waashk/dblp huggingface dataset:
prioritizing linear equality and inequality systems: application to local motion planning for redundant robots. we present a novel method for prioritizing both linear equality and inequality systems and provide one algorithm for its resolution. this algorithm can be summarized as a sequence of optimal resolutions for each linear system following their priority order. we propose an optimality criterion that is adapted to linear inequality systems and characterize the resulting optimal sets at every priority level. we have successfully applied our method to plan local motions for the humanoid robot hpr-2. we will demonstrate the validity of the method using an original scenario where linear inequality constraints are solved at lower priority than equality constraints.
conflict-evaluated maximum approximated likelihood approach for building grid maps using sonar sensors. in this paper, we address the problem of building a grid map using cheap sonar sensors, that is, the problem of using erroneous sensors when seeking to model an environment as accurately as possible. we rely on the inconsistency of information among sonar measurements and the sound pressure of the waves from the sonar sensors to develop a new method of detecting incorrect sonar readings, called the conflict evaluation with sound pressure (cesp). to fuse the correct measurements into a map, we start with the maximum likelihood approach due to its ability to manage the angular uncertainty of sonar sensors. however, since this approach suffers from heavy computational complexity, we convert it to a light logic problem called the maximum approximated likelihood (mal) approach. integrating the mal approach with the cesp method results in the conflict-evaluated maximum approximated likelihood (cemal) approach. the cemal approach generates a very accurate map that is close to the map that would be built by accurate laser sensors, and does not require adjustment of parameters for various environments.
from pixels to objects: enabling a spatial model for humanoid social robots. this work adds the concept of object to an existent low-level attention system of the humanoid robot icub. the objects are defined as clusters of sift visual features. when the robot first encounters an unknown object, found to be within a certain (small) distance from its eyes, it stores a cluster of the features present within an interval about that distance, using depth perception. whenever a previously stored object crosses the robot's field of view again, it is recognized, mapped into an egocentrical frame of reference, and gazed at. this mapping is persistent, in the sense that its identification and position are kept even if not visible by the robot. features are stored and recognized in a bottom-up way. experimental results on the humanoid robot icub validate this approach. this work creates the foundation for a way of linking the bottom-up attention system with top-down, object-oriented information provided by humans.
constructing action set from basis functions for reinforcement learning of robot control. continuous action sets are used in many reinforcement learning (rl) applications in robot control since the control input is continuous. however, discrete action sets also have the advantages of ease of implementation and compatibility with some sophisticated rl methods, such as the dyna [1]. however, one of the problem is the absence of general principles on designing a discrete action set for robot control in higher dimensional input space. in this paper, we propose to construct a discrete action set given a set of basis functions (bfs). we designed the action set so that the size of the set is proportional to the number of the bfs. this method can exploit the function approximator's nature, that is, in practical rl applications, the number of bfs does not increase exponentially with the dimension of the state space (e.g. [2]). thus, the size of the proposed action set does not increase exponentially with the dimension of the input space. we apply an rl with the proposed action set to a robot navigation task and a crawling and a jumping tasks. the simulation results demonstrate that the proposed action set has the advantages of improved learning speed, and better ability to acquire performance, compared to a conventional discrete action set.
tissue property estimation and graphical display for teleoperated robot-assisted surgery. manual palpation of tissue and organs during a surgical procedure provides clinicians with valuable information for diagnosis and surgical planning. in present-day robot-assisted minimally invasive surgery systems, lack of perceptible haptic feedback makes it challenging to detect a tumor in an organ or a calcified artery in heart tissue. this study presents an automated tissue property estimation method and a real-time graphical overlay that allow an operator to discriminate hard and soft tissues. we first evaluate experimentally the properties of an artificial tissue and compare seven possible mathematical tissue models. self-validation as well as cross-validation confirm that the hunt-crossley model best describes the experimentally observed phantom tissue properties and is suitable for our purpose. second, we present the development of a system in which the phantom tissue is palpated using a teleoperated surgical robot, and the stiffness of the hunt-crossly model is estimated in real time by recursive least squares. a real-time visual overlay representing tissue stiffness is created using a hue-saturation-luminance representation on a semitransparent disc at the tissue surface. hue depicts the stiffness at a palpated point and saturation is calculated based on distance from the point. a simple interpolation technique creates a continuous stiffness color map. in an experiment, the graphical overlay successfully shows the location of an artificial calcified artery hidden in phantom tissue.
