waashk/dblp huggingface dataset:
a parametric study of flapping wing performance using a robotic flapping wing. flapping wings have the potential to revolutionize the field of micro aerial vehicles (mavs), however the effect of flapping motion on the performance of such wings has not been studied in detail. this paper presents a parametric study of flapping wing propulsion, using two types of passive flapping wings and three flapping motions. each combination of wing type and flapping motion was tested over a range of amplitudes and frequencies ranging from 1.4-36° and 5-50 hz respectively. wing performance was evaluated by measuring lift force and mechanical efficiency for all tests. the performance of wing a was found to be significantly higher than wing b, with up to twice the maximum lift and efficiency. overall, wing a with the triangular flapping motion was found to be the most suitable for mavs. this research has the potential to significantly improve the performance of flapping wing propulsion, resulting in new capabilities and applications for mavs.
real-time correlative scan matching. scan matching, the problem of registering two laser scans in order to determine the relative positions from which the scans were obtained, is one of the most heavily relied-upon tools for mobile robots. current algorithms, in a trade-off for computational performance, employ heuristics in order to quickly compute an answer. of course, these heuristics are imperfect: existing methods can produce poor results, particularly when the prior is weak. the computational power available to modern robots warrants a re-examination of these quality vs. complexity trade-offs. in this paper, we advocate a probabilistically-motivated scan-matching algorithm that produces higher quality and more robust results at the cost of additional computation time. we describe several novel implementations of this approach that achieve real-time performance on modern hardware, including a multiresolution approach for conventional cpus, and a parallel approach for graphics processing units (gpus). we also provide an empirical evaluation of our methods and several contemporary methods, illustrating the benefits of our approach. the robustness of the methods make them especially useful for global loop-closing.
integration of active and passive compliance control for safe human-robot coexistence. in this paper we discuss the integration of active and passive approaches to robotic safety in an overall scheme for real-time manipulator control. the active control approach is based on the use of a supervisory visual system, which detects the presence and position of humans in the vicinity of the robot arm, and generates motion references. the passive control approach uses variable joint impedance which combines with velocity control to guarantee safety in worst-case conditions, i.e. unforeseen impacts. the implementation of these techniques in a 3-dof, variable impedance arm is described, and the effectiveness of their functional integration is demonstrated through experiments.
on efficiency and optimality of asymmetric dynamic bipedal gait. period-doubling bifurcation and chaotic behavior are interesting phenomena in limit cycle walking. their mechanisms are very complicated and their roles in dynamic biped locomotion are still unclear. this paper then investigates how the gait efficiency changes with the asymmetrization through analysis of a simple rimless wheel model. we mathematically show that the symmetric configuration gives the optimal solution when the mean value of the inter-leg angles is constant in terms of kinetic energy. this is derived from two magnitude relations of the energy-loss coefficient and restored mechanical energy. we also show that the symmetric gait is not always optimal from another viewpoint.
impedance control of redundant drive joints with double actuation. this paper proposes impedance control of redundant drive joints with double actuation (rdj-da) to produce compliant motions with the future goal of higher bandwidth. first, to reduce joint inertia, a double-input-single-output mechanism with one internal degree of freedom (dof) is presented as part of the basic structure of the rdj-da. next, the basic structure of rdj-da is further explained and its dynamics and statics are derived. then, the impedance control scheme of rdj-da to produce compliant motions is proposed and the validity of the proposed controller is investigated using numerical examples.
design of a rehabilitation device based on a mechanical link system. realizing an ideal impedance control system in lower extremity rehabilitation systems is challenged by mechanical impedance of robot hardware. although some studies in the field of control systems have been helpful in reducing the mechanical impedance of actuators, they have not been able to remove the inertia of robot hardware. this paper introduces an alternative design in which mechanical links are utilized. the mechanical links are driven by one actuator without any complicated servo systems. the design parameters are optimized for the link system to generate the normal walking motion. the simulation data shows that the normal gait patterns are realized successfully. the device is connected to a patient using elastic components, and therefore the inertia of the robot is not directly imposed on the patient. the patient's legs are guided to follow the motion of the robot with the forces generated by the elastic components.
