waashk/dblp huggingface dataset:
real-time implementation of airborne inertial-slam. this paper addresses some challenges to the real-time implementation of simultaneous localisation and mapping (slam) on a uav platform. when compared to the implementation of slam in 2d environments, airborne implementation imposes several difficulties in terms of computational complexity and loop closure, with high nonlinearity in both vehicle dynamics and observations. an implementation of airborne slam is formulated to relieve this computational complexity in both direct and indirect ways. our implementation is based on an extended kalman filter (ekf), which fuses data from an inertial measurement unit (imu) with data from a passive vision system. real-time results from flight trials are provided.
a decomposition approach to multi-vehicle cooperative control. we use a decomposition approach to generate cooperative strategies for a class of multi-vehicle control problems. by introducing a set of tasks to be completed by the team of vehicles and a task execution method for each vehicle, we decompose the problem into a combinatorial component and a continuous component. the continuous component of the problem is captured by task execution, and the combinatorial component is captured by task assignment. in this paper, we present a solver for task assignment that generates near-optimal assignments quickly and can be used in real-time applications. to motivate our methods, we apply them to an adversarial game between two teams of vehicles. one team is governed by simple rules and the other by our algorithms. in our study of this game we found phase transitions, showing that the task assignment problem is most difficult to solve when the capabilities of the adversaries are comparable. finally, we utilize our algorithms in a hierarchical model predictive control architecture with a variable replanning rate at each level to provide feedback in dynamically changing and uncertain environments.
robocentric map joining: improving the consistency of ekf-slam. in this paper we study the extended kalman filter approach to simultaneous localization and mapping (ekf-slam), describing its known properties and limitations, and concentrate on the filter consistency issue. we show that linearization of the inherent nonlinearities of both the vehicle motion and the sensor models frequently drives the solution of the ekf-slam out of consistency, specially in those situations where uncertainty surpasses a certain threshold. we propose a mapping algorithm, robocentric map joining, which improves consistency of the ekf-slam algorithm by limiting the level of uncertainty in the continuous evolution of the stochastic map: (1) by building a sequence of independent local maps, and (2) by using a robot centered representation of each local map. simulations and a large-scale indoor/outdoor experiment validate the proposed approach.
oats: oxford aerial tracking system. small robot helicopters are becoming a popular research platform due to the availability of off-the-shelf components and their suitability for useful applications. we describe the oxford aerial tracking system (oats) that we are commissioning, which takes a commercial airframe and low-level flight controller, and adapts these for use in applications requiring the visual tracking of ground targets. this uses a camera on a two-axis gimbal feeding images into an on-board processing system, which communicates summary information to a ground station. so far we have tested the system off the aircraft using laboratory targets and canned image sequences; we have also developed in simulation a variety of high-level algorithms for the platform, including those for target area scanning, geo-localisation, 3-d path planning, and target trajectory prediction; this paper focuses on the hardware, system software, and object tracking sub-systems. testing on the airframe is now underway.
visual task identification and characterization using polynomial models. developing robust and reliable control code for autonomous mobile robots is difficult, because the interaction between a physical robot and the environment is highly complex, subject to noise and variation, and therefore partly unpredictable. this means that to date it is not possible to predict robot behaviour based on theoretical models. instead, current methods to develop robot control code still require a substantial trial-and-error component to the software design process. this paper proposes a method of dealing with these issues by (a) establishing task-achieving sensor-motor couplings through robot training, and (b) representing these couplings through transparent mathematical functions that can be used to form hypotheses and theoretical analyses of robot behaviour. we demonstrate the viability of this approach by teaching a mobile robot to track a moving football and subsequently modelling this task using the narmax system identification technique.
realtime motion path generation using subtargets in a rapidly changing environment. in this work an algorithm is proposed for path planning in a rapidly changing environment. the algorithm is computationally cheap and generates a sub-optimal smooth path with bounds on the allowed velocity, acceleration, and jerk. the algorithm is designed for holonomic omniwheel platforms. it outperforms potential field algorithms regarding both convergence and optimality. furthermore, it is able to adapt fast in a rapidly changing environment due to the low computational cost in the order of ms for a single update, in contrast with computationally more expensive methods such as wavefront algorithms and global optimization methods, where the computational cost is mostly on the order of seconds. the algorithm will be tested via simulations and experiments.