constraint based world modeling in mobile robotics. in this paper we present a novel approach using constraint based techniques for world modeling, i.e. self localization and object modeling. within the last years, we have seen a reduction of landmarks such as beacons or colored goals within the robocup domain. using other features as line information becomes more important. using such sensor data is tricky, especially when the resulting position belief is stretched over a larger area. constraints can overcome this limitations, as they have several advantages: they can represent large distributions and are easy to store and to communicate to other robots. propagation of several constraints can be computationally cheap. even high dimensional belief functions can be used. we will describe a sample implementation and show experimental results.
a novel 1d trifocal tensor-based control for differential-drive robots. this paper presents an image-based approach to perform visual control for differential-drive robots. we use for the first time the elements of the 1d trifocal tensor directly in the control law. the visual control utilizes the usual teach-by showing strategy without requiring any a prior knowledge of the scene and does not need any auxiliary image. the main contribution of the paper is that the proposed two-steps control law ensures total correction of both position and orientation without switching to any other visual constraint rather than the 1d trifocal tensor. the paper exploits the sliding mode control technique in a square system, ensuring stability and robustness for the closed loop. the good performance of the control system is proven via simulations.
the dlr mirosurge - a robotic system for surgery. this video presents the in-house developed dlr mirosurge robotic system for surgery. as shown, the system is suitable for both minimally invasive and open surgery. essential part of the system is the miro robot: the soft robotics feature enables intuitive interaction with the robot. in the presented minimally invasive robotic setup three miros guide an endoscopic stereo camera and two endoscopic instruments with force feedback sensors. the master console for teleoperation consists of an autostereoscopic monitor and force reflecting input devices for both hands. versatility is shown with two additional applications: for assistance in manual minimally invasive surgery a miro robot automatically guides the endoscope such that the surgical instrument is always in view. in a biopsy application the miro robot is positioning the needle with navigation system support.
kinematic reconfigurability of mobile robots on irregular terrains. this paper considers the active control problem of reconfigurable mobile robots on irregular terrain. a kinematic control strategy to improve robot mobility (stability and traction) is proposed. the proposed control is validated through numerical simulations and experimental tests using an amphibious wheel-legged robot, named environmental hybrid robot, recently developed by petrobras s.a. (brazilian petroleum company) for environmental monitoring in the amazon rain forest.
autonomous driving in semi-structured environments: mapping and planning. we consider the problem of autonomous driving in semi-structured environments (e.g., parking lots). such environments have strong topological structure (graphs of drivable lanes), but maneuvers with significant deviations from those graphs are valid and frequent. we address two main challenges of operating in such environments: i) detection of topological structure from sensor data, and ii) using that structure to guide path planning. we present experimental results on both of these topics, demonstrating robust estimation of lane networks in parking lots and the benefits of using these topological networks to guide path planning.
most salient region tracking. in this paper, we introduce a cognitive approach for object tracking from a mobile platform. the approach is based on a biologically motivated attention system which is able to detect regions of interest in images based on concepts of the human visual system. a top-down guided visual search module of the system enables to especially favor features which fit to a previously learned target object. here, the appearance of an object is learned online within the first image in which it is detected. in subsequent images, the attention system searches for the target features and builds a top-down, target-related saliency map. this enables to focus on the most relevant features of especially this object in especially this scene without knowing anything about a particular object model or scene in advance. the system is able to operate in real-time and to cope with the requirements of real-world tasks such as illumination variations and other moving objects.
active guidance of a handheld micromanipulator using visual servoing. in microsurgery, a surgeon often deals with anatomical structures of sizes that are close to the limit of the human hand accuracy. robotic assistants can help to push beyond the current state of practice by integrating imaging and robot-assisted tools. this paper demonstrates control of a handheld tremor reduction micromanipulator with visual servo techniques, aiding the operator by providing three behaviors: snap-to, motion-scaling, and standoff-regulation. a stereo camera setup viewing the workspace under high magnification tracks the tip of the micromanipulator and the desired target object being manipulated. individual behaviors activate in task-specific situations when the micromanipulator tip is in the vicinity of the target. we show that the snap-to behavior can reach and maintain a position at a target with an accuracy of 17.5 ± 0.4µm root mean squared error (rmse) distance between the tip and target. scaling the operator's motions and preventing unwanted contact with non-target objects also provides a larger margin of safety.
