waashk/dblp huggingface dataset:
force control with safety constraints via iterative feedback tuning. this paper presents a new design method for force control, which aims to control the interaction force between a robot and a human by satisfying certain safety requirements. in this method, an optimization-based control algorithm, called iterative feedback tuning (ift), is used to employ safety requirements as constraints of an optimization problem, which is then solved using sequential quadratic programming (sqp). therefore, this control method is applicable for safety-critical systems such as personal service robots. these robots are developed to provide assistance to patients or disabled people in their daily life by performing human-robot contact tasks such as wiping the face with a towel, scratching, etc. in addition, in ift, a newton search direction to update the controller parameters at each iteration is obtained on the basis of the closed-loop experimental data. hence, ift does not require explicit modeling of environment, in particular, human dynamics, otherwise in model-based approach it will be hard task to obtain a useful model. in the simulation and experiment, the effectiveness of the proposed method is examined by applying it to 1-dof contact system.
modeling and control of hydraulic rotary actuators used in forestry cranes. the steps for modeling and control of a hydraulic rotary actuator are discussed. our aim is to present experimental results working with a particular sensing device for angular position as a complement to pressure sensing devices. we provide the steps in experimental system identification used for modeling the system dynamics. the cascade controller designed contains an inner loop for an accurate tracking of torque while stabilizing position reference trajectories. the performance of this design is experimentally verified.
mining gps data for extracting significant places. this paper addresses the problem of safety in mining applications. it presents new metrics that can be used to determine dangerous situations during mine operation in real time. it also presents a fast and robust algorithm for extracting significant places from information logged by a state-of-the-art collision avoidance system. determining significant places provides valuable context information in a variety of applications such as map building, vehicle tracking and user assistance. in our case, we are interested in obtaining context information as a preliminary step towards improving mining safety. the algorithm presented here is validated with experimental data obtained from a fleet of haulage vehicles operating in various open pit mines.
randomised mpc-based motion-planning for mobile robot obstacle avoidance. this paper presents an algorithm for real-time sensor-based motion planning under kinodynamic constraints, in unknown environments. the objective of the trajectory-generation algorithm is to optimise a cost function out to a limited time horizon. the space of control trajectories is searched by expanding a tree using randomised sampling, in a manner similar to an rrt. the algorithm is improved by seeding the tree using the best control trajectory from the previous iteration, and by pruning branches based on a bound to the cost function and the best trajectory found so far. performance of the algorithm is analysed in simulation. in addition, the algorithm has been implemented on two kinds of vehicles: the segway rmp and a four-wheel-drive. the algorithm has been used to drive autonomously for a combined total on the order of hundreds of hours.
the effect of chord-wise flexibility on the aerodynamic force generation of flapping wings: experimental studies. wings of insects are flexible structures. although there has been much recent progress in the area of insect flight aerodynamics, very little is known about how wing flexibility influences aerodynamic forces during flapping flight. we investigated this question using a dynamically scaled mechanical model of insect wings. using a suite of wings with varying flexural stiffness (ei) values, we generated aerodynamic polar plots to characterize the force coefficients of flexible wings. these polar plots showed that the aerodynamic performance of the wings varied with wing flexibility. in general, aerodynamic force production decreased with increasing flexibility. both lift and drag coefficients of wings are greater when wings are more rigid. however, at very high angles of attack, flexible wings generated greater lift than a rigid wing. in addition, the ratio of lift-to-drag also decreased with increasing flexibility. these data show that flexible wings offer no aerodynamic advantage over a rigid wing under steady state circumstances. because wing material in insects is usually flexible but reinforced by wing veins, we tested the hypothesis that wing veins enhance the aerodynamic performance of wings by increasing their effective stiffness. our data suggests that even a very basic framework of appropriately placed wing veins can substantially increase the functional rigidity of the wings thereby enhancing its aerodynamic performance.
a variable stiffness pzt cellular actuator with tunable resonance for cyclic motion tasks. a simple and efficient approach for varying the inherent stiffness and impedance of a muscle-like actuator is presented. the basic architecture of pzt cellular actuators has already achieved a large effective strain (10-20%). this architecture is modified and extended so that each cellular unit can be switched between a zero compliance state and constant compliance state. the effective stiffness of the cellular actuator is varied by changing the distribution of cellular units in the rigid versus compliant state. furthermore, by placing a multitude of these cellular units in series or parallel, the stiffness can vary within a large set of discrete values. this paper also demonstrates the viability of the variable stiffness cellular actuator for cyclic tasks such as running and flapping. the basic principle and design concept for the actuator is described, followed by force-displacement analysis. a dynamic model is then constructed to demonstrate the variable resonance properties of the actuator under load.
