waashk/dblp huggingface dataset:
human brain-teleoperated robot between remote places. this paper describes an eeg-based human brain-actuated robotic system, which allows performing navigation and visual exploration tasks between remote places via internet, using only brain activity. in operation, two teleoperation modes can be combined: robot navigation and camera exploration. in both modes, the user faces a real-time video captured by the robot camera merged with augmented reality items. in this representation, the user concentrates on a target area to navigate to or visually explore; then, a visual stimulation process elicits the neurological phenomenon that enables the brain-computer system to decode the intentions of the user. in the navigation mode, the target destination is transferred to the autonomous navigation system, which drives the robot to the desired place while avoiding collisions with the obstacles detected by the laser scanner. in the camera mode, the camera is aligned with the target area to perform an active visual exploration of the remote scenario. in june 2008, within the framework of the experimental methodology, five healthy subjects performed pre-established navigation and visual exploration tasks for one week between two cities separated by 260km. on the basis of the results, a technical evaluation of the device and its main functionalities is reported. the overall result is that all the subjects were able to successfully solve all the tasks reporting no failures, showing a high robustness of the system.
unified motion planning of passing under obstacles with humanoid robots. a motion planning method for humanoid robots to pass under obstacles is proposed. the proposed motion planner can calculate a goal configuration and connect it with an initial configuration in a collision-free dynamically stable motion. the method can generate not only a body motion but also the footstep sequence. the effectiveness of the proposed method was validated by experiments with the humanoid robot hrp- 2.
navigating by stigmergy: a realization on an rfid floor for minimalistic robots. stigmergy is a mechanism that allows the coordination of actions within the same agent or across different agents by means of traces left in the environment. we propose a stigmergetic approach to robot navigation in which a robot sets values in a hexagonal grid of rfid tags buried under the floor. this approach only requires minimal resources on the robot. the rfid floor will eventually contain a distance map that can guide the robot to a given goal (or set of goals) without the use of any localization system. the same map can be used or improved by other robots or by the same robot at later times. we define algorithms for building the rfid-floor map and for navigating on this map, we prove the convergence of the map building algorithm, and we show an empirical validation of our results using a small robot in a domestic environment.
a self-repairing structure for modules and its control by vibrating actuation mechanisms. in this study, we propose a self-repairing mechanism for modules. this mechanism has been developed by studying the self-repairing processes found in nature. proposed model is considered with the aid of energy given from the external environment. we investigate a self-repairing process using small modules actuated by vibration. we first describe the proposed self-repairing mechanism and its application to the control of a group of modules. we report the development of the vibrator used in this study and formulate the equation of translational and rotational motions of modules placed on this plate. the proposed mechanism is verified by carrying out numerical simulation and experiments. in addition, we introduce two simple structures based on the proposed mechanism.
evaluation of frequency band technique in estimating muscle fatigue during dynamic contraction task. long-time exposure to repetitive or monotonous work is associated with increased risk for musculoskeletal disorders that are due to muscle fatigue. previously, researchers reported that muscle fatigue can be estimated using a low-frequency band of an semg signal. however, these studies were performed exclusively during static contraction tasks. the objective of the present study was to evaluate and determine the frequency range for a low-frequency band. in addition, the performance during dynamic contraction tasks was analyzed. a group of healthy university students (8 males) was recruited, and endurance handgrip tasks were conducted. semg signals were captured from the forearm muscle. the frequency range for the low-frequency band was redefined as 5 - 40hz. the results from a dynamic contraction task indicated that a low-frequency band is a reliable method for indexing muscle fatigue from semg signals.
tracking groups of people with a multi-model hypothesis tracker. people in densely populated environments typically form groups that split and merge. in this paper we track groups of people so as to reflect this formation process and gain efficiency in situations where maintaining the state of individual people would be intractable. we pose the group tracking problem as a recursive multi-hypothesis model selection problem in which we hypothesize over both, the partitioning of tracks into groups (models) and the association of observations to tracks (assignments). model hypotheses that include split, merge, and continuation events are first generated in a data-driven manner and then validated by means of the assignment probabilities conditioned on the respective model. observations are found by clustering points from a laser range finder given a background model and associated to existing group tracks using the minimum average hausdorff distance. experiments with a stationary and a moving platform show that, in populated environments, tracking groups is clearly more efficient than tracking people separately. our system runs in real-time on a typical desktop computer.
