waashk/dblp huggingface dataset:
spiral: a novel biologically-inspired algorithm for gas/odor source localization in an indoor environment with no strong airflow. this work describes the design and experimental results of an algorithm, designed to localize a gas source in an indoor environment with no strong airflow by using an autonomous agent. this condition exacerbates the patchiness and intermittency of odor distribution, typical of turbulent flows in the presence of strong mean flows. furthermore, no information about the wind can be used to detect the position of the source. in the approach proposed here, the robot moves along spirals. a spiral can be reset and a new one started, based on the information acquired about gas distribution. this enables the robot to get close to the ejecting source, without relying on airflow measurements. results from experiments are also described and discussed, to assess the efficiency of the proposed method.
recognition of human grasps by time-clustering and fuzzy modeling. in this paper, we address the problem of recognition of human grasps for five-fingered robotic hands and industrial robots in the context of programming-by-demonstration. the robot is instructed by a human operator wearing a data glove capturing the hand poses. for a number of human grasps, the corresponding fingertip trajectories are modeled in time and space by fuzzy clustering and takagi-sugeno (ts) modeling. this so-called time-clustering leads to grasp models using time as an input parameter and fingertip positions as outputs. for a sequence of grasps, the control system of the robot hand identifies the grasp segments, classifies the grasps and generates the sequence of grasps shown before. for this purpose, each grasp is correlated with a training sequence. by means of a hybrid fuzzy model, the demonstrated grasp sequence can be reconstructed.
fusion of 2d and 3d sensor data for articulated body tracking. in this article, we present an approach for the fusion of 2d and 3d measurements for model-based person tracking, also known as human motion capture. the applied body model is defined geometrically with generalized cylinders, and is set up hierarchically with connecting joints of different types. the joint model can be parameterized to control the degrees of freedom, adhesion and stiffness. this results in an articulated body model with constrained kinematic degrees of freedom. the fusion approach incorporates this model knowledge together with the measurements, and tracks the target body iteratively with an extended iterative closest point (icp) approach. generally, the icp is based on the concept of correspondences between measurements and model, which is normally exploited to incorporate 3d point cloud measurements. the concept has been generalized to represent and incorporate also 2d image space features. together with the 3d point cloud from a 3d time-of-flight (tof) camera, arbitrary features, derived from 2d camera images, are used in the fusion algorithm for tracking of the body. this gives complementary information about the tracked body, enabling not only tracking of depth motions but also turning movements of the human body, which is normally a hard problem for markerless human motion capture systems. the resulting tracking system, named voodoo is used to track humans in a human-robot interaction (hri) context. we only rely on sensors on board the robot, i.e. the color camera, the tof camera and a laser range finder. the system runs in realtime (~20 hz) and is able to robustly track a human in the vicinity of the robot.
decentralised decision making in heterogeneous teams using anonymous optimisation. this paper considers the scenario where multiple autonomous agents must cooperate in making decisions to minimise a continuous and differentiable team cost function. a distributed and asynchronous optimisation algorithm is presented which allows each agent to incrementally refine their decisions while intermittently communicating with the rest of the team. a convergence analysis provides quantitative requirements on the frequency agents must communicate that is prescribed by the structure of the decision problem. in general the solution method will require every agent to communicate to and have a model of every other agent in the team. to overcome this, a specific subset of systems, called partially separable, is defined. these systems only require each agent to have a combined summary of the rest of the team and allows each agent to communicate locally over an acyclic communication network, greatly increasing the scalability of the system.
kick it with elasticity: safety and performance in human-robot soccer. the robocup community has one definite goal [h. kitano, m. asada, robocup humanoid challenge: that's one small step for a robot, one giant leap for mankind, in: ieee/rsj int. conf. on intelligent robots and systems, iros1998, victoria, pp. 419-424, 1998]: winning against the human world soccer champion team by the year 2050. this implies real tackles and fouls between humans and robots, rising safety concerns for the robots and even more important for the human players. nowadays, similar questions are discussed in the field of physical human-robot interaction (phri), but mainly in the context of industrial and service robotics applications. the first part of our paper is an attempt for a phri view on human-robot soccer. we take scenes from real soccer matches and discuss what could have happened if one of the teams consisted of robots instead of humans. the most important result is that elastic joints are needed to reduce the impact during collisions. the second and third part consider conversely, how the robot can handle the impact of kicking the ball and how it can reach the velocity of human-level soccer. again joint elasticity is the key point. overall, the paper analyzes a vision far ahead. however, all our conclusions are based on concrete simulations, experiments, derivations, or findings from sports science, forensics, and phri.
