waashk/dblp huggingface dataset:
vision-based interception of a moving target with a nonholonomic mobile robot. a novel vision-based scheme is presented for driving a nonholonomic mobile robot to intercept a moving target. the proposed method has a two-level structure. on the lower level, the pan-tilt platform carrying the on-board camera is controlled so as to keep the target as close as possible to the center of the image plane. on the higher level, the relative position of the target is retrieved from its image coordinates and the camera pan-tilt angles through simple geometry, and used to compute a control law which drives the robot to the target. various possible choices are discussed for the high-level robot controller, and the associated stability properties are rigorously analysed. the proposed visual interception method is validated through simulations as well as experiments on the mobile robot magellanpro.
adaptive task assignment for multiple mobile robots via swarm intelligence approach. this paper describes an adaptive task assignment method for a team of fully distributed mobile robots with initially identical functionalities in unknown task environments. a hierarchical assignment architecture is established for each individual robot. in the higher hierarchy, we employ a simple self-reinforcement learning model inspired by the behavior of social insects to differentiate the initially identical robots into ''specialists'' of different task types, resulting in stable and flexible division of labor; on the other hand, in dealing with the cooperation problem of the robots engaged in the same type of task, ant system algorithm is adopted to organize low-level task assignment. to avoid using a centralized component, a ''local blackboard'' communication mechanism is utilized for knowledge sharing. the proposed method allows the robot team members to adapt themselves to the unknown dynamic environments, respond flexibly to the environmental perturbations and robustly to the modifications in the team arising from mechanical failure. the effectiveness of the presented method is validated in two different task domains: a cooperative concurrent foraging task and a cooperative collection task.
classifying still faces with ultrasonic sensing. the echo of a chirp of ultrasonic energy from an object contains information about the geometry of that object: the relative depth of its surfaces and the approximate area of those surfaces. a human face has complex geometry that produces a distinctive echo. in this paper, we report the initial research into whether there is sufficient information in the echo to recognize a still face. potential features for classification are identified using a facial model. the classification results for 10 faces encourage future research with a larger number of faces and with moving faces.
short and long-range visual navigation using warped panoramic images. in this paper, we present a method that uses panoramic images to perform long-range navigation as a succession of short-range homing steps along a route specified by appearances of the environment of the robot along the route. our method is different from others in that it does not extract any features from the images and only performs simple image processing operations. the method does only make weak assumptions about the surroundings of the robot, assumptions that are discussed. furthermore, the method uses a technique borrowed from computer graphics to simulate the effect in the images of short translations of the robot to compute local motion parameters. finally, the proposed method shows that it is possible to perform navigation without explicitly knowing where the destination is nor where the robot currently is. results in our lab are presented that show the performance of the proposed system.
force position control for a robot finger with a soft tip and kinematic uncertainties. we consider the problem of force and position regulation for a robot finger with a soft tip in contact with a surface with unknown geometrical characteristics. an adaptive controller is proposed, and the asymptotic convergence of the applied force error and the estimated position error of the tip to zero is shown for the spatial case. simulation results demonstrate the controller performance.
real-time hierarchical pomdps for autonomous robot navigation. this paper proposes a new hierarchical formulation of pomdps for autonomous robot navigation that can be solved in real-time, and is memory efficient. it will be referred to in this paper as the robot navigation-hierarchical pomdp (rn-hpomdp). the rn-hpomdp is utilized as a unified framework for autonomous robot navigation in dynamic environments. as such, it is used for localization, planning and local obstacle avoidance. hence, the rn-hpomdp decides at each time step the actions the robot should execute, without the intervention of any other external module for obstacle avoidance or localization. our approach employs state space and action space hierarchy, and can effectively model large environments at a fine resolution. finally, the notion of the reference pomdp is introduced. the latter holds all the information regarding motion and sensor uncertainty, which makes the proposed hierarchical structure memory efficient and enables fast learning. the rn-hpomdp has been experimentally validated in real dynamic environments.
a fast evaluation criterion for the recognition of occluded shapes. a robust and fast object recognition method is crucial in many robotic applications, especially (but not restricted to) in manufacturing. this paper introduces a novel algorithm that satisfies both of these criteria and is capable of recognising a set of model shapes in a complicated scene given as input. the shapes are described using the turning angle representation. shape matching is carried out by finding the correspondence between the model shape and the input (i.e. corresponding points), and calculating the geometrical transformation of the model that minimises the least square distance between the corresponding points. a new geometric feature is proposed, called ''generalised angle'', which facilitates fast elimination of infeasible matches. the generalised angle (ga) is invariant to rotation, translation and scaling and does not result in a considerable computational cost to the system. moreover, an evaluation function is used, which takes several criteria into account and renders the method capable of recognising shapes under occlusion effectively.
image-based robot navigation from an image memory. this paper addresses the problem of vision-based navigation and proposes an original control law to perform such navigation. the overall approach is based on an appearance-based representation of the environment, where the scene is directly defined in the sensor space by a database of images acquired during a learning phase. within this context, a path to follow is described by a set of images, or image path extracted from the database. this image path is designed so as to provide enough information to control the robotic system. the central contribution of this paper is the closed-loop control law that drives the robot to its desired position using this image path. this control does not require either a global 3d reconstruction or a temporal planning step. furthermore, the robot is not constrained to converge directly upon each image of the path, but chooses its trajectory automatically. we propose a process of qualitative visual servoing, enabling us to enlarge the convergence space towards positioning in a range within a confidence interval. we propose and use specific visual features which ensure that the robot navigates within the visibility path. experimental simulations are given to show the effectiveness of this method for controlling the motion of a camera in three-dimensional environments (free-flying camera, or camera moving on a plane). in addition, experiments realized with a robotic arm observing a planar scene are also presented.