motion control of impedance-type haptic devices. impedance type devices are frequently used in haptics due to their excellent rendition of free-space, low cost, and convenience. the spring drive, a recently proposed alternative to the traditional current motor driver for such devices, has been shown to improve their stiffness in rigid contact by moving the haptic coupling from the digital domain to analog circuitry. unlike the current drive, which operates the motor as a force source, the spring drive converts the motor to a motion source. in this paper we construct a motion-based virtual environment to fully leverage the benefits of operating an impedance device with motion control. this quasi-static environment is connected to a mid-level controller which interfaces with the analog motor drive. a full system analysis and experimental demonstration verify the anticipated performance.
development of a grip aid system using air cylinders. we have been developing a grip aid system using air cylinders to assist elderly who have weak grasping force to have daily living activities. the system is small and light weight. in this paper, mechanism to assist thumb, index, and middle fingers using one air cylinder for each finger, is proposed. and also control method of the device using a bending sensor to transfer their will to control the system is developed. finally much kind of objects are grasped with an assist of the system and the result shows a potential of the system.
the implementation of a novel, bio-inspired, robotic security system. the implementation of a robotic security solution generally requires one algorithm to route the robot around the environment and another algorithm to perform anomaly detection. solutions to the routing problem require the robot to have a good estimate of its own pose. we present a novel security system that uses metrics generated by the localisation algorithm to perform adaptive anomaly detection. the localisation algorithm is a vision-based slam solution called ratslam, based on mechanisms within the hippocampus. the anomaly detection algorithm is based on the mechanisms used by the immune system to identify threats to the body. the system is explored using data gathered within an unmodified office environment. it is shown that the algorithm successfully reacts to the presence of people and objects in areas where they are not usually present and is tolerised against the presence of people in environments that are usually dynamic.
using critical junctures and environmentally-dependent information for management of tightly-coupled cooperation in heterogeneous robot teams. this paper addresses the challenge of forming appropriate heterogeneous robot teams to solve tightly-coupled, potentially multi-robot tasks, in which the robot capabilities may vary over the environment in which the task is being performed. rather than making use of a permanent tightly-coupled robot team for performing the task, our approach aims to recognize when tight coupling is needed, and then only form tight cooperative teams at those times. this results in important cost savings, since coordination is only used when the independent operation of the team members would put mission success at risk. our approach is to define a new semantic information type, called environmentally dependent information, which allows us to capture certain environmentally-dependent perceptual constraints on vehicle capabilities. we define locations at which the robot team must transition between tight and weak cooperation as critical junctures. note that these critical juncture points are a function of the robot team capabilities and the environmental characteristics, and are not due to a change in the task itself. we calculate critical juncture points by making use of our prior asymtre approach, which can automatically configure heterogeneous robot team solutions to enable sharing of sensory capabilities across robots. we demonstrate these concepts in experiments involving a human-controlled blimp and an autonomous ground robot in a target localization task.
a robotic sentinel for benthic sampling along a transect. this paper presents the design of a novel robotic system capable of long-term benthic sampling along a transect. the robot is built to traverse back and forth along a mechanical guide-rail at the bottom of a water body. we present results from localization tests with the robot in a laboratory tank and a shallow swimming pool. a pilot deployment was made at a marina to accurately observe the rate and direction of water flow across a section of the marina inlet. results from the experiment demonstrate the potential of this platform for monitoring various aquatic phenomena of interest.
dynamic path planning with multi-agent data fusion - the parallel hierarchical replanner. the design of a hierarchical planning system in which each level operates in parallel and communicates asynchronously is presented. it is shown that this parallel hierarchical replanner is both reactive, and as close to optimal over all information in the state space as is possible given finite computational power. a comparison with three other hierarchical methods is presented, which demonstrates that for scenarios in which the time taken to achieve a mission goal is of greater importance than the cost incurred, this approach has better performance than related methods in the literature.