a variational approach to path planning for hyper-redundant manipulators. motion planning for hyper-redundant manipulators in a complicated and cluttered workspace is a challenging problem. many of the path planning algorithms, based on cell decomposition or potential field, fail due to the high dimensionality and complex nature of the c-space. probabilistic roadmap methods (prm) which have been proven to be successful in high dimensional c-spaces suffer from the drawback of generating paths which involve a lot of redundant motion. in this paper, we propose a path optimizing method to improve a given path in terms of path length and the safety against the collisions, using a variational approach. the capability of variational calculus to optimize a path is demonstrated on a variety of examples. the approach succeeds in providing a good quality path even in high dimensional c-spaces.
holonomy in mobile robots. the search for a simple and accurate odometry is a main concern when working with mobile robots. this article presents a general analysis of the problem and proposes a particular solution to improve the odometry. the three crucial kinematical aspects of mobile robots (mobility, control, and positioning) are reviewed in detail for vehicles based both in conventional and in omnidirectional wheels. the latter case is more suitable from a maneuvering point of view as it provides the robot frame with the three degrees of freedom (dof) of plane motion without singular configurations. moreover, a suitable design of the omnidirectional wheels leads to a strictly invariant jacobian matrix and thus to a linear control equation with constant coefficients. it is shown that such vehicles may have a holonomic behavior when moving under suitable kinematical restrictions without constraining their trajectory. in that case, the odometry is algebraic (instead of integrative) and thus more accurate. an application case is presented.
using probabilistic reasoning over time to self-recognize. using the probabilistic methods outlined in this paper, a robot can learn to recognize its own motor-controlled body parts, or their mirror reflections, without prior knowledge of their appearance. for each item in its visual field, the robot calculates the likelihoods of each of three dynamic bayesian models, corresponding to the categories of ''self'', ''animate other'', or ''inanimate''. each model fully incorporates the object's entire motion history and the robot's whole motor history in constant update time, via the forward algorithm. the parameters for each model are learned in an unsupervised fashion as the robot experiments with its arm over a period of four minutes. the robot demonstrated robust recognition of its mirror image, while classifying the nearby experimenter as ''animate other'', across 20 experiments. adversarial experiments, in which a subject mirrored the robot's motion showed that as long as the robot had seen the subject move for as little as 5 s before mirroring, the evidence was ''remembered'' across a full minute of mimicry.
pure reactive behavior learning using case based reasoning for a vision based 4-legged robot. a traditional problem in robotics is adaptation of developed algorithms to different platforms and sensors, as each of them has its specifics and associated errors. hierarchical control architectures deal with the problem through division of the system into layers, where deliberative processing is performed at high level and low level layers are in charge of dealing with reactive behaviors and adaptation to platform and sensor hardware. specifically, approaches based on the emergent behavior theory rely on building high level behaviors by combining simpler ones that provide intuitive reactive responses to sensory instance. this combination is controlled by higher layers in order to obtain more complex behaviors. unfortunately, low level behaviors might be difficult to develop, specially when dealing with legged robots and sensors like video cameras, where resulting motion is heavily influenced by the robot kinematics and dynamics and sensory input is affected by external conditions, transformations, distortions, noise and motion itself (e.g. the camera bouncing problem). in this paper, we propose a new learning based method to solve most of these problems. it basically consists of creating a reactive behavior by supervisedly driving a robot for a time. during that time, its visual input is reactively associated to commands sent to the robot through a case based reasoning (cbr) behavior builder. thus, the robot learns what the person would do in its situation to achieve a certain goal. this approach has two advantages. first, humans are particularly good at adapting and taking into account the specifics of a given mobile after some use. thus, kinematics and dynamics are absorbed into the casebase along with how the person thinks they should be dealt with by that particular robot. similarly, commands are associated to the input sensor as is, so systematic errors in sensors and motors are also implicitly learnt in the casebase (camera bouncing, distorsions, noise ...). also, different reactive strategies to reach a simple goal can be programmed into the robot by showing, rather than by coding. this is particularly useful because some reactive behaviors are ill-fitted to equations. naturally, cbr allows online adaptation to potential changes after supervised training, so the system is able to learn by itself when working autonomously too. the proposed system has been successfully tested in a 4-legged aibo robot in a controlled environment. to prove that it is adequate to create low level layers for hybrid architectures, two different cbr reactive behaviors have been tested and combined into an emergent one. a deliberative layer could be used to extent the system to more complex environments.
