IROS 2016 论文列表

This paper proposes a novel algorithm to control wheels of a rover in rough terrains with keeping traction and minimizing energy consumption on the basis of observing a change of a robot velocity from a 4WD Kinematic model. Proposed kinematic-based rough terrain control (KRTC) can satisfy a rover to meet an integrated performance of following a desired velocity while keeping maximum traction and minimizing energy use without an estimator of wheel sinkage and an observer of a dynamic parameter. KRTC consists of two main parts; a slip optimizer and a slip controller. For slip optimization, optimal slip values regarding maximum traction and tractive efficiency are derived from indoor experimental data during a wheel-terrain interaction. Optimized slip ratio is used as an input of a PID slip controller. KRTC only uses the desired velocity of the robot as an input value and gets the two feedback data of actual slip and actual velocity of each wheel. For verification, KRTC was applied to a 4WD rover and the performance was analyzed based on acquired experimental data on a variety of rough terrains.

In this paper, an hybrid position-position and position-velocity teleoperation control scheme for a generic mobile manipulator is presented and discussed. The mobile manipulator is composed by a mobile platform and a 5 dof arm, and the proposed control scheme allows the simultaneous control of both the devices by means of a single haptic device characterized by an open kinematic chain and not specifically designed for mobile manipulators teleoperation (e.g. a Phantom Omni). The proposed teleoperation controller overcomes the mismatch of the control signals to be sent to the arm (position) and to the mobile platform (velocity) through a proper partition of the master device workspace. Tests have been performed both by simulation and with a real setup. The setup is composed by a 6 dof Phantom Omni haptic device acting as master, and a single-arm Kuka youBot omnidirectional manipulator acting as slave. Experimental results related to a pick and place task, performed on the real setup and involving the motion of both the arm and the platform are reported and commented.

Graffiti work using a humanoid with artistic technique can convey the value of artists' work to people. Previous work that focused on drawing an image on a canvas accurately has not contained artistic processes like performance, drawing skills and other artists' intents at the time of creation. To combine such artistic processes, the work in this paper utilizes whole-body motion of a humanoid to paint an image on a wall using Pointillism. However, a biped humanoid consists of high Degree Of Freedom (DOF) system and is very sensitive to internal and external disturbances from interaction with environments. As is the case when graffitiing on a wall. Most notably, the vibration from impact contacts and mechanical uncertainties limit the humanoid in graffitiing properly. This paper presents an approach to realize drawing a large image on a wall through real-time motion planning for printing, artificial compliance, and a disturbance controller.

This paper presents a simulation-based safety training simulator for robot assisted surgery. While adverse events occur rarely during training, they could be fatal to the patients if they happen during real surgical procedures and are not handled properly by the surgical team. In this work we propose a hardware-in-the-loop robotic surgery simulator with high fidelity of the robot motion in a simulated environment, which is capable of reproducing adverse events during surgery. The proposed simulator is built upon the Raven-II open source surgical robot, integrated with a simulated surgeon console and a safety hazard injection engine, which automatically injects faults into modules of the robot control software. We simulate representative safety hazards seen in the adverse events, related to da Vinci™ robot, reported to the FDA MAUDE database. A novel haptic feedback strategy is provided to the operator when the underlying dynamics differ from the real robot states.

Solving the direct kinematics (DK) of parallel robots, i.e. finding all the possible poses of the platform for given input values, is most of the time a difficult problem. This is evidently true for cable-driven parallel robots (CDPR) that are more complex than classical parallel robot because of the unilateral nature of the cable actions but also if cable deformations are taken into account. Furthermore there are many different deformable cable models and developing a DK algorithm for each of them will be a tremendous task. Consequently using a numerical continuation scheme that starts from the DK solutions for non-deformable cables and then moves toward the solution for deformable cables appears to be an interesting approach. To use this scheme we have to assume that the cable model relies on physical parameters that have limit values for which the deformable cable acts like a non-deformable one. Under that assumption we are then able to derive a possible maximum of solutions for the DK, whatever is the cable model. We then apply this approach for a specific complex cable model, the catenary case, to show that this approach is computer efficient but requires to address difficult theoretical issues in order to obtain a solving algorithm that is guaranteed to determine all DK solutions.

