waashk/dblp huggingface dataset:
a combined stochastic and greedy hybrid estimation capability for concurrent hybrid models with autonomous mode transitions. probabilistic hybrid discrete/continuous models, such as concurrent probabilistic hybrid automata (cpha) are convenient tools for modeling complex robotic systems. in this paper, we present a novel method for estimating the hybrid state of cpha that achieves robustness by balancing greedy and stochastic search. to accomplish this, we (1) develop an efficient stochastic sampling approach for cpha based on rao-blackwellised particle filtering, (2) perform an empirical comparison of the greedy and stochastic approaches to hybrid estimation and (3) propose a strategy for mixing stochastic and greedy search. the resulting method handles nonlinear dynamics, concurrently operating components and autonomous mode transitions. we demonstrate the robustness of the mixed method empirically.
hardware design of high performance miniature anthropomorphic robots. technical issues in designing reliable high-performance miniature humanoid robots are discussed. although the light-weight and small-size body of such a humanoid robot facilitates safer and smoother experiments of agile motions involving large accelerations and impacts, building a complex humanoid system in a small body is still a challenging problem. simultaneous requirements for wider motion ranges, higher rigidity, less fragile electric devices and circuits, and less sensitive electric wiring should be satisfied in a limited space. in order to meet them by overcoming the difficulties, we propose to mechanically modularize joint structures, and to equip a centralized electric control unit involving pc boards, sensing devices, signal-processors, communication boards, and power amplifiers. the developments of the mechanical modules and the centralized unit are made for two miniature humanoid robots.
control hardware integration of a biped humanoid robot with an android head. the kaist hubo team and the hanson robotics team jointly developed an android-type humanoid robot termed albert hubo, which may be the world's first robot that incorporates an expressive human face on a walking biped robot. albert hubo adopts the techniques of the hubo design for the body of albert hubo and uses technology from hanson robotics for the head. the head and the body are two independent systems that use different computers to control each system. the head uses rc servo motors for facial expressions. it is controlled using a pc that utilizes rs232 communication. the head pc also processes vocal and visual information. the body (including the arms, fingers, legs, and torso) pc controls walking and body movements. it is connected via can communication to body servo controllers. the pcs are connected by rs232, which is able to transfer command data to each system. the height and weight of albert hubo are 137 cm and 57 kg, respectively. the robot has 66 dofs (31 for the head motions and 35 for the body motions). the head part uses a material known as 'frubber', which serves as a smooth human-like skin. in the head, 28 servo motors for facial movements and three servo motors for neck movements are used to generate a full range of facial expressions; thus, the robot is able to laugh, act surprised, and express sadness or anger. the body is modified from hubo (khr-3), which was introduced in 2004, and is joined with the head of albert hubo with 35 dc motors that are embedded for the purpose of imitating various human-like body motions. the biped humanoid robot with a human-like android head was realized by integrating two independent systems as a self-contained robotic system. the integration of the two systems is in the topic of this paper.
when hard realtime matters: software for complex mechatronic systems. a still growing number of software concepts and frameworks have been proposed to meet the challenges in the development of more and more complex robotic systems, like humanoids or networked robotics. the issue of hard realtime, however, has not been the main focus of such concepts, but is essential for building and controlling mechatronic systems. here we discuss the specific demands of complex mechatronic systems and present a software concept, the ''agile robot development'' (ard) concept, which we developed at our institute to pragmatically address these demands. we show that the performance of current computing and communication hardware allows for a flexible component-based concept with distributed execution, even in hard realtime with rates in the khz range.
theoretical foundations for rendezvous of glowworm-inspired agent swarms at multiple locations. we present theoretical foundations for a variation of the multi-agent rendezvous problem involving design of local control strategies that enable agent swarms, with hard-limited sensing ranges, to split into disjoint subgroups, exhibit simultaneous taxis behavior toward, and eventually rendezvous at, multiple unknown locations of interest. the algorithm used to solve the above problem is based on a glowworm swarm optimization (gso) technique, developed earlier, that finds multiple optima of multi-modal objective functions. we characterize the various phases of the algorithm that help us to develop a theoretical framework required for analysis. in particular, we show through simulations that the implementation of the gso algorithm at the individual agent level gives rise to two major phases at the group level-splitting of the agent-swarm into subgroups and local convergence of agents in each subgroup to the peak locations. we provide local convergence results under certain restricted set of assumptions, leading to a simplified model of the algorithm, making it amenable to analysis, while still reflecting most of the features of the original algorithm. in particular, we find an upper bound on the time taken by the agents to converge to an isolated leader and on the time taken by the agents to converge to one of the leaders with non-isolated and non-overlapping neighborhoods. finally, we show that agents under the influence of multiple leaders with overlapping neighborhoods asymptotically converge to one of the leaders. we present some illustrative simulations to support the theoretical findings of the paper.