real-time face tracking and pose estimation with partitioned sampling and relevance vector machine. tracking the pose of human face has long been an important research topic which has many important applications, and it is particularly challenging with a monocular camera because the depth information is lost due to the perspective projection. this work adopts particle filter with partitioned sampling to decompose the state space of face pose tracking into two subspaces for increasing the sampling efficiency, thus achieving satisfactory performance with fewer particles. the parameters in the first subspace describe the target on image plane, and the parameter in the second subspace is used for the estimate of the face pose in yaw angle direction. for the evaluation of each hypothesis in the second subspace, a statistical learning algorithm called relevance vector machine (rvm) is used to map a face containing image to the pose of the face. the training of rvm is tailored to each detected frontal face, and it takes less than half second, which is suitable for a real-time application. the learning based regression model also presents the insensitive ability to expression variation and unmodeled degree of freedom. the experimental results verify that the combination of particle filter and rvm can efficiently reduce the processing time and add robustness to the performance of the system, thus making this algorithm applicable to human-machine interface with low-cost webcams.
learning 3-d object orientation from images. we propose a learning algorithm for estimating the 3-d orientation of objects. orientation learning is a difficult problem because the space of orientations is non-euclidean, and in some cases (such as quaternions) the representation is ambiguous, in that multiple representations exist for the same physical orientation. learning is further complicated by the fact that most man-made objects exhibit symmetry, so that there are multiple "correct" orientations. in this paper, we propose a new representation for orientations--and a class of learning and inference algorithms using this representation-- that allows us to learn orientations for symmetric or asymmetric objects as a function of a single image. we extensively evaluate our algorithm for learning orientations of objects from six categories.
3d tree reconstruction from laser range data. we present a method for reconstructing 3d models of tree branch structure from laser range data. our approach is probabilistic, and uses general knowledge of tree structure to guide an iterative reconstruction process. our goal is to recover parameters such as branch locations, angles, radii, and lengths, as well as connectivity information between branches. these parameters can then be fed into functional-structural plant models to study the relationships between the structure of a plant, its environment, and its internal biology. in this paper we present an algorithm for finding these parameters, and results on both simulated and real datasets.
environment adapted active multi-focal vision system for object detection. a biologically inspired foveated attention system in an object detection scenario is proposed. thereby, a high-performance active multi-focal camera system imitates visual behaviors such as scan, saccade and fixation. bottom-up attention uses wide-angle stereo data to select a sequence of fixation points in the peripheral field of view. successive saccade and fixation of high foveal resolution using a telephoto camera enables high accurate object recognition. once an object is recognized as target object, the bottom-up attention model is adapted to the current environment, using the top-down information extracted from this target object. the bottom-up attention model and the object recognition algorithm based on sift are implemented using cuda technology on graphics processing units (gpus), which highly accelerates image processing. in the experimental evaluation, all the target objects were detected in different backgrounds. evident improvements in accuracy, flexibility and efficiency are achieved.
transmission of tactile roughness through master-slave systems. in this study, a tactile-roughness transmission system applicable to master-slave systems with a communication time delay is developed. the master-side system constructs a local model of target objects placed in the slave-side environment. tactile feedbacks presented to an operator at the master side are produced by combining the physical properties of target objects in the local model and the kinetic information of the operator. the time delay between the operator's motion and the tactile feedback is cancelled because the stimuli are synchronized with the exploratory motions. the proposed system is applied to the transmission of tactile-roughness. the tactile stimuli presented to the operator are vibratory stimuli whose amplitude and frequency are controlled. these stimuli are locally synthesized by combining the surface wavelength of target objects and the operator's hand velocity. using the developed tactile-roughness transmission system, an experiment for transmitting the perceived roughness of grating scales was conducted. as a result, the roughness perceived by the operators was found to highly correlate with the roughness of the scales in the slave-side environment with a coefficient of 0.83.
a magnetic type tactile sensor using a two-dimensional array of inductors. a novel tactile sensor based on electromagnetic induction that can detect slippage in addition to three-axis force is proposed. the structure of this sensor is simple, and the sensor, which is essentially a displacement gauge, consists of a two-dimensional array of inductors and an elastic body containing a permanent magnet. formulas to transform the output voltage of the proposed sensor into a force vector are derived, and analyses of both deformation of the proposed structure and magnetic field generated by a cylindrical permanent magnet are carried out to extract transformation parameters. in addition, it is shown that the sensor sensitivity can be designed using the aspect ratio of the cylindrical magnet. a prototype unit-cell of a tactile sensor is developed with four chip inductors, silicon gel as an elastic body and a neodymium magnet, and experiments are undertaken with a six-axis force/torque sensor as a reference. the contributions of inductors to sensor output are observed experimentally to be in agreement with analytical prediction and the reference sensor. experimental results show that the proposed tactile sensor is useful for three-axis force measurement and slip detection.