Robots are being increasingly used in safety-critical contexts, such as transportation and health. The need for flexible behavior in these contexts, due to human interaction factors or unstructured operating environments, led to a transition from hardware- to software-based safety mechanisms in robotic systems, whose reliability and quality is imperative to guarantee. Source code static analysis is a key component in formal software verification. It consists on inspecting code, often using automated tools, to determine a set of relevant properties that are known to influence the occurrence of defects in the final product. This paper presents HAROS, a generic, plug-in-driven, framework to evaluate code quality, through static analysis, in the context of the Robot Operating System (ROS), one of the most widely used robotic middleware. This tool (equipped with plug-ins for computing metrics and conformance to coding standards) was applied to several publicly available ROS repositories, whose results are also reported in the paper, thus providing a first overview of the internal quality of the software being developed in this community.

Haptically enabled hands-on or tele-operated surgical robotic systems provide a unique opportunity to integrate pre- and intra-operative information into physical actions through active constraints (also known as virtual fixtures). In many surgical procedures, including cardiac interventions, where physiological motion complicates tissue manipulation, dynamic active constraints can improve the performance of the intervention in terms of safety and accuracy. The non-energy-storing class of dynamic guidance constraints attempt to assist the clinician in following a reference path, while guaranteeing that the control system will not generate undesired motion due to stored potential energy. An important aspect that has not received much attention from the researchers is that while these methods help increase the performance, they should by no means distract the user systematically. In this paper, a viscosity-based dynamic guidance constraint is introduced that continuously redirects the tool's motion towards the reference path. The proportionality and continuity of generated forces make the method less distracting and subjectively appealing. The performance is validated and compared with two existing non-energy-storing methods through extensive experimentation.

Flying robots are increasingly adopted in search and rescue missions because of their capability to quickly collect and stream information from remote and dangerous areas. To further enhance their use, we are investigating the development of a new class of drones, foldable sensorized hubs that can quickly take off from rescuers' hands as soon as they are taken out of a pocket or a backpack. With this aim, this paper presents the development of a foldable wing inspired by insects. The wing can be packaged for transportation or deployed for flight in half a second with a simple action from the user. The wing is manufactured as a thick origami structure with a foldable multi-layer material. The prototype of the foldable wing is experimentally characterized and validated in flight on a mini-drone.

As robot swarms become more viable, efficient solutions to fundamental tasks such as swarm search and collection are required. We propose the distributed deterministic spiral algorithm (DDSA) which generalises a spiral search pattern to robot swarms. While being an effective search strategy in its own right, the DDSA is also a useful point of comparison for other swarm search strategies. Such a benchmark for robot swarm search is currently needed but missing. As a case study, we compare the DDSA to a biologically-inspired central-place foraging algorithm that uses stochastic search, memory, and communication to efficiently collect resources in a variety of different resource distributions.

Weakly electric fish use self-established electric field to sense the underwater environment that may be cluttered and turbid. Previous works on building artificial counterparts are limited to simplest cases, as no analytical solutions exist under complex boundary conditions. Universal numerical approaches like Finite Element Method (FEM) and Boundary Element Method (BEM) suffer from lengthy meshing process and heavily computational burden. In this paper, discrete dipole approximation (DDA), which is widely used in light scattering and absorption problems, was for the first time proposed to be applied for underwater electrosense. This approach is lightweight, flexible and computationally efficient compared with FEM. It was simulated in electric fields excited by parallel-plate electrodes and spherical electrodes of a simplified robotic model. A constrained unscented Kalman filter (CUKF) was further utilized to localize the position and identify the size of an invading cube. Results comparison with FEM indicate the differences of a cuboidal object in two orthogonal positions were 7.10% and 10.46% respectively, and the difference in size was 11.82%. These results were achieved at a cost of less than 1% of the computational effort of the FEM. The proposed approach proved effective from the simulation results and laid a solid foundation for real-time underwater active electrosense in a more general environment.

This paper proposed a phase-closed-loop nanomachining control method to realize the directly control of machining depth based on ultrasonic vibration-assisted AFM. By using applied force to control the machining depth, conventional AFM machining approaches unable to machining a nanostructure with specified machined depth. With the proposed method, the vibration phase of micro-cantilever has a specific relationship with machining depth. Therefore, the nano-grooves with desired depth can be machined by using phase value as feedback of PID control. In this paper, the theoretical analysis and simulation are carried out, and the experiments of phase-closed-loop control method are conducted. The experimental results verify the primary feasibility of the proposed method. The present method also demonstrates the potential on the fabrication of three-dimension nanostructures and nanoelectronic device.

