waashk/dblp huggingface dataset:
safe robot arm with safe joint mechanism using nonlinear spring system for collision safety. collision safety between humans and robots has drawn much attention since service robots are increasingly being used in human environments. a safe robot arm based on passive compliance can usually provide faster and more reliable responses for dynamic collision than an active one involving sensors and actuators. since both positioning accuracy and collision safety of the robot arm are equally important, a robot arm should have very low stiffness when subjected to a collision force greater than the injury tolerance, but should otherwise maintain very high stiffness. to implement these requirements, a novel safe joint mechanism (sjm-ii) which has much smaller size and lighter weight than the previous model, is proposed in this research. the sjm-ii has the advantage of nonlinear spring which is achieved using only passive mechanical elements such as linear springs and a double-slider mechanism. various analyses and experiments on static and dynamic collisions show that stiffness of the sjm-ii is kept very high against an external torque less than the predetermined threshold torque, but abruptly drops when the input torque exceeds this threshold, thereby guaranteeing positioning accuracy and collision safety. furthermore, a robot arm with two sjm-iis is verified to achieve collision safety in 2d space.
learning sound location from a single microphone. we consider the problem of estimating the incident angle of a sound, using only a single microphone. the ability to perform monaural (single-ear) localization is important to many animals; indeed, monaural cues are also the primary method by which humans decide if a sound comes from the front or back, as well as estimate its elevation. such monaural localization is made possible by the structure of the pinna (outer ear), which modifies sound in a way that is dependent on its incident angle. in this paper, we propose a machine learning approach to monaural localization, using only a single microphone and an "artificial pinna" (that distorts sound in a direction-dependent way). our approach models the typical distribution of natural and artificial sounds, as well as the direction-dependent changes to sounds induced by the pinna. our experimental results also show that the algorithm is able to fairly accurately localize a wide range of sounds, such as human speech, dog barking, waterfall, thunder, and so on. in contrast to microphone arrays, this approach also offers the potential of significantly more compact, as well as lower cost and power, devices for sounds localization.
design and calibration of a microfabricated 6-axis force-torque sensor for microrobotic applications. this work describes the design of a capacitive multi-axis force-torque sensor for the monitoring of forces in the sub-milli-newton and sub-micro-newtonmeter range. this force range makes it a valuable tool for microrobotic applications. the sensor is experimentally investigated and calibrated. this is the first microfabricated 6-axis force-torque sensor that has been successfully developed.
safe motion planning computation for databasing balanced movement of humanoid robots. motion databasing is an important topic in robotics research. humanoid robots have a large number of degrees of freedom and their motions have to satisfy a set of constraints (balance, maximal joint torque velocity and angle values). thus motion planning cannot efficiently be done online. the computation of optimal motions is performed off-line to create databases that transform the problem of large computation time into a problem of large memory space. motion planning can be seen as a semi-infinite programming problem (sip) since it involves a finite number of variables over an infinite set of constraints. most methods solve the sip problem by transforming it into a finite programming one using a discretization over a prescribed grid. we show that this approach is risky because it can lead to motions which may violate one or several constraints. then we introduce our new method for planning safe motions. it uses interval analysis techniques in order to achieve a safe discretization of the constraints. we show how to implement this method and use it with state-of-the-art constrained optimization packages. then, we illustrate its capabilities for planning safe motions dedicated to the hoap-3 humanoid robot.
traction force distribution on omni-directional four wheel independent drive electric vehicle. this paper proposes an optimal traction force distribution for omni-directional four wheel independent steering (4wis) and four wheel independent drive (4wid) vehicle. the proposed force distribution algorithm is aimed to enhance the vehicle stability with minimum cost. the algorithm avoids the use of any feedback information of vehicle motion such as linear velocity as this information is difficult to measure accurately and the price of the measuring equipment is very high. as a result, the implementation cost can be reduced and at the same time avoid improper force distribution due to the inaccurate measured information. moreover, the proposed algorithm does not involve any parameter tuning. it makes the algorithm easy to implement. the proposed algorithm can also be applied to any steering types of 4wid vehicle such as typical two wheel steering (2ws) as 4wis is the general case of any steering configuration. simulation results reveal that the performance of the proposed force distribution is superior to the uniform force distribution which is commonly used in 4wid vehicle.