multi-vehicle path coordination in support of communication. this paper presents a framework for generating time-optimal velocity profiles for a group of path-constrained vehicle robots that have fixed and known initial and goal locations and are required to maintain communication connectivity. each robot must follow a fixed and known path, arrive at its goal as quickly as possible (or at least not increase the time for the last robot to arrive at its goal) and stay in communication with other robots in the arena throughout its journey. the main contribution of this paper is the formulation of the problem as a discrete time nonlinear programming problem (nlp) with constraints on robot kinematics, dynamics, collision avoidance, and communication connectivity. we develop partition elimination constraints that assist in ensuring that the communication network is fully connected (no network partitions). these constraints are enforced only when network partitions would otherwise occur, an approach which significantly reduces the problem size and the required computational effort. in addition, we introduce path-constrained jammer robots with known paths and velocity profiles into the scenario. these jammer robots have an effective jamming range and disrupt all communications within this range. except for the jammers, all robots must remain outside this jamming range at all times. we investigate the scalability of the proposed approach by testing scenarios involving up to fifty (50) robots. solutions demonstrate (i) the trade off between the arrival time and the communication connectivity requirements in scenarios with and without jamming; and (ii) the dependence of computation time on the number of robots.
adaptive bayesian filtering for vibration-based terrain classification. outdoor robots are faced with a variety of terrain types each possessing different characteristics. to ensure a safe traversal a robot has to infer the current ground surface from sensor readings. recent techniques generate a model which predicts the terrain class from single vibration signals disregarding the temporal coherence between consecutive measurements. in this paper, we present a novel approach in which the final classification relies on the analysis of not only one, but several recent observations. therefore, the probabilistic framework of the bayes filter is adopted to the problem of terrain classification. we propose an adaptive approach which automatically adjusts its parameters according to the history of observations. to demonstrate the performance of our method we further describe and compare another technique based on temporal coherence. the evaluation using data collected from our rwi atrv-jr robot shows that our approach is both reactive and stable enough to detect fast terrain transitions and selective misclassifications.
automated 2-d nanoparticle manipulation with an atomic force microscope. atomic force microscope (afm) based nanomanipulation systems are generally slow, not repeatable and imprecise due to a lack of control on the success of limited attempts in the literature. to improve the amount of control, reliability and precision of such systems, this work proposes an automated nanomanipulation method. spherical gold nanoparticles with 100 nm diameter are positioned mechanically on a flat mica substrate by contact manipulation by the afm probe tip to a desired position autonomously. the most significant issue of the manipulation operation is the lack of real-time visual feedback. this issue is solved by developing a robust algorithm for particle center detection and using the afm cantilever deflection (force) signals to detect contact losses in real-time and to repeat the manipulation again until the target location is reached. using these solutions, an automated afm manipulation system is developed and a statistical study is made, where gold nanoparticles are positioned for 50 times to random target positions in different directions and pushing distances. 86% of all the particles could be successfully positioned to the target positions with an accuracy less than 100 nm. unsuccessful positioning operations are due to the particle sticking to either the tip (8%) or the substrate (6%). additionally, performance of the successful manipulations are investigated on 60 manipulation operations in 12 different directions and 5 different distances. the metrics used to quantify performance are the final particle position error and the average manipulation speed.
bayesian surprise and landmark detection. automatic detection of landmarks, usually special places in the environment such as gateways, for topological mapping has proven to be a difficult task. we present the use of bayesian surprise, introduced in computer vision, for landmark detection. further, we provide a novel hierarchical, graphical model for the appearance of a place and use this model to perform surprise-based landmark detection. our scheme is agnostic to the sensor type, and we demonstrate this by implementing a simple laser model for computing surprise. we evaluate our landmark detector using appearance and laser measurements in the context of a topological mapping algorithm, thus demonstrating the practical applicability of the detector.
humanoid robot lola. this paper presents our new 25-dof humanoid walking robot lola. the goal of our research is to realize a fast, human-like walking motion (target speed: 5 km/h). furthermore, we want to increase the robot's autonomous, vision-guided walking capabilities. the robot has 25 degrees of freedom, including 7-dof legs with actively driven toe joints. it is characterized by its lightweight construction, a modular, multi-sensory joint design with brushless motors and an electronics architecture using decentralized joint controllers. special emphasis was put on an improved mass distribution of the leg apparatus to achieve good dynamic performance. the sensor system comprises absolute angular sensors in all links, two custom-made force/torque sensors in the feet and a high-precision inertial sensor in the upper body. trajectory generation and control system aim at faster, more flexible, and more robust walking patterns.
