waashk/dblp huggingface dataset:
simultaneous local and global state estimation for robotic navigation. recent applications of robotics often demand two types of spatial awareness: 1) a fine-grained description of the robot's immediate surroundings for obstacle avoidance and planning, and 2) knowledge of the robot's position in a large-scale global coordinate frame such as that provided by gps. although managing information at both of these scales is often essential to the robot's purpose, each scale has different requirements in terms of state representation and handling of uncertainty. in such a scenario, it can be tempting to pick either a body-centric coordinate frame or a globally fixed coordinate frame for all state representation. although both choices have advantages, we show that neither is ideal for a system that must handle both global and local data. this paper describes an alternative design: a third coordinate frame that stays fixed to the local environment over short time-scales, but can vary with respect to the global frame. careful management of uncertainty in this local coordinate frame makes it well-suited for simultaneously representing both locally and globally derived data, greatly simplifying system design and improving robustness. we describe the implementation of this coordinate frame and its properties when measuring uncertainty, and show the results of applying this approach to our 2007 darpa urban challenge vehicle.
multi-robot routing with linear decreasing rewards over time. we study multi-robot routing problems (mr-ldr) where a team of robots has to visit a set of given targets with linear decreasing rewards over time, such as required for the delivery of goods to rescue sites after disasters. the objective of mr-ldr is to find an assignment of targets to robots and a path for each robot that maximizes the surplus, which is defined to be the total reward collected by the team minus its total travel cost. we develop a mixed integer program that solves mr-ldr optimally with a flow-type formulation and can be solved faster than the standard tsp-type formulations but also show that solving mr-ldr optimally is np-hard. we then develop an auction-based algorithm and demonstrate that it solves mr-ldr in seconds and with a surplus that is comparable to the surplus found by the mixed integer program with a 12 hour time limit.
manipulation at the nanonewton level: micrograpsing for mechanical characterization of biomaterials. this paper presents the use of a monolithic, force-feedback mems (microelectomechanical systems) microgripper for characterizing both elastic and viscoelastic properties of highly deformable hydrogel microcapsules (15-25µm) at wet state during micromanipulation. the single-chip microgripper integrates an electrothermal microactuator and two capacitive force sensors, one for contact detection (force resolution: 38.5nn) and the other for gripping force measurements (force resolution: 19.9nn). through nanonewton force measurements, closed-loop force control, and visual tracking, the system quantified young's modulus values and viscoelastic parameters of alginate microcapsules, demonstrating an easy-to-operate, accurate compression testing technique for characterizing soft, micrometer-sized biomaterials.
robotic rehabilitation treatments: realization of aquatic therapy effects in exoskeleton systems. exoskeletons are attracting a great attention as a new means of rehabilitation devices. in such applications, control algorithms of exoskeletons are often inspired by nature for natural and effective assistance for patients. in this paper, a control algorithm is inspired by aquatic therapy. aquatic therapy has various benefits for rehabilitation processes based on useful properties of water, e.g. buoyancy and drag. however, realization of such effects is challenged by limitations in hardware, such as mechanical impedance or impreciseness of actuator forces. therefore, the resistive forces generated by actuators, which cause serious discomfort to patients, are precisely modeled and compensated to realize the control algorithm inspired by aquatic therapy effectively. the proposed methods are implemented in subar developed by sogang university and verified by experiments.
a harmonic potential field approach for navigating a rigid, nonholonomic robot in a cluttered environment. this paper demonstrates the ability of the harmonic potential field (hpf) planning approach to generate a provably-correct, constrained, well-behaved trajectory for a rigid, nonholonomic robot (a tractor-trailer robot is not rigid) in a stationary, cluttered environment. this is accomplished using a closed loop control scheme that is inspired by model predictive control (mpc). the scheme is realized using a synchronizing signal derived from the hpf along with a procedure for inverting the process the robot is using for actuating motion. performance proofs as well as simulation results of the suggested planner are supplied.
efficient and safe on-line motion planning in dynamic environments. this paper presents a new on-line planner for dynamic environments that is based on the concept of velocity obstacles (vo). it addresses the issue of motion safety, i.e. avoiding states of inevitable collision, by selecting a proper time horizon for the velocity obstacle. the proper choice of the time horizon ensures that the boundary of the velocity obstacle coincides with the boundary of the set of inevitable collision states. this time horizon is determined by the minimum time it would take the robot to avoid collision, either by stopping or by passing the respective obstacle. the planner generates a near-time optimal trajectory to the goal by selecting at each time step the velocity that minimizes the time-to-go and is out of the velocity obstacle. the planner takes into account the shape, velocity, and path curvature of the obstacle's trajectory. it is demonstrated for on-line motion planning in very crowded static and dynamic environments.