tendon-based transmission systems for robotic devices: models and control algorithms. tendon-based transmission systems present many positive aspects and greatly simplify the mechanical design of small robotic devices, such as robotic fingers. on the other hand, they introduce several nonlinear effects that must be properly considered by the control algorithms to achieve a suitable performance level in the regulation of the finger joint torques. in this paper, the model of the tendons-based driving system and of the nonlinear effects arising from the use of sliding paths instead of pulleys for the tendon routing are discussed, and control algorithms aiming at compensating these nonlinearities are presented. both models and control algorithms have been validated by experiments. in particular, in order to gain a better insight on the force distribution along the tendon, an experimental setup for the measurement of the tension in some intermediate points has been developed. after the identification of the tendon characteristics, a suitable control law for the compensation of the nonlinear effects due to the friction acting on the transmission system has been applied. the proposed compensation scheme is based on a sliding-mode controller with boundary layer, where the boundary threshold is modulated as a function of the desired tendon tension.
real-time trajectory generation for car-like vehicles navigating dynamic environments. this paper presents tiji, a trajectory generation scheme, ie an algorithm that computes a feasible trajectory between a start and a goal state, for a given robotic system. tiji is geared towards complex dynamic systems subject to differential constraints, such as wheeled vehicles, and its efficiency warrants it can be used in real-time. above all, tiji is able to compute a trajectory that reaches the goal state at a prescribed final time in order to avoid collision with the moving objects of the environment. the method proposed, which relies upon a parametric trajectory representation, is variational in nature. the trajectory parameters are incrementally updated in order to optimize of a cost function involving the distance between the end of the trajectory computed and the (goal state, final time) pair. should the goal state be unreachable (if the final time is ill-chosen), the method returns a trajectory that ends as close as possible to the (goal state, final time) pair, which can be useful in certain applications.
thermally constrained motor operation for a climbing robot. climbing robots are especially susceptible to thermal overload during normal operation, due to the need to oppose gravity and to frequently apply internal forces for clinging. as an alternative to setting conservative limits on the motor peak and average current, we investigate methods for measuring motor temperatures, predicting motor thermal conditions and generating thermally constrained behavior. a thermal model, verified using empirical data, predicts the motor's winding temperature based on measured case temperature and input current. we also present a control strategy that maximizes robot velocity while satisfying a constraint on the maximum permissible motor winding temperature.
delay-dependent stability analysis of teleoperation systems with unsymmetric time-varying delays. this paper investigates the stability analysis problem of teleoperation system. compared with previous work, the communication delays are assumed to be both time-varying and unsymmetric. the stability analysis is performed on two classes of controllers: delayed position error feedback and delayed force feedback. by choosing lyapunov krasovskii functional, we show that the master-slave teleoperation system is asymptotically stable under specific lmi conditions. with the given controller design parameters, the proposed stability criteria can be used to compute the allowable maximum delay values. finally, the simulations are performed to show the effectiveness of the proposed method.
i-bug: an intensity-based bug algorithm. this paper introduces a sensor-based planning algorithm that uses less sensing information than any others within the family of bug algorithms. the robot is unable to access precise information regarding position coordinates, angular coordinates, time, or odometry, but is nevertheless able to navigate itself to a goal among unknown piecewise-analytic obstacles in the plane. the only sensor providing real values is an intensity sensor, which measures the signal strength emanating from the goal. the signal intensity function may or may not be symmetric; the main requirement is that the level sets are concentric images of simple closed curves, i.e. topological circles. convergence analysis and distance bounds are established for the presented approach.
dynamic effects of arc feet on the leg motion of passive walker. the passive walker with knees can naturally execute the leg motion by the dynamics of legs under the gravity only. circular arc foot is very important for the leg motion. in this paper, the dynamical effects of the arc foot, which can keep the knee joint of stance leg straight only by the stopper and can enhance the flexion of knee joint of swing leg, are discussed. also, its geometrical negative effect is examined. finally, the desired arc foot is designed and the actual effects are confirmed by a walking experiment.
static anti-windup controller design for planar 2dof robot manipulators with actuator saturation. a static anti-integrator-windup controller design method is proposed for zero-gravity planar robot manipulators. the asymptotic stability is achieved using a simple static anti-windup gain matrix for pi or pid controllers. the control performance is verified by numerical simulations and experiments on a two-link planar 2dof robot arm.
mixed-initiative in human augmented mapping. in scenarios that require a close collaboration and knowledge transfer between inexperienced users and robots, the "learning by interacting" paradigm goes hand in hand with appropriate representations and learning methods. in this paper we discuss a mixed initiative strategy for robotic learning by interacting with a user in a joint map acquisition process. we propose the integration of an environment representation approach into our interactive learning framework. the environment representation and mapping system supports both user driven and data driven strategies for the acquisition of spatial information, so that a mixed initiative strategy for the learning process is realised. we evaluate our system with test runs according to the scenario of a guided tour, extending the area of operation from structured laboratory environment to less predictable domestic settings.