fault-tolerant robot manipulators based on output-feedback h controllers. this paper develops two fault-tolerant control strategies for robot manipulators. the first is based on linear parameter-varying systems and the second on markovian jump linear systems. firstly, it is shown that with the lpv approach post-fault stability is guaranteed only if the robot stops completely after a fault detection. then, with an underactuated configuration, the manipulator can be controlled appropriately. secondly, it is shown that with the fault-tolerant system based on markovian jump linear systems, stability is guaranteed after a fault is detected even with the robot still moving. this approach incorporates all manipulator configurations in a unified model. both strategies have been implemented based on output-feedback controllers, which are the main focus of this paper. experimental results illustrate the performance of each controller.
learning spatial concepts from ratslam representations. ratslam is a biologically-inspired visual slam and navigation system that has been shown to be effective indoors and outdoors on real robots. the spatial representation at the core of ratslam, the experience map, forms in a distributed fashion as the robot learns the environment. the activity in ratslam's experience map possesses some geometric properties, but still does not represent the world in a human readable form. a new system, dubbed ratchat, has been introduced to enable meaningful communication with the robot. the intention is to use the ''language games'' paradigm to build spatial concepts that can be used as the basis for communication. this paper describes the first step in the language game experiments, showing the potential for meaningful categorization of the spatial representations in ratslam.
autonomous and fast robot learning through motivation. research on robot techniques that are fast, user-friendly, and require little application-specific knowledge by the user, is more and more encouraged in a society where the demand of home-care or domestic-service robots is increasing continuously. in this context we propose a methodology which combines reinforcement learning and genetic algorithms to teach a robot how to perform a task when only the specification of the main restrictions of the desired behaviour is provided. through this combination, both paradigms must be merged in such a way that they influence each other to achieve a fast convergence towards a good robot-control policy, and reduce the random explorations the robot needs to carry out in order to find a solution. another advantage of our proposal is that it is able to easily incorporate any kind of domain-dependent knowledge about the task. this is very useful for improving a robot controller, for applying a robot-controller to move a different robot-platform, or when we have certain ''feelings'' about how the task should be solved. the performance of our proposal is shown through its application to solve a common problem in mobile robotics.
instantaneous robot self-localization and motion estimation with omnidirectional vision. this paper presents two related methods for autonomous visual guidance of robots: localization by trilateration, and interframe motion estimation. both methods use coaxial omnidirectional stereopsis (omnistereo), which returns the range r to objects or guiding points detected in the images. the trilateration method achieves self-localization using r from the three nearest objects at known positions. the interframe motion estimation is more general, being able to use any features in an unknown environment. the guiding points are detected automatically on the basis of their perceptual significance and thus they need not have either special markings or be placed at known locations. the interframe motion estimation does not require previous motion history, making it well suited for detecting acceleration (in 20th of a second) and thus supporting dynamic models of robot's motion which will gain in importance when autonomous robots achieve useful speeds. an initial estimate of the robot's rotation &omega; (the visual compass) is obtained from the angular optic flow in an omnidirectional image. a new noniterative optic flow method has been developed for this purpose. adding &omega; to all observed (robot relative) bearings &theta; gives true bearings towards objects (relative to a fixed coordinate frame). the rotation &omega; and the r,&theta; coordinates obtained at two frames for a single fixed point at unknown location are sufficient to estimate the translation of the robot. however, a large number of guiding points are typically detected and matched in most real images. each such point provides a solution for the robot's translation. the solutions are combined by a robust clustering algorithm clumat that reduces rotation and translation errors. simulator experiments are included for all the presented methods. real images obtained from scitosg5 autonomously moving robot were used to test the interframe rotation and to show that the presented vision methods are applicable to real images in real robotics scenarios.
space-variant motion detection for active visual target tracking. a biologically inspired approach to active visual target tracking is presented. the approach makes use of three strategies found in biological systems: space-variant sensing, a spatio-temporal-frequency-based model of motion detection and the alignment of sensory-motor maps. space-variant imaging is used to create a 1d array of elementary motion detectors (emds) that are tuned in such a way as to make it possible to detect motion over a wide range of velocities while still being able to detect motion precisely. the array is incorporated into an active visual tracking system. a method of analysis and design for such a tracking system is proposed. it makes use of a sensory-motor map which consists of a phase-plane plot of the continuous-time dynamics of the tracking system overlaid onto a map of the detection capabilities of the array of emds. this sensory-motor map is used to design a simple 1d tracking system and several simulations show how the method can be used to control tracking performance using such metrics as overshoot and settling time. a complete 1d active vision system is implemented and a set of simple target tracking experiments are performed to demonstrate the effectiveness of the approach.