minimalistic control of a compass gait robot in rough terrain. although there has been an increasing interest in dynamic bipedal locomotion for significant improvement of energy efficiency and dexterity of mobile robots in the real world, their locomotion capabilities are still mostly restricted on flat surfaces. the difficulty of dynamic locomotion in rough terrain is mainly originated in the stability and controllability of gait patterns while exploiting the natural mechanical dynamics of the robots. for a systematic investigation of the challenging problem, this paper presents the simplest control architecture for the compass gait model which can be used for locomotion in rough terrain. locomotion of the model is mainly achieved by an open-loop oscillator which induces self-stabilizing gait patterns, and we test the proposed control architecture in a real-world robotic platform. in addition, we also found that this controller is capable of varying stride length with a minimum change of control parameters, which enables locomotion in rough terrains. by using these basic principles of self-stability and gait variability, we extended the proposed controller with a simple sensory feedback about the location in the environment, which makes the robot possible to control gait patterns autonomously for traversing a rough terrain. we describe a set of experimental results and discuss how the proposed minimalistic control architecture can be enhanced for dynamic locomotion control in more complex environment.
modeling and control of the monopedal robot thumper. a hybrid controller that induces stable running gaits on a monopedal robot is developed. the robot features a rigid leg with a revolute knee and a heavy torso with center of mass located far from the hip. the torso houses a novel powertrain that provides series compliance in the compression direction of the leg. the proposed control law is developed within the hybrid zero dynamics framework and it acts on two levels. on the first level, continuous within-stride control asymptotically imposes (virtual) holonomic constraints reducing the dynamics of the robot to a lower-dimensional hybrid subsystem. on the second level, event-based control stabilizes the resulting hybrid subsystem. the controller achieves the dual objectives of working harmoniously with the system's natural dynamics and inducing provably exponentially stable running motions, while all relevant physical constraints are respected.
wireless reconfigurable modules for robotic endoluminal surgery. in this paper, a reconfigurable modular robotic system is proposed to augment the dexterity of endoluminal interventions in the gastrointestinal tract. in the proposed system, miniaturized robotic modules are ingested and assembled in the stomach cavity. the assembled robot can change its configuration according to the target location, thus enabling complicated surgical tasks. the robotic assembly, the robotic configuration and the surgical tasks are controlled via wireless bidirectional communication. based on this concept, early prototypes of the robotic modules were designed and fabricated. the developed module has 2dof (±90° of bending and 360° of rotation), measures 15.4 mm in diameter and 36.5 mm in length. it weighs 5.6 g and contains a li-po battery, two brushless dc motors, and a custom-made control board capable of wireless communication. the performance of the bending and rotational motion was evaluated and the future work has been discussed.
dynamic distributed intrusion detection for secure multi-robot systems. a general technique to build a dynamic and distributed intrusion detector for a class of multi-agent systems is proposed in this paper, by which misbehavior in the motion of one or more agents can be discovered. previous work from the authors has focused on how to distinguish the behavior of a misbehaving agent in a completely distributed way, by developing a solution where agents act as local monitors of their neighbors and use locally sensed information as well as data received from other monitors at a particular time. in this work, we improve the system detection capability by allowing monitors to use information collected at different instants and thus realizing a dynamic state observer that is valid for any system in the considered class. finally, we show through simulations the effectiveness of the proposed solution for a case study.
safe fall: humanoid robot fall direction change through intelligent stepping and inertia shaping. although fall is a rare event in the life of a humanoid robot, we must be prepared for it because its consequences are serious. in this paper we present a fall strategy which rapidly modifies the robot's fall direction in order to avoid hitting a person or an object in the vicinity. our approach is based on the key observation that during "toppling" the rotational motion of a robot necessarily occurs at the leading edge or the leading corner of its support base polygon. to modify the fall direction the robot needs to change the position and orientation of this edge or corner vis-a-vis the prohibited direction. we achieve it through intelligent stepping as soon as a fall is detected. we compute the optimal stepping location which results in the safest fall. additional improvement to the fall controller is achieved through inertia shaping techniques aimed at controlling the centroidal inertia of the robot. we demonstrate our results through the simulation of an asimo-like humanoid robot. to our knowledge, this is the first implementation of a controller that attempts to change the fall direction of a humanoid robot.