conflict-free container routing in mesh yard layouts. container terminals play an important role in global cargo transportation and they have become an essential intermodal interface between the sea and the land. in the container terminal, the service area is often arranged into rectangular blocks, which leads to a mesh-like path topology. we present a mathematical model for general container routing in mesh yard layouts. based on this model, a simple container routing algorithm guaranteeing freedom of conflicts is then presented. the algorithm works by carefully choosing suitable containers' speeds such that the containers using the same junction will arrive at different points in time, and hence incur no conflicts; meanwhile, high routing performance can be achieved. the task completion time and the requirements on timing control during the container routing are also presented. numerical results verify that our routing scheme has good performance and is free of conflicts.
a dialogue approach to learning object descriptions and semantic categories. acquiring new knowledge through interactive learning mechanisms is a key ability for humanoid robots in a natural environment. such learning mechanisms need to be performed autonomously, and through interaction with the environment or with other agents/humans. in this paper, we describe a dialogue approach and a dynamic object model for learning semantic categories, object descriptions, and new words acquisition for object learning and integration with visual perception for grounding objects in the real world. the presented system has been implemented and evaluated on the humanoid robot armar iii.
bio-mimetic impedance control of robotic manipulator for dynamic contact tasks. a human performs a variety of skillful movements by adjusting dynamic characteristics of his or her musculoskeletal system according to a task involved. such characteristics of human movements can be described by mechanical impedance parameters. if the regulation mechanism of human impedance properties during the task can be clarified and modeled, there is a possibility that human skillful strategies can be integrated into robot motion control. this paper investigates human hand impedance in preparation for task operations, the so-called ''task-readiness impedance'', in a virtual ball-catching task. it further discusses a bio-mimetic impedance control of robotic manipulators for contact tasks via computer simulations using measured task-readiness impedance.
what can be done with an embedded stereo-rig in urban environments? the development of the autonomous guided vehicles (agvs) with urban applications are now possible due to the recent solutions (darpa grand challenge) developed to solve the simultaneous localization and mapping (slam) problem: perception, path planning and control. for the last decade, the introduction of gps systems and vision have been allowed the transposition of slam methods dedicated to indoor environments to outdoor ones. when the gps data are unavailable, the current position of the mobile robot can be estimated by the fusion of data from odometer and/or inertial navigation system (ins). we detail in this article what can be done with an uncalibrated stereo-rig, when it is embedded in a vehicle which is going through urban roads. the methodology is based on features extracted on planes: we mainly assume the road at the foreground as the plane common to all the urban scenes but other planes like vertical frontages of buildings can be used if the features extracted on the road are not enough relevant. the relative motions of the coplanar features tracked with both cameras allow us to estimate the vehicle ego-motion with a high precision. futhermore, the features which don't check the relative motion of the considered plane can be assumed as obstacles.
towards semantic maps for mobile robots. intelligent autonomous action in ordinary environments calls for maps. 3d geometry is generally required for avoiding collision with complex obstacles and to self-localize in six degrees of freedom (6 dof) (x, y, z positions, roll, yaw, and pitch angles). meaning, in addition to geometry, becomes inevitable if the robot is supposed to interact with its environment in a goal-directed way. a semantic stance enables the robot to reason about objects; it helps disambiguate or round off sensor data; and the robot knowledge becomes reviewable and communicable. the paper describes an approach and an integrated robot system for semantic mapping. the prime sensor is a 3d laser scanner. individual scans are registered into a coherent 3d geometry map by 6d slam. coarse scene features (e.g., walls, floors in a building) are determined by semantic labeling. more delicate objects are then detected by a trained classifier and localized. in the end, the semantic maps can be visualized for human inspection. we sketch the overall architecture of the approach, explain the respective steps and their underlying algorithms, give examples based on a working robot implementation, and discuss the findings.