Obstacle avoidance is the core problem for mobile robots. Its objective is to allow mobile robots to explore an unknown environment without colliding into other objects. It is the basis for various tasks, e.g. surveillance and rescue, etc. Previous approaches mainly focused on geometric models (such as constructing local cost-maps) which could be regarded as low-level intelligence without any cognitive process. Recently, deep learning has made great breakthroughs in computer vision, especially for recognition and cognitive tasks. It takes advantage of the hierarchical models inspired by human brain structures. However, it is a fact that deep learning, up till now, has seldom been used for controlling and decision making. Inspired by the advantages of deep learning, we take indoor obstacle avoidance as example to show the effectiveness of a hierarchical structure that fuses a convolutional neural network (CNN) with a decision process. It is a highly compact network structure that takes raw depth images as input, and generates control commands as network output, by which a model-less obstacle avoidance behavior is achieved. We test our approach in real-world indoor environments. The new findings and results are reported at the end of the paper.

Recently, robotics has been seen as a key solution to improve the quality of life of amputees. In order to create smarter robotic prosthetic devices to be used in an everyday context, one must be able to interface them seamlessly with the end-user in an inexpensive, yet reliable way. In this paper, we are looking at guiding a robotic device by detecting gestures through measurement of the electrical activity of muscles captured by surface electromyography (sEMG). Reliable sEMG-based gesture classifiers for end-users are challenging to design, as they must be extremely robust to signal drift, muscle fatigue and small electrode displacement without the need for constant recalibration. In spite of extensive research, sophisticated sEMG classifiers for prostheses guidance are not yet widely used, as systems often fail to solve these issues simultaneously. We propose to address these problems by employing Convolutional Neural Networks. Specifically as a first step, we demonstrate their viability to the problem of gesture recognition for a low-cost, low-sampling rate (200Hz) consumer-grade, 8-channel, dry electrodes sEMG device called Myo armband (Thalmic Labs) on able-bodied subjects. To this effect, we assessed the robustness of this machine learning oriented approach by classifying a combination of 7 hand/wrist gestures with an accuracy of ∼97.9% in real-time, over a period of 6 consecutive days with no recalibration. In addition, we used the classifier (in conjunction with orientation data) to guide a 6DoF robotic arm, using the armband with the same speed and precision as with a joystick. We also show that the classifier is able to generalize to different users by testing it on 18 participants.

The majority of recent researches have been focused on developing compliant joint for rotary motion. Few authors contributed to the problematic of safety in pure linear motion, i.e. prismatic joint. The contribution of this work is to present a new design capable of achieving, passively, a nonlinear elastic behavior for prismatic joint implementation, the so-called Prismatic Compliant Joint (PCJ). This new device is based on a six-bar mechanism equipped with a linear spring. Hence, this structure generates the desired nonlinear stiffness behavior under a specified external force. The elastic characteristic will comply with force safety criteria of physical Human/Robot interaction (pHRI); a Hunt-Crossley model based one. In order to fit the PCJ response curve to the established safety measures, an optimization based on genetic algorithm method tunes PCJ intrinsic parameters subject to the chosen constraints.

Observations of human grasping reveal that the exploitation of environmental constraints is a key structural aspect for the robustness and versatility of human grasping behavior. We analyze 3,400 human grasping trials with 17 subjects grasping 25 objects to show that viewing environmental constraints as the central structural aspect of human grasping yields surprisingly simple representations of human grasping behavior. We present hypothesis-driven experiments that emphasize the centrality of environmental constraints in human grasping and extract from data a simple “grasping plan” that is a generative model for all of the human grasping trials we observed. This grasping plan can in principle be transferred to a robot system in an attempt to leverage environmental constraints to improve the performance of robotic grasping.