a harmonic potential field approach with a probabilistic space descriptor for planning in non-divisible environments. this paper extends the capabilities of the harmonic potential field approach to planning to cover the situation where the workspace of a robot cannot be segmented into geometrical subregions each having an attribute of its own. instead the suggested planner accepts a holistic, task-centered, probabilistic descriptor of the workspace as an input. this descriptor is processed along with a goal point to yield the navigation policy used to direct motion. the extension is based on the physical analogy with an electric current flowing in a nonhomogeneous conducting medium. proofs of the ability of the modified approach to avoid zero-probability (definite threat) regions and converge to the goal are provided. the capabilities of the suggested planner are demonstrated using simulation.
1-dof dynamic pitching robot that independently controls velocity, angular velocity, and direction of a ball: contact models and motion planning. this paper demonstrates that a 1-dof planar ball-throwing robot has the capability of controlling three kinematic variables of a ball independently: translational velocity, angular velocity, and direction. the throwing motion is modeled using two underactuated contact dynamics, called a finger-link contact model and a fingertip contact model, with a unidirectional transition from one model to another. a combination of a preliminary global search method and a search algorithm based on a simulated annealing (sa) algorithm provides joint torque commands for this highly nonlinear system. an experimental system with a 1-dof planer manipulator has been developed that throws a disk (ball) in a frictionless plane. the experimental results confirm the validity of the contact models and the feasibility of independent control of the three kinematic variables.
guiding medical needles using single-point tissue manipulation. this paper addresses the use of robotic tissue manipulation in medical needle insertion procedures to improve targeting accuracy and to help avoid damaging sensitive tissues. to control these multiple, potentially competing objectives, we present a phased controller that operates one manipulator at a time using closed-loop imaging feedback. we present an automated procedure planning technique that uses tissue geometry to select the needle insertion location, manipulation locations, and controller parameters. the planner uses a stochastic optimization of a cost function that includes tissue stress and robustness to disturbances. we demonstrate the system on 2d tissues simulated with a mass-spring model, including a simulation of a prostate brachytherapy procedure. it can reduce targeting errors from more than 2cm to less than 1mm, and can also shift obstacles by over 1cm to clear them away from the needle path.
design and synthesis of wire-actuated universal-joint wrists for surgical applications. this paper presents synthesis methods and performance measures for wire-actuated wrists using a universal-joint constraining linkage. performance measures based on the isotropy of the wrench-closure workspace are derived and used in generating design atlases as a function of non-dimensional design parameters. the performance indices that are optimized include wrench-closure workspace, isotropy of the wrench-closure workspace, and kinematic conditioning index. stiffness is used to define a safety margin from singularities of wrench closure. a design example of a wire-actuated surgical wrist is presented and validated through simulation. the methods presented in this paper are useful for quick dimensional synthesis of wire-actuated wrists with predefined wrench-closure workspaces.
active-learning assisted self-reconfigurable activity recognition in a dynamic environment. it is desirable to know a resident's on-going activities before a robot or a smart system can provide attentive services to meet real human needs. this work addresses the problem of learning and recognizing human daily activities in a dynamic environment. most currently available approaches learn offline activity models and recognize activities of interest on a real time basis. however, the activity models become outdated when human behaviors or device deployment have changed. it is a tedious and error-prone job to recollect data for retraining the activity models. in such a case, it is important to adapt the learnt activity models to the changes without much human supervision. in this work, we present a self-reconfigurable approach for activity recognition which reconfigures previously learnt activity models and infers multiple activities under a dynamic environment meanwhile pursuing minimal human efforts in relabeling training data by utilizing active-learning assistance.
tactile sensing for an anthropomorphic robotic hand: hardware and signal processing. in this paper, a tactile sensing system for an anthropomorphic robot hand is presented. the tactile sensing system is designed as a construction kit making it very versatile. the sensor data preprocessing is embedded into the hand's hardware structure and is fully integrated. the sensor system is able to gather tactile pressure profiles and to measure vibrations in the sensor's cover. additionally to the introduction of the hardware, the signal processing and the classification of the acquired sensor data will be explained in detail. these algorithms make the tactile sensing system capable to detect contact points, to classify contact patterns and to detect slip conditions during object manipulation and grasping.