solving the potential field local minimum problem using internal agent states. we propose a new, extended artificial potential field method, which uses dynamic internal agent states. the internal states are modeled as a dynamical system of coupled first order differential equations that manipulate the potential field in which the agent is situated. the internal state dynamics are forced by the interaction of the agent with the external environment. local equilibria in the potential field are then manipulated by the internal states and transformed from stable equilibria to unstable equilibria, allowing escape from local minima in the potential field. this new methodology successfully solves reactive path planning problems, such as a complex maze with multiple local minima, which cannot be solved using conventional static potential fields.
combining declarative, procedural, and predictive knowledge to generate, execute, and optimize robot plans. one of the main challenges in motor control is expressing high-level goals in terms of low-level actions. to do so effectively, motor control systems must reason about actions at different levels of abstraction. grounding high-level plans in low-level actions is essential semantic knowledge for plan-based control of real robots. we present a robot control system that uses declarative, procedural and predictive knowledge to generate, execute and optimize plans. declarative knowledge is represented in pddl, durative actions constitute procedural knowledge, and predictive knowledge is learned by observing action executions. we demonstrate how learned predictive knowledge enables robots to autonomously optimize plan execution with respect to execution duration and robustness in real-time. the approach is evaluated in two different robotic domains.
path planning for laser scanning with an industrial robot. reverse engineering is concerned with the problem of creating cad models of real objects by measuring point data from their surfaces. current solutions either require manual interaction or expect the nature of the objects to be known. we believe that in order to create a fully automatic system for re of unknown objects the software that creates the cad-model should be able to control the operation of the measuring system. this paper is based on a real implementation of a measuring system controlled by cad software, capable of measuring along curved paths. some details of the system have been described in earlier publications. this paper is concerned with the problem of automatic path planning for a system that can move along curved paths.
motion intention recognition in robot assisted applications. acquiring, representing and modelling human skills is one of the key research areas in teleoperation, programming-by-demonstration and human-machine collaborative settings. the problems are challenging mainly because of the lack of a general mathematical model to describe human skills. one of the common approaches is to divide the task that the operator is executing into several subtasks or low-level subsystems in order to provide manageable modelling. in this paper we consider the use of a layered hidden markov model (lhmm) to model human skills. we evaluate a gesteme classifier that classifies motions into basic action-primitives, or gestemes. the gesteme classifiers are then used in a lhmm to model a teleoperated task. the proposed methodology uses three different hmm models at the gesteme level: one-dimensional hmm, multi-dimensional hmm and multi-dimensional hmm with fourier transform. the online and off-line classification performance of these three models is evaluated with respect to the number of gestemes, the influence of the number of training samples, the effect of noise and the effect of the number of observation symbols. we also apply the lhmm to data recorded during the execution of a trajectory tracking task in 2d and 3d with a mobile manipulator in order to provide qualitative as well as quantitative results for the proposed approach. the results indicate that the lhmm is suitable for modelling teleoperative trajectory-tracking tasks and that the difference in classification performance between one and multidimensional hmms for gesteme classification is small. it can also be seen that the lhmm is robust with respect to misclassifications in the underlying gesteme classifiers.
a visual 3d-tracking and positioning technique for stereotaxy with ct scanners. in this paper, we present a 3d-tracking technique inspired by visual servoing, specifically designed for computed tomography (ct). this work has been developed within the framework of robot- and computer-assisted interventional radiology, using stereotactic external fiducials made of radiopaque rods. these fiducials produce a set of image feature points that are used in a pose estimation algorithm, with only one slice. the patient's movements can then be tracked with the proposed algorithm by means of a motion field approach, so as to update the 2d/3d registration. therefore, the proposed method solves a fundamental safety issue associated to the robotic assistance of ct-guided interventions. the contributions of the paper are threefold. first, the stereotactic visual feedback is modelled using the plucker representation for 3d straight lines, while the ct plane slice provides corresponding image points. it is shown that the number of features needed to compute the pose is minimal compared to the known previous techniques. second, the jacobian matrix which relates the image points displacements to the velocity screw of the stereotactic frame is computed, providing the ct motion field. third, the update of the jacobian matrix is investigated. it requires the on-line stereotactic registration. as with the ct imaging modality, the 2d/3d registration is highly inaccurate when the fiducials are being moving, this paper provides a ct visual 3d tracking method inspired by the image-based visual servoing, which may alleviate the jacobian matrix updates. to validate this technique, we first present simulations of the ct visual tracking. finally, the proposed method is applied to real images obtained with stereotactic markers mounted on a robotic platform and placed in a ct scanner.