Most traditional robotic mechanisms feature inelastic joints that are unable to robustly handle large deformations and off-axis moments. As a result, the applied loads are transferred rigidly throughout the entire structure. The disadvantage of this approach is that the exerted leverage is magnified at each subsequent joint possibly damaging the mechanism. In this paper, we present two lightweight, elastic, bio-inspired tensegrity robotic arms adapted from prior static models which mitigate this danger while improving their mechanism's functionality. Our solutions feature modular tensegrity structures that function similarly to the human elbow and the human shoulder when connected. Like their biological counterparts, the proposed robotic joints are flexible and comply with unanticipated forces. Both proposed structures have multiple passive degrees of freedom and four active degrees of freedom (two from the shoulder and two from the elbow). The structural advantages demonstrated by the joints in these manipulators illustrate a solution to the fundamental issue of elegantly handling off-axis compliance. Additionally, this initial experiment illustrates that moving tensegrity arms must be designed with large reachable and dexterous workspaces in mind, a change from prior tensegrity arms which were only static. These initial experiments should be viewed as an exploration into the design space of active tensegrity structures, particularly those inspired by biological joints and limbs.

The foraging problem, which evolved from ants finding food and delivering them to the nest, is for a swarm of robots to transport objects from a source to a destination. To avoid collision of robots or congestions, object transportation is generally conducted in a pipelining manner. Existing solutions towards the foraging problem either have performance problems or cannot tolerate failures. We present a self-stabilizing solution for the swarm robotic system to form a pipeline structure to transport objects and achieve the foraging task. In this paper, we first define the stable state for the swarm, which includes two requirements: (1) the robots operate in non-overlapping regions, i.e., transport objects in a pipeline structure and (2) the transportation rate of the system is optimal. Then, we introduce our self-stabilizing algorithm for the foraging problem and prove its convergence and the correctness of its convergence properties. Due to the self-stabilization nature, our solution is decentralized and fault tolerant. The swarm can achieve the foraging task as long as there is at least one working robot. Due to the definition of the stable state, our swarm system, when converged, can achieve optimal performance. We conduct simulations to evaluate the effectiveness of our algorithm, and the experimental results show that from any state, our algorithm can converge very quickly to reach the stable state and provide optimal performance for object transportation.

Conventional ultrasonic drills can drill into rocks using high frequency axial vibration, which feature lower power and lower preload force, thus they could become a very attractive solution for future deep-space exploration. However, drill cuttings cannot be removed effectively with the increase of drilling depth by conventional ultrasonic drills that only driven by longitudinal vibration. To address this issue, a Rotary-Percussive Ultrasonic Drill (RPUD) is proposed, which employs one single PZT transducer to generate rotary-percussive motion for rock fracturing. RPUD is composed of a PZT transducer, a percussive unit, a rotary unit, and a drill tool. The percussive unit enlarges the longitudinal vibration of front part of PZT transducer, and provides a reciprocating percussion to the drill tool. The rotary unit transforms longitudinal vibration of rear part of PZT transducer into longitudinal-torsional vibration to drive the drill tool to rotate continuously. The rotary and percussive motions are independent of each other, and can be adjusted separately. To make the rotary and percussive motions move synchronously, finite element method is employed to tune the resonance frequencies of the rotary unit and percussive unit to be close by transient analysis. Experimental results show that RPUD can improve removal efficiency of the drill cuttings due to its rotary motion, compared with conventional ultrasonic drills.

This paper tackles the inverse problem of finding optimal configurations of magnetic gradient sources in Dipole Field Navigation (DFN), a magnetic navigation method proposed recently for the direct targeting of drugs. In DFN, a limited number of these gradient sources, called the cores, must be positioned properly around a patient in a Magnetic Resonance Imaging scanner to induce the required directional forces on the navigated therapeutic carriers. To overcome some limitations of the previous approach for solving this problem, here we propose a novel and conceptually simple multidimensional variant of the well-known Particle Swarm Optimization (PSO) algorithm. This variant, called Progressive Multidimensional PSO (PMD-PSO), enables a tradeoff between the quality and the complexity of the solutions by progressively increasing the number of dimensions in the search space. We apply this algorithm to the core placement problem using an improved fitness function for the evaluation of a core configuration given a vascular path towards a target. Experiments on simulated vasculatures show that, while the approach can effectively solve this inverse problem, PMD-PSO exhibits better performances for DFN compared with two other multidimensional PSO variants.