a stereoscopic fibroscope for camera motion and 3d depth recovery during minimally invasive surgery. this paper introduces a stereoscopic fibroscope imaging system for minimally invasive surgery (mis) and examines the feasibility of utilizing images transmitted from the distal fibroscope tip to a proximally mounted ccd camera to recover both camera motion and 3d scene information. fibre image guides facilitate instrument miniaturization and have the advantage of being more easily integrated with articulated robotic instruments. in this paper, twin 10,000 pixel coherent fibre bundles (590µm diameter) have been integrated into a bespoke laparoscopic imaging instrument. images captured by the system have been used to build a 3d map of the environment and reconstruct the laparoscope's 3d pose and motion using a slam algorithm. detailed phantom validation of the system demonstrates its practical value and potential for flexible mis instrument integration due to the small footprint and flexible nature of the fibre image guides.
sliding motion control of active flexible cable using simple shape information. we propose a new framework for a self-propelled flexible cable in which the freedom of lateral motion with sliding movements is increased on the basis of simple shape information. we developed a large-scale prototype of the flexible cable that has a ciliary drive mechanism and precise shape sensors to investigate our concept. we developed a kinetic model for the prototype by employing the nonlinear driving force model and the lateral friction model for representing slippages. further, we proposed a sliding motion control method that focus on the shape and length of the straight element. we applied the method to control the running direction. the experimental results and dynamic simulations demonstrated the effectiveness of sliding motion for controlling the running direction.
representing sets of orientations as convex cones. in a wide range of applications the orientation of a rigid body does not need to be restricted to one given orientation, but can be given as a continuous set of frames. we address the problem of defining such sets and to find simple tests to verify if an orientation lies within a given set. the unit quaternion is used to represent the orientation of the rigid body and we develop three different sets of orientations that can easily be described by simple constraints in quaternion space. the three sets discussed can also be described as convex cones in r3 defined by different norms. by describing the sets as convex cones and using certain properties of dual cones, we are able find simpler representations for the set of orientations and computationally faster and more accurate tests to verify if a quaternion lies within the given set.
trajectory generation of robotic fingers based on tri-axial tactile data for cap screwing task. in a previous paper, we developed a robotic finger equipped with optical three-axis tactile sensors, of which the sensing cell can separately detect normal and shearing forces. with appropriate precision, the robotic finger was able to perform three tasks: scanning flat specimens to obtain the friction coefficient, following the contour of objects, and manipulating a parallelepiped case put on a table by sliding it on the table. in the present study, designed as a follow-up to the above study, a robotic hand is composed of two robotic fingers. not only tri-axial force distribution directly obtained from the tactile sensor but also the time derivative of the shearing force distribution are used for the hand control algorithm: if grasping force measured from normal force distribution is lower than a threshold, grasping force is increased; the time derivative is defined as slippage; if slippage arises, grasping force is enhanced to prevent fatal slippage between the finger and an object. in the verification test, the robotic hand screws a bottle cap to close it. although input finger trajectories were a rectangular roughly decided to touch and screw the cap, a segment of the rectangular was changed from a straight line to a curved line to fit the cap contour. we concluded that higher order tactile information such as tri-axial tactile data can reduce the complexity of the control algorithm.
climbing rough vertical surfaces with hierarchical directional adhesion. prior research in biology and mechanics has shown the importance of hierarchy to the performance of dry adhesive systems on rough surfaces. the gecko utilizes several levels of hierarchy that operate on length scales from millimeters to 100s of nanometers in order to maneuver on smooth and rough vertical surfaces ranging from glass to rock. the gecko's hierarchical system serves two main purposes: it permits conformation to the surface for a large effective area of contact, and it distributes the load evenly among contacting elements. we present a new two-tiered directional adhesive system that provides these capabilities for a gecko-inspired climbing robot. the distal features consist of wedge-shaped structures with a base width of 50 µm and a height of approximately 180 µm. the wedges are mounted atop angled cylindrical features, 380 µm in diameter by approximately 1 mm long. together, the proximal and distal features bend preferentially in the direction of inclination when loaded with a tangential force, achieving a combination of directional adhesion and conformation to rough surfaces. using this system, a four legged robot that was previously restricted to climbing smooth surfaces is able to climb vertical surfaces such as a wood panels, painted metals, and plastics. on rougher surfaces, the two-tiered system improves adhesion by a factor of five compared to the wedge features alone. the hierarchical system also improved alignment and performance for large patch sizes.