robust visual tracking control system of a mobile robot based on a dual-jacobian visual interaction model. this paper presents a novel design of a robust visual tracking control system, which consists of a visual tracking controller and a visual state estimator. this system facilitates human-robot interaction of a unicycle-modeled mobile robot equipped with a tilt camera. based on a novel dual-jacobian visual interaction model, a robust visual tracking controller is proposed to track a dynamic moving target. the proposed controller not only possesses some degree of robustness against the system model uncertainties, but also tracks the target without its 3d velocity information. the visual state estimator aims to estimate the optimal system state and target image velocity, which is used by the visual tracking controller. to achieve this, a self-tuning kalman filter is proposed to estimate interesting parameters and to overcome the temporary occlusion problem. furthermore, because the proposed method is fully working in the image space, the computational complexity and the sensor/camera modeling errors can be reduced. experimental results validate the effectiveness of the proposed method, in terms of tracking performance, system convergence, and robustness.
an experimental study of distributed robot coordination. coordinating the path of multiple robots along assigned paths is a computationally hard problem with great potential for applications. we here provide a detailed experimental study of a randomized algorithm for scheduling priorities that we have developed, and also compare it with exact and approximated solutions. it turns out that for problems of reasonable size our algorithm exhibits an appealing compromise between speed and quality.
learning to fall: designing low damage fall sequences for humanoid soccer robots. a methodology for the analysis and design of fall sequences of robots that minimize joint/articulation injuries, and the damage of valuable body parts is proposed. these fall sequences can be activated/triggered by the robot in case of a detected unintentional fall or an intentional fall, which are common events in humanoid soccer environments. the methodology is human-based and requires the use of a realistic simulator as development tool. the obtained results show that fall sequences designed using the proposed method produce less damage than standard, uncontrolled falls.
color learning and illumination invariance on mobile robots: a survey. recent developments in sensor technology have made it feasible to use mobile robots in several fields, but robots still lack the ability to accurately sense the environment. a major challenge to the widespread deployment of mobile robots is the ability to function autonomously, learning useful models of environmental features, recognizing environmental changes, and adapting the learned models in response to such changes. this article focuses on such learning and adaptation in the context of color segmentation on mobile robots in the presence of illumination changes. the main contribution of this article is a survey of vision algorithms that are potentially applicable to color-based mobile robot vision. we therefore look at algorithms for color segmentation, color learning and illumination invariance on mobile robot platforms, including approaches that tackle just the underlying vision problems. furthermore, we investigate how the inter-dependencies between these modules and high-level action planning can be exploited to achieve autonomous learning and adaptation. the goal is to determine the suitability of the state-of-the-art vision algorithms for mobile robot domains, and to identify the challenges that still need to be addressed to enable mobile robots to learn and adapt models for color, so as to operate autonomously in natural conditions.
sensor-based guidance control of a continuum robot for a semi-autonomous colonoscopy. due to their compliance and high dexterity, biologically-inspired continuum robots have attracted much interest for applications such as medical surgery, urban search and rescue, de-mining etc. in this paper, we will present an application to medical surgery-colonoscopy by designing a pneumatic-driven flexible robotic manipulator, called colobot. the focus of this paper lies in the sensor-based position control of the colobot for guiding the advancement in a tubular, compliant and slippery environment. the kinematic model related the position of the distal end of the colobot to the actuator inputs which is firstly developed and formulated to control the shape of the colobot through position control of the distal tip. to achieve safe guidance, the ideal position of the tip should be in the central axis of the colon. a method based on a circumscribed circle is proposed to approximate the central position in real-time based on three sensor readings. this position will be used as reference position for the tip to adjust its shape in real time to avoid the contact with tube wall. this proposed approach can be extended to the control of continuum robots in the conditions of a dynamically confined space. the simulation results and experimental results with a curved tube will be presented in order to validate the proposed control strategy.