integration of planning and execution in force controlled compliant motion. this paper presents the compliant task generator, a new approach for the automatic conversion of a geometrical contact path into a force-based task specification. a contact path planner generates a sequence of six-dimensional poses and corresponding contact formations, while a hybrid robot controller expects a desired wrench, twist and the local wrench and twist subspaces. our approach automatically converts a geometrical path description into a force based tasks specification for the hybrid controller, based on a user specified input of the magnitudes and the norms of the desired contact force and execution speed. the approach applies to all contact motions between known polyhedral objects, and is verified in real world experiments.
two-phase discontinuous gaits for quadruped walking machines with a failed leg. a fault-tolerant gait of multi-legged robots with static walking is a gait which can maintain gait stability and continue its walking against an occurrence of a leg failure. this paper proposes two-phase discontinuous gaits as a new fault-tolerant gait for quadruped robots suffering from a locked joint failure, which prevents a joint of a leg from moving and makes it locked in a known place. by comparing with previously developed one-phase discontinuous gaits, it is shown that the proposed gait has great advantages in gait performance such as the stride length and terrain adaptability. as an application of the proposed gait, the gait algorithm for ditch crossing and avoidance of quadruped robots with a failed leg is presented and illustrated by simulated figures.
logic-based robot control in highly dynamic domains. in this paper, we present the robot programming and planning language readylog, a golog dialect, which was developed to support the decision making of robots acting in dynamic real-time domains, such as robotic soccer. the formal framework of readylog, which is based on the situation calculus, features imperative control structures such as loops and procedures, allows for decision-theoretic planning, and accounts for a continuously changing world. we developed high-level controllers in readylog for our soccer robots in robocup's middle-size league, but also for service robots and for autonomous agents in interactive computer games. for a successful deployment of readylog on a real robot it is also important to account for the control problem as a whole, integrating the low-level control of the robot (such as localization, navigation, and object recognition) with the logic-based high-level control. in doing so, our approach can be seen as a step towards bridging the gap between the fields of robotics and knowledge representation.
an interactive tool for mobile robot motion planning. this paper presents an interactive tool aimed at facilitating the understanding of several well-known algorithms and techniques involved in solving mobile robot motion problems. these range from those modelling the mechanics of mobility to those used in navigation. the tool focuses on describing these problems in a simple manner in order to be useful for education purposes among different disciplines. by highlighting interactivity, the tool provides a novel means to study robot motion planning ideas in a manner that enhances full understanding. furthermore, the paper discuses how the tool can be used in an introductory course of mobile robotics.
action evaluation for mobile robot global localization in cooperative environments. this work is about solving the global localization issue for mobile robots, operating in large and cooperative environments. it tackles the problem of estimating the pose of a robot, or team of robots in a map reference frame, given the map, the real-time data from the robot onboard sensors, and the real-time data coming from other robots or sensors in the environment. after a first step of position hypotheses generation, an efficient probabilistic active strategy selects an action, for a single lost robot case, or two joint actions when two lost robots are in a line of sight, so that the hypotheses set is best disambiguated. the action set is adapted to the multi-hypothesis situation, and action evaluation takes into account remote observations available in robot network systems. this paper presents the theoretical formulation for both non-cooperative, and cooperative cases. an implementation of the proposed strategy is discussed, and simulation results presented.
location of legged robots in outdoor environments. knowledge of a robot's position with an accuracy of within a few centimeters is required for potential applications for legged robots, such as humanitarian de-mining tasks. individual sensors are unable to provide such accuracy. thus information from various sources must be used to accomplish the tasks. following this trend, this paper describes the method developed for estimating the position of legged robots in outdoor environments. the proposed method factors in the specific features of legged robots and combines dead-reckoning estimation with data provided by a differential global positioning system through an extended kalman filter algorithm. this localization system permits accurate trajectory tracking of legged robots during critical activities such as humanitarian de-mining tasks. preliminary experiments carried out with the silo4 system have shown adequate performance using this localization system.