airborne smoothing and mapping using vision and inertial sensors. this paper presents a framework for integrating sensor information from an inertial measuring unit (imu), global positioning system (gps) receiver and monocular vision camera mounted to a low-flying unmanned aerial vehicle (uav) for building large-scale terrain reconstructions. our method seeks to integrate all of the sensor information using a statistically optimal non-linear least squares smoothing algorithm to estimate vehicle poses simultaneously to a dense point feature map of the terrain. a visualisation of the terrain structure is then created by building a textured mesh-surface from the estimated point features. the resulting terrain reconstruction can be used for a range of environmental monitoring missions such as invasive plant detection and biomass mapping.
design methodologies of a hybrid actuation approach for a human-friendly robot. determining design parameters is often a challenging procedure, especially in human-friendly robot design due to competition between robot safety and performance. presenting an analytical model of hybrid actuation for human-friendly robot development, this paper proposes design methodologies to improve performance factors such as range of motion, payload, and acceleration while maintaining the safety factor of effective inertia. the optimized parameters for various design requirements have been provided for 1dof and 2dof applications. comparison between current design parameters and the optimized parameters for a current platform shows the performance improvement. in future work this research will be extended to systems with higher degrees of freedom.
automatic weight learning for multiple data sources when learning from demonstration. traditional approaches to programming robots are generally inaccessible to non-robotics-experts. a promising exception is the learning from demonstration paradigm. here a policy mapping world observations to action selection is learned, by generalizing from task demonstrations by a teacher. most learning from demonstration work to date considers data from a single teacher. in this paper, we consider the incorporation of demonstrations from multiple teachers. in particular, we contribute an algorithm that handles multiple data sources, and additionally reasons about reliability differences between them. for example, multiple teachers could be inequally proficient at performing the demonstrated task. we introduce demonstration weight learning (dwl) as a learning from demonstration algorithm that explicitly represents multiple data sources and learns to select between them, based on their observed reliability and according to an adaptive expert learning inspired approach. we present a first implementation of dwl within a simulated robot domain. data sources are shown to differ in reliability, and weighting is found impact task execution success. furthermore, dwl is shown to produce appropriate data source weights that improve policy performance.
on connectivity maintenance in linear cyclic pursuit. the paper studies the cyclic pursuit problem in presence of connectivity constraints among single-integrator agents. the robots, each one pursuing its leading neighbor along the line of sight rotated by a common offset angle, are supposed to have a communication set described by a disk of constant radius. given the initial position of the agents, we determine the communication radii that preserve the connectivity of the robots while they rendezvous at a point or converge to an evenly spaced circle formation. the special case that the initial condition is a linear combination of the eigenvectors of the dynamic matrix of the system, is studied in detail. on the other hand, given the communication radii, we find the set of initial conditions that guarantee the robots remain always connected. as a final contribution, once assigned a "non-optimal" radius, we study the stability of the hybrid system describing the dynamics of the robotic network under variable connectivity levels.
model adaptation with least-squares svm for adaptive hand prosthetics. the state-of-the-art in control of hand prosthetics is far from optimal. the main control interface is represented by surface electromyography (emg): the activation potentials of the remnants of large muscles of the stump are used in a nonnatural way to control one or, at best, two degrees-of-freedom. this has two drawbacks: first, the dexterity of the prosthesis is limited, leading to poor interaction with the environment; second, the patient undergoes a long training time. as more dexterous hand prostheses are put on the market, the need for a finer and more natural control arises. machine learning can be employed to this end. a desired feature is that of providing a pre-trained model to the patient, so that a quicker and better interaction can be obtained. to this end we propose model adaptation with least-squares svms, a technique that allows the automatic tuning of the degree of adaptation. we test the effectiveness of the approach on a database of emg signals gathered from human subjects. we show that, when pre-trained models are used, the number of training samples needed to reach a certain performance is reduced, and the overall performance is increased, compared to what would be achieved by starting from scratch.
sparsing of information matrix for practical application of a robot's slam. mobile robot could navigate in unknown environment autonomously with the help of simultaneous localization and mapping (slam). recently, slam based on information matrix enjoys much popularity since it is naturally sparse. however, the computational burden related to information matrix balloons with respect to the increase of the mapped landmarks. in this paper, by considering the features of information matrix, we present a novel method which wipes off nearly half of the elements in information matrix. the errors that come from sparsification decrease apparently by loop-closure. furthermore, the relationship between sparsification and slam accuracy is analyzed theoretically. a large scale simulation and experiment conducted on a real robot suggest that the technique is effective for a robot's slam in real-world applications.