controller decomposition and combination design of body / motion elements based on orbit attractor. robot control systems consist of a feedback controller and reference motion pattern. they are designed based on the robot dynamics and coupled with each other, and it requires lots of calculation to obtain them. so far, we have proposed controller design method based on orbit attractor of nonlinear dynamics. because the controller yields one motion for one robot, we can assume that the controller includes information of motion and body elements. if those elements can be decomposed from the controller, a new controller can be easily designed by the combination of these elements. so in this paper, we propose the motion and body elements design method with lagrange's method of undetermined multipliers based on robot dynamics, and combination design method of a new controller using these elements. the effectiveness of the proposed method is evaluated by experiments with tapping dance robots.
developing a planning method for straight needle insertion using probability-based condition where a puncture occurs. needle insertion treatments require accurate placement of the needle tip into the target cancer. however, it is difficult to insert the needle accurately because of cancer displacement caused by organ deformation. therefore, a path planning using numerical simulation to analyze the deformation of the organ is important for accurate needle insertion. the problem in developing a planning method is that puncture conditions, such as the force applied to the needle, is difficult to be decided deterministically, because the experimental data of puncture conditions have variations. therefore, the purpose of this research was to develop a novel planning method to decide the robust paths of straight needle insertion for various puncture points. the basic idea of this planning method is to consider the puncture condition probabilistic and to evaluate the expected value of needle placement accuracy. first, a probability-based puncture condition was introduced, and then the expected value of needle placement accuracy was defined. next, the optimization method was developed to search the insertion path in a way that minimizes the expected values of needle placement accuracy. then, a numerical simulation and evaluation of the planning method was conducted, using a liver-shaped 2d model. furthermore, an in-vitro experiment was carried out to measure needle placement accuracy from the optimized path. experimental results show that the planning method realizes needle insertion with a mean accuracy of 1.5 mm.
robust hitting with dynamics shaping. the present paper proposes the motion planning based on "the dynamics shaping" for a robotic arm to hit the target robustly toward the desired direction, of which the concept is to shape the robot dynamics appropriately in order to accomplish the desired motion. according to the linear system theory, the positional error of the end-point converges onto near the singular vector corresponding to its maximum singular value of the output controllability matrix of the robotic arm. therefore, if we can control the direction of the singular vector by applying the dynamics shaping, we will be able to control the direction of the positional error of the end-effector caused by the disturbance. we propose a novel motion planning based on the dynamics shaping and verify numerically and experimentally that the robotic arm can robustly hit the target toward the desired direction with a simple open-loop control system even though the disturbance is applied.
inverse kinematics with closed form solutions for highly redundant robotic systems. this paper presents inverse position kinematics algorithms with real time capability for justin, a robotic system with high redundancy and many degrees of freedom. the combination of closed form solutions for parts of the kinematic chain embedded in a nonlinear equation solver is shown to be advantageous. the algorithms are evaluated with dlr's robot justin both in simulation and reality. calculation times of 1 ms are achieved, including various optimization criteria for redundancy resolution. in case only a single arm with 7 dof is considered, a fast calculation time of 250 µs is reached. with inclusion of an iterative step, reachability can be shown in more than 99% of the calculations regardless of the initial guess. the problem of weighting in multi-criteria optimization problems remains, though in the chosen approach the tool tip position is never compromised by other criteria due to the partially closed form solution. the presented algorithm can be applied to inverse position kinematics for all manipulators with serial or tree structure and redundant joints in case closed form solutions are available for parts of the kinematic chain.
on the use of inverse scaling in monocular slam. recent works have shown that it is possible to solve the simultaneous localization and mapping problem using an extended kalman filter and a single perspective camera. the principal drawback of these works is an inaccurate modeling of measurement uncertainties, which therefore causes inconsistencies in the filter estimations. a possible solution to proper uncertainty modeling is the unified inverse depth parametrization. in this paper we propose the inverse scaling parametrization that still allows an un-delayed initialization of features, while reducing the number of needed parameters and simplifying the measurement model. this novel approach allows a better uncertainty modeling of both low and high parallax features and reduces the likelihood of inconsistencies. experiments in simulation demonstrate that the use of the inverse scaling solution improves the performance of the monocular ekf slam filter when compared with the unified inverse depth approach; experiment on real data confirm the applicability of the idea.