vision-based guidance and control of a hovering vehicle in unknown, gps-denied environments. this paper describes the system architecture and core algorithms for a quadrotor helicopter that uses vision data to navigate an unknown, indoor, gps-denied environment. without external sensing, an estimation system that relies only on integrating inertial data will have rapidly drifting position estimates. micro aerial vehicles (mavs) are stringently weight-constrained, leaving little margin for additional sensors beyond the mission payload. the approach taken in this paper is to introduce an architecture that exploits a common mission payload, namely a video camera, as a dual-use sensor to aid in navigation. several core algorithms, including a fast environment mapper and a novel heuristic for obstacle avoidance, are also presented. finally, drift-free hover and obstacle avoidance flight tests in a controlled environment are presented and analyzed.
probabilistic estimation of multi-level terrain maps. recent research has shown that robots can model their world with multi-level (ml) surface maps, which utilize 'patches' in a 2d grid space to represent various environment elevations within a given grid cell. though these maps are able to produce 3d models of the environment while exploiting the computational feasibility of single elevation maps, they do not take into account in-plane uncertainty when matching measurements to grid cells or when grouping those measurements into 'patches.' to respond to these drawbacks, this paper proposes to extend these ml surface maps into probabilistic multi-level (pml) surface maps, which uses formal probability theory to incorporate estimation and modeling errors due to uncertainty. measurements are probabilistically associated to cells near the nominal location, and are categorized through hypothesis testing into 'patches' via classification methods that incorporate uncertainty. experimental results comparing the performances of the pml and ml surface mapping algorithms on representative objects found in both indoor and outdoor environments show that the pml algorithm outperforms the ml algorithm in most cases including in the presence of noisy and sparse measurements. the experimental results support the claim that the pml algorithm produces more densely populated, conservative representations of its environment with fewer measurements than the ml algorithm.
nanolab: a nanorobotic system for automated pick-and-place handling and characterization of cnts. carbon nanotubes (cnts) are one of the most promising materials for nanoelectronic applications. before bringing cnts into large-scale production, a reliable nanorobotic system for automated handling and characterization as well as prototyping of cnt-based components is essential. this paper presents the nanolab setup, a nanorobotic system that combines specially developed key components such as electrothermal microgrippers and mobile microrobots inside a scanning electron microscope. the working principle and fabrication of mobile microrobots and electrothermal microgripper as well as their interaction and integration is described. furthermore, the nanolab is used to explore novel key strategies such as automated locating of cnts for pick-and-place handling and methods for electrical characterization of cnts. the results have been achieved within the framework of a european research project where the scientific knowledge will be transfered into an industrial system that will be commercially available for potential customers.
flow separation for fast and robust stereo odometry. separating sparse flow provides fast and robust stereo visual odometry that deals with nearly degenerate situations that often arise in practical applications. we make use of the fact that in outdoor situations different constraints are provided by close and far structure, where the notion of close depends on the vehicle speed. the motion of distant features determines the rotational component that we recover with a robust two-point algorithm. once the rotation is known, we recover the transiational component from close features using a robust one-point algorithm. the overall algorithm is faster than estimating the motion in one step by a standard ransac-based three-point algorithm. and in contrast to other visual odometry work, we avoid the problem of nearly degenerate data, under which ransac is known to return inconsistent results. we confirm our claims on data from an outdoor robot equipped with a stereo rig.
state estimation with delayed measurements considering uncertainty of time delay. state estimation problem with time delayed measurements is addressed. in dynamic system with noise, after taking measurements, it often requires some time until that is available in a filter. a filter not considering this time delay cannot be used since a current measurement is related with a past state. these delayed measurements problem is solved with augmented state kalman filter, and uncertainty of the delayed time is also resolved based on the probability distribution of the delay. the proposed method is analyzed by a simple example, and its consistency is verified.