multi-beam laser micromanipulation of microtool by integrated optical tweezers. in noncontact cell manipulation with optical tweezers, we need to use microtools to avoid damages to the cells by direct laser irradiation. when we manipulate complex structures such as microtools, a multi-beam optical tweezers which can manipulate multiple objects is suitable; however manipulation speed or trapping number are not so good. therefore, we developed an integrated optical tweezers by using time-shared scanning method for high speed manipulation and generalized phase contrast method for a lot of trapping. then, we proposed cell manipulation method which suits for the integrated optical tweezers and then we designed the shape of microtools made of su-8. a new fabrication method of su-8 microtools was proposed to improve productivity. at last, we made the experiment system and the effectiveness of proposed methods was confirmed.
towards a navigation system for autonomous indoor flying. recently there has been increasing research on the development of autonomous flying vehicles. whereas most of the proposed approaches are suitable for outdoor operation, only a few techniques have been designed for indoor environments. in this paper we present a general system consisting of sensors and algorithms which enables a small sized flying vehicle to operate indoors. this is done by adapting techniques which have been successfully applied on ground robots. we released our system as open-source with the intention to provide the community with a new framework for building applications for indoor flying robots. we present a set of experiments to validate our system on an open source quadrotor.
inferring a probability distribution function for the pose of a sensor network using a mobile robot. in this paper we present an approach for localizing a sensor network augmented with a mobile robot which is capable of providing inter-sensor pose estimates through its odometry measurements. we present a stochastic algorithm that samples efficiently from the probability distribution for the pose of the sensor network by employing rao-blackwellization and a proposal scheme which exploits the sequential nature of odometry measurements. our algorithm automatically tunes itself to the problem instance and includes a principled stopping mechanism based on convergence analysis. we demonstrate the favourable performance of our approach compared to that of established methods via simulations and experiments on hardware.
power modeling of a skid steered wheeled robotic ground vehicle. analysis of the power consumption of a robotic ground vehicle (rgv) is important for planning since it enables motion plans that do not violate the power limitations of the motors, energy efficient path planning, prediction of the ability to complete a task based upon the vehicle's current energy supply, and estimation of when the vehicle will need to refuel or recharge. power modeling is particularly difficult for skid steered vehicles because of the complexities of properly taking into account the skidding that is used for vehicle turning. this paper begins with a 2-dimensional, second order differential equation of a skid steered, wheeled rgv and shows that the power model is terrain dependent and is a function of both the turning radius and linear velocity of the vehicle. this model was verified experimentally, and a comprehensive set of experiments was performed to describe the power consumption of a skid steered rgv on asphalt.
a modified newton-euler method for dynamic computations in robot fault detection and control. we present a modified recursive newton-euler method for computing some dynamic expressions that arise in two problems of fault detection and control of serial robot manipulators, and which cannot be evaluated numerically using the standard method. the two motivating problems are: i) the computation of the residual vector that allows accurate detection of actuator faults or unexpected collisions using only robot proprioceptive measurements, and ii) the evaluation of a passivity-based trajectory tracking control law. the modified newton-euler algorithm generates factorization matrices of the coriolis and centrifugal terms that satisfy the skew-symmetric property. the computational advantages with respect to numerical evaluation of symbolically obtained dynamic expressions is illustrated on a 7r dlr lightweight manipulator.
statistically integrated semiotics that enables mutual inference between linguistic and behavioral symbols for humanoid robots. this paper describes the linguistic model based on symbolization of motion patterns for humanoid robots. the model consists of two kinds of stochastic models: the motion language model and the natural language model. the motion language model stochastically connects the symbols of motion patterns to the morpheme words through the latent states which represent the underlying linguistic structure such as semantic contents. the natural language model represents the dynamics of the word classes. the motion language model and the natural language model correspond to semantics and syntax respectively. the integration of the motion language model and the natural language model allows robots not only to linguistically interpret the motion patterns as sentences but also to generate the motions from the sentences. the two kinds of linguistic processes of the interpretation and the generation can be obtained by solving search problems: search for a sequence of morpheme words and a symbol of motion pattern. the proposed approach to interpretation of motion patterns as sentences and generation of motion patterns from the sentences through integration of the motion language model and the natural language model is validated by the experiment on the human behavioral data.