exploiting angular momentum to enhance bipedal center-of-mass control. recent humanoid control investigations have emphasized the importance of controlling whole-body angular momentum throughout a movement task. for typical movement tasks, such as normal walking, such controllers minimize fluctuations in angular momentum about the center of mass (cm). this minimization is consistent with observed behavior of humans for such tasks. however, there are cases where such minimization is not desirable. in this study, we investigate movement tasks where bipedal balance control requires a relaxation of the goal of minimizing whole-body angular moment. we construct a humanoid model having a humanlike mass distribution, and a moment-exploiting control algorithm that modulates whole-body angular momentum to enhance cm control. the model only requires reference trajectories for cm position and torso orientation. joint reference trajectories are not required. while balancing on one leg, we show that the controller is capable of correcting errors in cm state by sacrificing angular postural goals for the swing leg, trunk and head. this prioritization capability provides robustness to significant disturbances, without the need to plan new reference trajectories. we compare the dynamic behavior of our humanoid model to that of human test participants. while standing on one leg, the model, like the human, is shown to reposition its cm just above the stance foot from an initial body state where cm velocity is zero, and the ground cm projection falls outside the foot envelope.
centralized fusion for fast people detection in dense environment. human beings do not have well defined shapes neither well defined behaviors. in dense outdoor environments, they are as a consequence hard to detect and algorithms based on a single sensor tend to produce lot of wrong detections. moreover, many applications require algorithms that work very fast on cpu limited mobile architectures while remaining able to detect, track and classify objects as people with a very high precision. we present an algorithm based on the contribution of a range finder and a vision based algorithm that addresses these three constraints: efficiency, velocity and robustness and that we believe is scalable to a large variety of applications.
an active anti-rollover device based on predictive functional control: application to an all-terrain vehicle. the active devices dedicated to on-road vehicle stability cannot be applied satisfactorily in an off-road context, since the variability and the non-linear features of grip conditions can no longer be neglected. specific solutions have then to be investigated. in this paper, the prevention of light all-terrain vehicle (atv) rollover is addressed. first, a backstepping observer is designed in order to estimate online a rollover indicator accounting for sliding phenomena, from a low-cost perception system. next, the maximum vehicle velocity, compatible with a safe motion over some horizon of prediction, is computed via predictive functional control (pfc), and can then be applied, if needed, to the vehicle actuator to prevent from rollover. the capabilities of the proposed device are demonstrated and discussed thanks to an advanced simulation testbed that has proved to supply results very close to experimental ones.
clothes state recognition using 3d observed data. in this paper, we propose a deformable-model-driven method to recognize the state of hanging clothes using three-dimensional (3d) observed data. for the task to pick up a specific part of the clothes, it is indispensable to obtain the 3d position and posture of the part. in order to robustly obtain such information from 3d observed data of the clothes, we take a deformable-model-driven approach[4], that recognizes the clothes state by comparing the observed data with candidate shapes which are predicted in advance. to carry out this approach despite large shape variation of the clothes, we propose a two-staged method. first, small number of representative 3d shapes are calculated through physical simulations of hanging the clothes. then, after observing clothes, each representative shape is deformed so as to fit the observed 3d data better. the consistency between the adjusted shapes and the observed data is checked to select the correct state. experimental results using actual observations have shown the good prospect of the proposed method.
contextual occupancy maps using gaussian processes. in this paper we introduce a new statistical modeling technique for building occupancy maps. the problem of mapping is addressed as a classification task where the robot's environment is classified into regions of occupancy and unoccupancy. our model provides both a continuous representation of the robot's surroundings and an associated predictive variance. this is obtained by employing a gaussian process as a non-parametric bayesian learning technique to exploit the fact that real-world environments inherently possess structure. this structure introduces a correlation between points on the map which is not accounted for by many common mapping techniques such as occupancy grids. using a trained neural network covariance function to model the highly non-stationary datasets, it is possible to generate accurate representations of large environments at resolutions which suit the desired applications while also providing inferences into occluded regions, between beams, and beyond the range of the sensor, even with relatively few sensor readings. we demonstrate the benefits of our approach in a simulated data set with known ground-truth, and in an outdoor urban environment covering an area of 120,000 m2.
controlling a robotically steered needle in the presence of torsional friction. a flexible needle can be accurately steered by robotically controlling the orientation of the bevel tip as the needle is inserted into tissue. here, we demonstrate the significant effect of friction between the long, flexible needle shaft and the tissue, which can cause a significant discrepancy between the orientation of the needle tip and the orientation of the base where the needle is controlled. our experiments show that several common phantom tissues used in needle steering experiments impart substantial frictional forces to the needle shaft, resulting in a lag of over 45° for a 10 cm insertion depth in some phantoms; clinical studies have reported torques large enough to could cause similar errors during needle insertions. such angle discrepancies will result in poor performance or failure of path planners and image-guided controllers, since the needles used in percutaneous procedures are too small for state-of-the-art imaging to accurately measure the tip angle. to compensate for the angle discrepancy, we develop a model for the rotational dynamics of a needle being continuously inserted into tissue and show how a pd controller is sufficient to compensate for the rotational dynamics.