path planning in 1000+ dimensions using a task-space voronoi bias. the reduction of the kinematics and/or dynamics of a high-dof robotic manipulator to a low-dimension "task space" has proven to be an invaluable tool for designing feedback controllers. when obstacles or other kinodynamic constraints complicate the feedback design process, motion planning techniques can often still find feasible paths, but these techniques are typically implemented in the high-dimensional configuration (or state) space. here we argue that providing a voronoi bias in the task space can dramatically improve the performance of randomized motion planners, while still avoiding non-trivial constraints in the configuration (or state) space. we demonstrate the potential of task-space search by planning collision-free trajectories for a 1500 link arm through obstacles to reach a desired end-effector position.
using a pocket-filling strategy for distributed reconfiguration of a system of hexagonal metamorphic robots in an obstacle-cluttered environment. we address the problem of reconfiguration planning for a metamorphic robotic system composed of a large number of hexagonal mobile robots. our objective is to develop an algorithm to plan the concurrent movement of individual robots over a lattice composed of identical robots, from an initial configuration i to a goal configuration g, when g contains one or more obstacles. the contribution of this paper is a deterministic motion planning algorithm to envelop multiple obstacles in an admissible set of goal configurations while eliminating the risk of module collision or deadlock. we developed a discrete event simulator to test our algorithms, and every admissible g tested was filled successfully. we include a full proof of correctness and analysis of our algorithm.
comparison of surface normal estimation methods for range sensing applications. as mobile robotics is gradually moving towards a level of semantic environment understanding, robust 3d object recognition plays an increasingly important role. one of the most crucial prerequisites for object recognition is a set of fast algorithms for geometry segmentation and extraction, which in turn rely on surface normal vectors as a fundamental feature. although there exists a plethora of different approaches for estimating normal vectors from 3d point clouds, it is largely unclear which methods are preferable for online processing on a mobile robot. this paper presents a detailed analysis and comparison of existing methods for surface normal estimation with a special emphasis on the trade-off between quality and speed. the study sheds light on the computational complexity as well as the qualitative differences between methods and provides guidelines on choosing the 'right' algorithm for the robotics practitioner. the robustness of the methods with respect to noise and neighborhood size is analyzed. all algorithms are benchmarked with simulated as well as real 3d laser data obtained from a mobile robot.
statistics for sparse, high-dimensional, and nonparametric system identification. local linearization techniques are an important class of nonparametric system identification. identifying local linearizations in practice involves solving a linear regression problem that is ill-posed. the problem can be ill-posed either if the dynamics of the system lie on a manifold of lower dimension than the ambient space or if there are not enough measurements of all the modes of the dynamics of the system. we describe a set of linear regression estimators that can handle data lying on a lower-dimension manifold. these estimators differ from previous estimators, because these estimators are able to improve estimator performance by exploiting the sparsity of the system - the existence of direct interconnections between only some of the states - and can work in the "large p, small n" setting in which the number of states is comparable to the number of data points. we describe our system identification procedure, which consists of a presmoothing step and a regression step, and then we apply this procedure to data taken from a quadrotor helicopter. we use this data set to compare our procedure with existing procedures.
interactive learning of the acoustic properties of household objects. human beings can perceive object properties such as size, weight, and material type based solely on the sounds that the objects make when an action is performed on them. in order to be successful, the household robots of the near future must also be capable of learning and reasoning about the acoustic properties of everyday objects. such an ability would allow a robot to detect and classify various interactions with objects that occur outside of the robot's field of view. this paper presents a framework that allows a robot to infer the object and the type of behavioral interaction performed with it from the sounds generated by the object during the interaction. the framework is evaluated on a 7-d.o.f. barrett wam robot which performs grasping, shaking, dropping, pushing and tapping behaviors on 36 different household objects. the results show that the robot can learn models that can be used to recognize objects (and behaviors performed on objects) from the sounds generated during the interaction. in addition, the robot can use the learned models to estimate the similarity between two objects in terms of their acoustic properties.
