the shaft moves the slit with respect to the LED-bicell pair and modulates the light incident on each active element of the bicell. The differential photocurrent is conditioned and monitored to estimate the tip forces. The feasibility evaluation consists of a flexible beam model to quantify the required sensor performance, experimental results with a 3D printed prototype and estimation of the sensor limitations including the measurement bandwidth due to the structural dynamics. The proposed approach requires no modifications to the instrument, is adaptable to different instruments and robot platforms, and leads to high-resolution, high-dynamic range sensing without hysteresis.

OpenRoACH is a 15-cm 200-gram self-contained hexapedal robot with an onboard single-board computer. To our knowledge, it is the smallest legged robot with the capability of running the Robot Operating System (ROS) onboard. The robot is fully open sourced, uses accessible materials and off-the-shelf electronic components, can be fabricated with benchtop fast-prototyping machines such as a laser cutter and a 3D printer, and can be assembled by one person within two hours. Its sensory capacity has been tested with gyroscopes, accelerometers, Beacon sensors, color vision sensors, linescan sensors and cameras. It is low-cost within $150 including structure materials, motors, electronics, and a battery. The capabilities of OpenRoACH are demonstrated with multi-surface walking and running, 24-hour continuous walking burn-ins, carrying 200-gram dynamic payloads and 800-gram static payloads, and ROS control of steering based on camera feedback. Information and files related to mechanical design, fabrication, assembly, electronics, and control algorithms are all publicly available on https://wiki.eecs.berkeley.edu/biomimetics/Main/OpenRoACH.