behavioral control through evolutionary neurocontrollers for autonomous mobile robot navigation. this paper deals with the study of scaling up behaviors in evolutive robotics (er). complex behaviors were obtained from simple ones. each behavior is supported by an artificial neural network (ann)-based controller or neurocontroller. hence, a method for the generation of a hierarchy of neurocontrollers, resorting to the paradigm of layered evolution (le), is developed and verified experimentally through computer simulations and tests in a khepera^^^(r) micro-robot. several behavioral modules are initially evolved using specialized neurocontrollers based on different ann paradigms. the results show that simple behaviors coordination through le is a feasible strategy that gives rise to emergent complex behaviors. these complex behaviors can then solve real-world problems efficiently. from a pure evolutionary perspective, however, the methodology presented is too much dependent on user's prior knowledge about the problem to solve and also that evolution take place in a rigid, prescribed framework. mobile robot's navigation in an unknown environment is used as a test bed for the proposed scaling strategies.
a new paradigm of humanoid robot motion programming based on touch interpretation. most humanoid soccer robot teams design the basic movements of their robots, like walking and kicking, off-line and manually. once these motions are considered satisfactory, they are stored in the robot's memory and played according to a high level behavioral strategy. much time is spent in the development of the movements, and despite the significant progress made in humanoid soccer robots, the interfaces employed for the development of motions are still quite primitive. in order to accelerate development, an intuitive instruction method is desired. we propose the development of robot motions through physical interaction. in this paper we propose a ''teaching by touching'' approach; the human operator teaches a motion by directly touching the robot's body parts like a dance instructor. teaching by directly touching is intuitive for instructors. however, the robot needs to interpret the instructor's intention since tactile communication can be ambiguous. this paper presents a method to learn the interpretation of the touch meaning and investigates, through experiments, a general (shared among different users) and intuitive touch manner.
behavior transition between biped and quadruped walking by using bifurcation. in this research, we focus our discussion on the discontinuous dynamic behavior transition of a walking robot. for the transition, bifurcation of potential function is utilized. we demonstrate the behavior transition of the walking robot between biped and quadruped locomotion as an adaptation to the environment depending on the gradient of a slope. the behavior transition is carried out by using bifurcation of parameters used for control. we also show the effectiveness of hysteresis in the transition.
reference governor for constrained systems with time-varying references. this paper proposes constructing a reference governor for constrained linear systems with time-varying references. the main feature of the constructed reference governor is to simultaneously consider fulfillment of state and control constraints, as well as tracking performance by appropriately managing the reference to be inputted. to achieve constraint fulfillment and to evaluate tracking performance, the reference management is reduced into a convex quadratic programming problem using the concept of a maximal output admissible set. the reference governor is finally obtained in the form of a piecewise affine function of state and reference variables by means of a multi-parametric programming technique. in addition, the effectiveness of the reference governor is demonstrated by numerical and experimental examples of a practical dc position servomechanism with the control constraint.
development of adaptive modular active leg (amal) using bipedal robotics technology. the objective of the work presented here is to develop a low cost active above knee prosthetic device exploiting bipedal robotics technology which will work utilizing the available biological motor control circuit properly integrated with a central pattern generator (cpg) based control scheme. the approach is completely different from the existing active prosthetic devices, designed primarily as standalone systems utilizing multiple sensors and embedded rigid control schemes. in this research, first we designed a fuzzy logic based methodology for offering suitable gait pattern for an amputee, followed by formulating a suitable algorithm for designing a cpg, based on rayleigh's oscillator. an indigenous probe, humanoid gait oscillator detector (hgod) has been designed for capturing gait patterns from various individuals of different height, weight and age. these data are used to design a fuzzy inference system which generates most suitable gait pattern for an amputee. the output of the fuzzy inference system is used for designing a cpg best suitable for the amputee. we then developed a cpg based control scheme for calculating the damping profile in real time for maneuvering a prosthetic device called amal (adaptive modular active leg). also a number of simulation results are presented which show the stable behavior of knee and hip angles and determine the stable limit cycles of the network.
an appearance-based visual compass for mobile robots. localization is one of the most important basic skills of a mobile robot. most approaches, however, still rely either on special sensors or require artificial environments. in this article, a novel approach is presented that can provide compass information for localization, purely based on the visual appearance of a room. a robot using such a visual compass can quickly learn a cylindrical map of the environment, consisting of simple statistical features that can be computed very quickly. the visual compass algorithm is efficient, scalable and can therefore be used in real-time on almost any contemporary robotic platform. extensive experiments on a sony aibo robot have validated that the approach works in a vast variety of environments.