a tactile luminous floor for an interactive autonomous space. this paper describes the interactive tactile luminous floor that was constructed and used as the skin of the playful interactive space ada, which ran as a public exhibit for five months in 2002 and had over 550,000 visitors. ada's floor was custom-built to provide a means for individual and collective user interaction. it consists of 360 hexagonal 66 cm tiles covering a total area of 136 m^2, each with analogue tactile load sensors based on force-sensitive resistors and dimmable neon red, green and blue (rgb) lamps. the tiles are constructed from extruded aluminum with glass tops. an interbus factory automation bus senses and controls the tiles. software is described for rendering fluid, dynamic visual effects on the floor, for signal processing of the load information, for real-time visitor tracking and for a variety of behavioural modes, games and interactions. data from single tiles and from tracking are shown. this floor offers new modalities of human-computer interaction and human-robot interaction for autonomous robotic spaces.
neural network models of haptic shape perception. three different models of tactile shape perception inspired by the human haptic system were tested using an 8 d.o.f. robot hand with 45 tactile sensors. one model is based on the tensor product of different proprioceptive and tactile signals and a self-organizing map (som). the two other models replace the tensor product operation with a novel self-organizing neural network, the tensor-multiple peak self-organizing map (t-mpsom). the two t-mpsom models differ in the procedure employed to calculate the neural activation. the computational models were trained and tested with a set of objects consisting of hard spheres, blocks and cylinders. all the models learned to map different shapes to different areas of the som, and the tensor product model as well as one of the t-mpsom models also learned to discriminate individual test objects.
application of sonql for real-time learning of robot behaviors. this paper describes the semi-online neural-q-learning (sonql) algorithm designed for real-time learning of reactive robot behaviors. the q-function is generalized by a multilayer neural network allowing the use of continuous states. the algorithm uses a database of the most recent learning samples to accelerate and improve the convergence. each sonql algorithm represents an independent, reactive and adaptive state-action mapping, which implements the function of a robot behavior for one degree of freedom (dof). the generalization capability of the sonql algorithm was demonstrated by computer simulation with the ''mountain-car'' benchmark. the sonql was also investigated by experiment on a mobile robot for a target-following task. experimental results show that the sonql is promising for online robot learning.
intelligent decision making in multi-agent robot soccer system through compounded artificial neural networks. this paper proposes a two-stage approach using artificial neural networks for the intelligent decision-making by the robots in a mirosot small league. the first stage involves the use of an evolutionary algorithm for getting a rough estimate of the neural network weight matrices. the proposed approach is then generalized to the case of quick, intelligent and accurate decision-making in the case of a robot soccer system with robots utilizing the concept of compounded artificial neural networks. in the proposed approach a soccer field is divided into three zones so that the decision of the robots depends on the zone of the ball at any instant. the concept of a forward robot is also introduced in this paper to enhance the accuracy of the decision-making with the global strategy of advancing towards the goal area of the opponent for scoring a goal. simulation results indicate that the proposed techniques are very effective in taking intelligent decision-making in a multi-agent robot soccer system in mirosot small league as well as middle league.
self-localization in non-stationary environments using omni-directional vision. this paper presents an image-based approach for localization in non-static environments using local feature descriptors, and its experimental evaluation in a large, dynamic, populated environment where the time interval between the collected data sets is up to two months. by using local features together with panoramic images, robustness and invariance to large changes in the environment can be handled. results from global place recognition with no evidence accumulation and a monte carlo localization method are shown. to test the approach even further, experiments were conducted with up to 90% virtual occlusion in addition to the dynamic changes in the environment.
compensation of velocity and/or acceleration joint saturation applied to redundant manipulator. the article describes a new method for velocity/acceleration redistribution in order to compensate joint velocity and/or acceleration saturation. the method is designed for redundant manipulators. when some of the joint velocities/accelerations are in saturation other joints compensate for the lack of the velocity and the velocity in the task space remains unchanged. without the compensation the task space error would appear. using the compensation method we can achieve maximal velocity/acceleration in the task space while preserving joint velocity/acceleration within limits. the method is also appropriate to compensate a torque saturation. additionally, we have introduced a condition that shows if the compensation is kinematically and mathematically possible or not.