a high performance 2-dof over-actuated parallel mechanism for ankle rehabilitation. this paper presents the mechanical design of an ankle rehabilitation robotic device based on a 2-dof, redundantly actuated parallel mechanism. the parallel mechanism introduced in this paper has the advantage of mechanical and kinematic simplicity when compared to existing platforms while at the same time it is fully capable of carrying out all the exercises required by ankle rehabilitation protocols. the proposed device makes use of actuation redundancy to eliminate singularity and greatly improve the workspace dexterity. in addition, the requirements for high torque capacity and back-drivability are satisfied with the employment of a custom made cable driven linear electric actuator that combines the high force capacity with excellent back-drivability. the analysis undergoes the optimal design towards the maximization of manipulator workspace, dexterity, torque output and compactness of the device. finally, the performance of the custom linear actuator and the prototype of the rehabilitation device are shown.
periosim: haptic virtual reality simulator for sensorimotor skill acquisition in dentistry. this paper describes a haptic simulator that has been developed as an aid for the sensorimotor skill acquisition in dentistry. an important feature of the simulator is the ability to generate templates of position and force trajectories for the students to follow. furthermore, the simulator has a mechanism, haptic playback, to help the students follow and learn these templates. using this feature, the teacher can perform a procedure in the haptic simulator and record her actions. the trainee is then able to observe the recorded procedure and follow the correct trajectory in the position and force. several haptic playback techniques are reviewed and the procedure implemented in the simulator is described in detail. we also describe hardware and software components of the simulator and their functionality.we conclude by describing the results of a preliminary classroom evaluation of the simulator.
practical visual odometry for car-like vehicles. a method for calculating visual odometry for ground vehicles with car-like kinematic motion constraints similar to ackerman's steering model is presented. by taking advantage of this non-holonomic driving constraint we show a simple and practical solution to the odometry calculation by clever placement of a single camera. the method has been implemented successfully on a large industrial forklift and a toyota prado suv. results from our industrial test site is presented demonstrating the applicability of this method as a replacement for wheel encoder-based odometry for these vehicles.
traversable path identification in unstructured terrains: a markov random walk approach. many terrains in outdoor robot navigation problems have paths that are distinct and continuous compared to the non-traversable regions. in image space these paths correspond to continuous segments that can be thought of as clusters embedded in image feature space. these segments very often translate directly to traversable ground plane. in this paper we build the intuition for semi-supervised methods in path identification and present a markov random walk based approach that requires very few labeled points. the method creates a nearest neighbor graph representation of the current image frame using features deemed suitable for the task and propagates labels based on the concept of absorbing markov chains. we extend this formalism to the task of dynamically identifying traversable and non-traversable regions in the incoming image frames. we present results on actual terrains corresponding to test courses used by the lagr test team. the results demonstrate that with minimal initial supervision the robot can navigate to the goal. we also conduct comparisons of our path labeling technique against other machine learning techniques including non-linear support vector machines on hand labeled data. the results demonstrate that our semi-supervised approach is proficient in the domain of path traversal in unstructured domains.
integration of the control electronics for a mm-sized autonomous microrobot into a single chip. microrobots were proposed more than 20 years ago but it has proven challenging to integrate a power system and actuators into some few mm3. there have been some attempts to create an autonomous mobile microrobot but any has been successful. moreover, the proposed microrobots were simply mobile platforms incapable of sensing its environment and taking decisions. i-swarm has been designed to be a real autonomous microrobot. it is powered by solar cells and provided with a locomotion unit for moving, an ir module for communicating and a contact tip for detecting near objects. those modules are managed by an asic designed specifically for i-swarm. all the electronics (power electronics, buffers, adcs, dacs, control unit, analog transducers and an oscillator) have been embedded in the asic due to the limited area, 3 × 3 mm2. the asic is a complete system on chip (soc) that has several features not reported before in any circuit for microrobots: communicate and act cooperatively with other i-swarm microrobots, detect near objects, measure distance to an object, light trailing and reprogramability. this paper gives some guidelines to design integrated circuits for microrobots.