probabilistic graph-clear. this paper introduces a probabilistic model for multirobot surveillance applications with limited range and possibly faulty sensors. sensors are described with a footprint and a false negative probability, i.e. the probability of failing to report a target within their sensing range. the model implements a probabilistic extension to our formerly developed deterministic approach for modeling surveillance tasks in large environments with large robot teams known as graph-clear. this extension leads to a new algorithm that allows to answer new design and performance questions, namely 1) how many robots are needed to obtain a certain confidence that the environment is free from intruders, and 2) given a certain number of robots, how should they coordinate their actions to minimize their failure rate.
study on adhesion force reduction and state estimation by piezo-transducer. our previous paper presented a method for reducing adhesion forces by oscillation and showed the adhesion state can be checked by analyzing the data obtained by laser displacement meter. however, there are several problems in this method. 1)the end-effector must be located at the specific point where laser displacement meter can measure oscillation. 2)the adhesion state can not be checked if something blocks the light/laser or the target leaves the measuring point. 3)the total system becomes very large. to resolve these problems, this paper firstly presents a method for checking the adhesion state by piezo-transducer. next, to achieve more precise manipulation, we propose a method to deform the end-effector by adding dc input to the piezo actuator which is also oscillated simultaneously to reduce adhesion forces. furthermore, we find that the first mode resonance frequency shifts with the increase of the pushing force applied to the object by the end-effector. using the shift amount, we develop a method for checking the adhesion state.
a high-speed multi-gpu implementation of bottom-up attention using cuda. in this paper a novel implementation of the saliency map model on a multi-gpu platform using cuda technology is presented. the saliency map model is a well-known computational model for bottom-up attention selection and serves as a basis of many attention control strategies of cognitive vision systems. a real-time implementation is the prerequisite of an application of bottom-up attention on mobile robots and vehicles. parallel computation on graphics processing unit (gpu) provides an excellent solution for this kind of compute-intensive image processing. running on 1 to 4 nvidia geforce 8800 (gtx) graphics cards a frame rate of 313 fps at resolution of 640 × 480 is achieved, which is approximately 8.5 times faster than the standard implementations on cpus. the implementation is also evaluated using a high-speed camera at 200 hz. using two gpus only 2 ms extra computational time for the saliency map generation in addition to the camera capture time is required for images of 640 × 480 pixels.
visual homing and surprise detection for cognitive mobile robots using image-based environment representations. one important feature of a cognitive system is to perceive and understand its environment and to adapt its actions to changes and unforeseen situations. in this paper, we propose a scheme for visual surprise detection in cognitive mobile robots. with the robot's observation and a set of reference images previously acquired near its current viewpoint, a pixel-wise surprise trigger is computed using bayesian probabilistic inference techniques. with appropriate mathematical approximations this algorithm can be implemented on modern graphics hardware which nearly allows for real-time surprise detection. in order to refer to prior observations, a mobile robot has to be able to re-localize itself with respect to its environment. thus, we also present two online image-based homing algorithms which both facilitate the computation of location-independent surprise triggers. experiments show acceptable results in terms of robust and fast detection of unexpected changes in the environment.
bayesian plan recognition for brain-computer interfaces. for people with very severe motor dysfunctions, brain-computer interfaces (bcis) may provide the solution to regain mobility and manipulation capabilities. unfortunately, bcis are characterized by a limited bandwidth and uncertainty on the bci output. in the past, we have developed a bayesian plan recognition framework that estimates from uncertain human-robot interface signals the task a robot should execute. this paper extends our plan recognition framework to incorporate uncertain bci signals. a benchmark test is proposed and adopted to evaluate both the plan recognition framework and the performance of the bci user, for the concrete application of wheelchair driving.
combining area patrol, perimeter surveillance, and target tracking using ordered upwind methods. the problems of area patrol, perimeter surveillance, and target tracking are combined into a single reconnaissance framework. this integration is based on application of ordered upwind methods to track the propagation of the target position boundary. the ordered upwind methods provide a computationally efficient method for computing the target track boundary over a discretized nonuniform mesh of the environment. planning methods based on the target track boundary are discussed. simulation results demonstrate the use of ordered upwind methods for reconnaissance tasks.