distributed maximum a posteriori estimation for multi-robot cooperative localization. this paper presents a distributed maximum a posteriori (map) estimator for multi-robot cooperative localization (cl). as opposed to centralized map-based cl, the proposed algorithm reduces the memory and processing requirements by distributing data and computations amongst the robots. specifically, a distributed data-allocation scheme is presented that enables robots to simultaneously process and update their local data. additionally, a distributed conjugate gradient algorithm is employed that reduces the cost of computing the map estimates, while utilizing all available resources in the team and increasing robustness to single-point failures. finally, a computationally efficient distributed marginalization of past robot poses is introduced for limiting the size of the optimization problem. the communication and computational complexity of the proposed algorithm is described in detail, while extensive simulation studies are presented for validating the performance of the distributed map estimator and comparing its accuracy to that of existing approaches.
measurement of grasp position by human hands and grasp criterion for two soft-fingered robot hands. in this study, grasp positions of human hands are measured for planar objects for development of a reasonable grasp criterion for a two-soft-fingered robotic hand. we first propose a grasp criterion that yields the best grasping position for a robot hand with two soft fingers. to examine the properties of this criterion with respect to some weighting coefficients included in the criterion, a grasping experiment is performed by humans for planar objects with various sizes and shapes. results show that many observed grasping positions used by humans correspond to a set of weighting coefficients that equally weigh all the factors in the criterion. a grasping experiment using a robot has also been conducted using a set of coefficients corresponding to the equal weight; the effectiveness of the criterion was confirmed.
simultaneous task subdivision and allocation for teams of heterogeneous robots. in this paper, we present a negotiation protocol for simultaneous task subdivision and allocation for heterogeneous multi-robot systems. an abstraction of the concept of task is presented that allows to apply the protocol on a variety of tasks. the negotiation adopts rubinstein's alternate offers protocol, where offers are evaluated and generated using a heuristic search step. the protocol has been tested on computer simulations.
pdac-based underactuated 3d bipedal walking - stabilization of pdac constants and walking direction control -. this paper proposes a three-dimensional biped dynamic walking algorithm based on passive dynamic autonomous control (pdac) which is previously proposed. the robot dynamics is modeled as an autonomous system of a three-dimensional inverted pendulum by applying the pdac concept that is based on the assumption of point contact of the robot foot and the virtual constraint as to robot joints. due to autonomy, there are two conservative quantities named "pdac constant", that determine the velocity and direction of the biped walking. we also propose the convergence algorithm to make pdac constant converge to arbitrary value, so that walking velocity and direction is controllable. numerical simulation results validate proposed algorithm.
iclqg: combining local and global optimization for control in information space. when a mobile robot does not have perfect knowledge of its position, conventional controllers can experience failures such as collisions because the uncertainty of the position is not considered in choosing control actions. in this paper, we show how global planning and local feedback control can be combined to generate control laws in the space of distributions over position, that is, in information space. we give a novel algorithm for computing "information-constrained" linear quadratic gaussian (iclqg) policies for controlling a robot with imperfect state information. the iclqg algorithm uses the belief roadmap algorithm to efficiently search for a trajectory that approximates the globally-optimal motion plan in information space, and then iteratively computes a feedback control law to locally optimize the global approximation. the iclqg algorithm is not only robust to imperfect state information but also scalable to high-dimensional systems and environments. in addition, iclqg is capable of answering multiple queries efficiently. we demonstrate performance results for controlling a vehicle on the plane and a helicopter in three dimensions.
mobile robots with active ir-optical sensing for remote gas detection and source localization. while other robots use in-situ measurements for gas leak detection and localization, we propose to apply remote sensing. it is easier and safer to conduct, permits rapid scans and is applicable to leak sources high up. an ir-optical sensor is used, exploiting spectral absorption effects of gases. tailored leak detection and localization strategies are proposed. a simulation environment with a 3d model of the gas concentration field is used for developing and testing the detection and localization strategies. the system performance is demonstrated in a case study with a chemical plant.
optimal camera placement for total coverage. in this document, we study the problem of optimally placing a mixture of directional and omnidirectional cameras. in our solution, the workspace is represented by an occupancy grid map [1]. then, using surface-projected workspace and camera perception models, we develop a binary integer programming algorithm. the results of the algorithm are applied successfully to a variety of simulated scenarios.