a biologically inspired method for vision-based docking of wheeled mobile robots. we present a new control law for the problem of docking a wheeled robot to a target at a certain location with a desired heading. recent research into insect navigation has inspired a solution which uses only one video camera. the control law is of the ''behavioral'' type in that all control actions are based on immediate visual information. docking success under certain conditions is proved mathematically and simulation studies show the control law to be robust to camera intrinsic parameter errors. experiments were performed for verification of the control law.
a variational method for the recovery of dense 3d structure from motion. the purpose of this study is to investigate a variational formulation of the problem of three-dimensional (3d) interpretation of temporal image sequences based on the 3d brightness constraint and anisotropic regularization. the method allows movement of both the viewing system and objects and does not require the computation of image motion prior to 3d interpretation. interpretation follows the minimization of a functional with two terms: a term of conformity of the 3d interpretation to the image sequence first-order spatio-temporal variations, and a term of regularization based on anisotropic diffusion to preserve the boundaries of interpretation. the euler-lagrange partial differential equations corresponding to the functional are solved efficiently via the half-quadratic algorithm. results of several experiments on synthetic and real image sequences are given to demonstrate the validity of the method and its implementation.
amirolos an active marker internet-based robot localization system. sauron (surveillance autonomous robots over network) is a research project focused on the use of autonomous patrolling robots for surveillance purposes. this paper presents the structure of sauron localization module amirolos (active marker internet-based robot localization system). it provides on demand the robot position in indoor and outdoor environments using active markers and commercial off-the-shelf web-cams. the system has a client-server architecture, and is now part of a message-oriented middleware that is under development at the university of brescia. the functionality of the localization module has been demonstrated with real-world experiments.
dynamic object manipulation by an array of 1-dof manipulators: kinematic modeling and planning. dynamic manipulation of polygonal objects by an array of one degree of freedom arms is studied from kinematics and planning points of view. in the studied manipulation method, an object is manipulated to its goal configuration by a sequence of juggles. a kinematic model of an object throwing task is driven and a method for object manipulation by a sequence of throws is proposed. the method, called backward throws method (btm), is based on throwing an object backward towards the arm pivot. based on the developed model, a planning algorithm is proposed for btm. in addition, the only existing similar method to btm, which is based on forward throws-named ftm in this paper-is reformulated for implementation by a series of arms and compared with the proposed method. analytical investigations, simulation results, and experimental outcomes show that btm meets the desired requirements. moreover, in comparison to ftm, btm requires fewer number of throws and lower release velocity. in addition, the object's maximum height of flight is much lower in btm which results in lower catching impact. according to the experimental results, although the proposed method has no feedback from object position, accumulated position error is very small. this fact is directly related to the attained decrease in catching impact which causes small object slippage and rebound on catching. furthermore, there is no restriction on the arm geometry in btm while in ftm an arm with negative offset is needed.
reachability-based analysis for probabilistic roadmap planners. in the last fifteen years, sampling-based planners like the probabilistic roadmap method (prm) have proved to be successful in solving complex motion planning problems. while theoretically, the complexity of the motion planning problem is exponential in the number of degrees of freedom, sampling-based planners can successfully handle this curse of dimensionality in practice. we give a reachability-based analysis for these planners which leads to a better understanding of the success of the approach. this analysis compares the techniques based on coverage and connectivity of the free configuration space. the experiments show, contrary to general belief, that the main challenge is not getting the free space covered but getting the nodes connected, especially when the problems get more complicated, e.g. when a narrow passage is present. by using this knowledge, we can tackle the narrow passage problem by incorporating a refined neighbor selection strategy, a hybrid sampling strategy, and a more powerful local planner, leading to a considerable speed-up.
observer-based sliding mode impedance control of bilateral teleoperation under constant unknown time delay. sliding mode control has been used extensively in robotics to cope with parametric uncertainty and hard nonlinearities, in particular for time-delay teleoperators, which have gained gradual acceptance due to technological advancements. however, since the slave teleoperator is in contact with a rigid environment, the slave controller requires a free of chattering control strategy, thus making first order sliding mode teleoperation control unsuitable. as an alternative, chatter free, higher-order sliding mode teleoperator control is proposed in this paper to guarantee robust tracking under unknown constant time delay. moreover, complete order observers are proposed to avoid measurement of velocity and acceleration, along with a formal closed-loop stability proof of the observer-based controller. experimental results are presented and discussed, which reveals the effectiveness of the proposed teleoperation scheme.