continuous vocal imitation with self-organized vowel spaces in recurrent neural network. a continuous vocal imitation system was developed using a computational model that explains the process of phoneme acquisition by infants. human infants perceive speech sounds not as discrete phoneme sequences but as continuous acoustic signals. one of critical problems in phoneme acquisition is the design for segmenting these continuous speech sounds. the key idea to solve this problem is that articulatory mechanisms such as the vocal tract help human beings to perceive speech sound units corresponding to phonemes. to segment acoustic signal with articulatory movement, we apply the segmenting method to our system by recurrent neural network with parametric bias (rnnpb). this method determines the multiple segmentation boundaries in a temporal sequence using the prediction error of the rnnpb model, and the pb values obtained by the method can be encoded as kind of phonemes. our system was implemented by using a physical vocal tract model, called the maeda model. experimental results demonstrated that our system can self-organize the same phonemes in different continuous sounds, and can imitate vocal sound involving arbitrary numbers of vowels using the vowel space in the rnnpb. this suggests that our model reflects the process of phoneme acquisition.
control uncertainty in image-based visual servoing. it has recently been demonstrated that the effect of visual measurement errors on the open-loop control in visual servoing can be estimated using linear propagation of errors. the uncertainty estimation offers a tool to build and analyze hybrid control systems such as switching or partitioning control. in earlier works, position-based and 2.5d servoing have been analysed. this work extends the approach to the analysis of closed loop uncertainty, showing how the path uncertainty can be approximated. the uncertainty is analyzed in cartesian reference to make the approach general over different hardware. in addition, we show how image-based visual servoing can be analysed using the same approach.
effect of energy feedbacks on virtual slope walking: i. complementary energy feedback. this paper presents our study over the effect of complementary energy feedback on virtual slope walking, while virtual slope walking is our new biped gait generation method inspired by passive dynamic walking. the energy feedback strength is defined and the walking is modeled as a step-to-step function. the jacobi matrix eigenvalues of the function are calculated together with the basin of attraction. from the analysis, we find the characteristic of complementary energy feedback is being effective on a fast gait but weak on a slow one. by making use of the complementary energy feedback in walking experiment, our robot achieves speed change from 1.5leg/s to 4.1leg/s.
maccepa 2.0: adjustable compliant actuator with stiffening characteristic for energy efficient hopping. the maccepa (mechanically adjustable compliance and controllable equilibrium position actuator) is an electric actuator of which the compliance and equilibrium position are fully independently controllable and both are set by a dedicated servomotor. in this paper an improvement of the actuator is proposed where the torque-angle curve and consequently the stiffness-angle curve can be modified by choosing an appropriate shape of a profile disk, which replaces the lever arm of the former design. the actuator has a large joint angle, torque and stiffness range and these properties can be made beneficial for safe human robot interaction and the construction of energy efficient walking, hopping and running robots. the ability to store and release energy is shown by simulations on a 1dof hopping robot. its hopping height is much higher compared to a configuration in which the same motor is used in a traditional stiff setup. the stiffness of the actuator has a stiffening characteristic so the leg stiffness resembles more a linear stiffness as found in humans.
robust servo-control for underwater robots using banks of visual filters. we present an application of machine learning to the semi-automatic synthesis of robust servo-trackers for underwater robotics. in particular, we investigate an approach based on the use of boosting for robust visual tracking of color objects in an underwater environment. to this end, we use adaboost, the most common variant of the boosting algorithm, to select a number of low-complexity but moderately accurate color feature trackers and we combine their outputs. the novelty of our approach lies in the design of this family of weak trackers, which enhances a straightforward color segmentation tracker in multiple ways. from a large and diverse family of possible filters, we select a small subset that optimizes the performance of our trackers. the tracking process applies these trackers on the input video frames, and the final tracker output is chosen based on the weights of the final array of trackers. by using computationally inexpensive, but somewhat accurate trackers as members of the ensemble, the system is able to run at quasi real-time, and thus, is deployable on-board our underwater robot. we present quantitative cross-validation results of our spatio-chromatic visual tracker, and conclude by pointing out some difficulties faced and subsequent shortcomings in the experiments we performed, along with directions of future research in the area of ensemble tracking in real-time.
above 40g acceleration for pick-and-place with a new 2-dof pkm. this paper introduces a new two-degree-of-freedom parallel manipulator producing two translations in the vertical plane. one drawback of existing robots built to realize those dof is their lack of rigidity along the transversal axis, another one being their limited ability to provide very high acceleration. indeed, these architectures cannot be lightweight and stiff at the same time. the proposed architecture is a spatial mechanism which guarantees a good stiffness along the transversal axis. this parallel architecture is composed by two actuated kinematic chains, and two passive chains built in the transversal plane. the key feature of this robot comes from the passive chains which are coupled for creating a kinematic constraint: the platform stays in one plane. a stiffness analysis shows that the robot can be lighter and stiffer than a classical 2 dof mechanism. a prototype of this robot is presented and preliminary tests show that accelerations above 40 g can be achieved while keeping a low tracking error.