visual novelty detection with automatic scale selection. this paper presents experiments with an autonomous inspection robot, whose task was to highlight novel features in its environment from camera images. the experiments used two different attention mechanisms-saliency map and multi-scale harris detector-and two different novelty detection mechanisms - the grow-when-required (gwr) neural network and an incremental principal component analysis (pca). for all mechanisms we compared fixed-scale image encoding with automatically scaled image patches. results show that automatic scale selection provides a more efficient representation of the visual input space, but that performance is generally better using a fixed-scale image encoding.
mobile robot localization using active sensing based on bayesian network inference. in this paper we propose a novel method of sensor planning for a mobile robot localization problem. we represent the conditional dependence relation between local sensing results, actions, and belief of the global localization using a bayesian network. initially, the structure of the bayesian network is learned from the complete data of the environment using the k2 algorithm combined with a genetic algorithm (ga). in the execution phase, when the robot is kidnapped to some place, it plans an optimal sensing action by taking into account the trade-off between the sensing cost and the global localization belief, which is obtained by inference in the bayesian network. we have validated the learning and planning algorithm by simulation experiments in an office environment.
path planning with general end-effector constraints. in this paper, we address the path planning problem with general end-effector constraints (ppgec) for robot manipulators. two approaches are proposed. the first approach is the adapted-rgd method, which is adapted from an existing randomized gradient descent (rgd) method for closed-chain robots. the second approach is radically different. we call it atace, alternate task-space and configuration-space exploration. unlike the first approach which searches purely in c-space, atace works in both task space and c-space. it explores the task space for end-effector paths satisfying given constraints, and utilizes trajectory tracking technique(s) as a local planner(s) to track these paths in the configuration space. we have implemented both approaches and compared their relative performances in different scenarios. atace outperforms adapted-rgd in the majority (but not all) of the scenarios. we outline intuitive explanations for the relative performances of these two approaches.
a constrained slam approach to robust and accurate localisation of autonomous ground vehicles. for driving assistance systems, intelligent vehicles and autonomous robots to be viable in complex environments, it is necessary to have a reliable and robust localisation function. due to the large variability and uncertainty of such complex environments, which include theme parks, university campuses, suburbs, industrial estates and the like, it is difficult to rely on a specific method or set of sensor data to correctly and robustly estimate the robot path/pose. the key to solving the localisation problem is to optimally use and fuse all useful sources of information available to the mobile platform. for the envisaged environment, it is not unusual to have approximate digital maps of the road network. in this paper, in addition to the typical sensory information provided by extereoceptive and proprioceptive sensors, it is shown how a priori approximate knowledge available in the form of a road map can be systematically fused within a simultaneous localisation and map building (slam) framework to obtain more accurate and robust localisation results. this reformulation of slam through the introduction of constraints in the form of a priori map information not only makes the problem theoretically more correct in the sense of observability but also makes the system viable and effective, yielding more accurate results. the results obtained in an actual environment are presented to validate the claims.
high resolution relative localisation using two cameras. wheel odometry is a common method for high resolution relative localisation. however, wheel odometry relies on the integrity and accuracy of a kinematic model. in this paper, a new method for relative localisation, 'visiodometry', which does not rely on a kinematic model, is proposed. the system consists of two ground-facing cameras mounted on either side of the robot. from the sequence of images acquired, the relative change in pose of the robot is estimated using a phase correlation based method. results on a plain coloured carpeted surface, show that the method provides a truly odometric type sensor data input similar in modality and resolution to wheel odometry. a method to calibrate the visiodometry system using a 1d object is also presented.
multi-method learning and assimilation. considering the wide range of possible behaviours to be acquired for domestic robots, applying a single learning method is clearly insufficient. in this paper, we propose a new strategy for behaviour acquisition for domestic robots where the behaviours are acquired using multiple differing learning methods that are subsequently incorporated into a common behaviour selection system, enabling them to be performed in appropriate situations. an example of the implementation of this strategy applied to the entertainment humanoid robot qrio is introduced and the results are discussed.
maccepa, the mechanically adjustable compliance and controllable equilibrium position actuator: design and implementation in a biped robot. in this paper a rotational actuator with a novel adaptable compliance (inverse of stiffness) is presented. first, a number of comparable designs are given with their possible drawbacks. the maccepa concept and design is then described in detail. the equation to calculate the generated torque is derived. depending on the design parameters, it is shown that the torque is a quasi linear function with respect to the angle between the equilibrium position and the actual position. also, the change of the pre-tension has a quasi linear effect on the torque. another advantage is that the actuator can be built with standard components, e.g. electrical servo motors. experiments show independent control of the equilibrium position and compliance. the use of the maccepa in the controlled passive walking biped veronica is described. controlled passive walking is an approach that combines the advantages of actively controlled robots and passive walkers. by adapting the compliance of the joints, natural motions can be chosen in order to obtain a controllable and energy efficient walking motion. to test the concept, the biped veronica is built, actuated by six maccepas.
supervised semantic labeling of places using information extracted from sensor data. indoor environments can typically be divided into places with different functionalities like corridors, rooms or doorways. the ability to learn such semantic categories from sensor data enables a mobile robot to extend the representation of the environment facilitating interaction with humans. as an example, natural language terms like ''corridor'' or ''room'' can be used to communicate the position of the robot in a map in a more intuitive way. in this work, we first propose an approach based on supervised learning to classify the pose of a mobile robot into semantic classes. our method uses adaboost to boost simple features extracted from sensor range data into a strong classifier. we present two main applications of this approach. firstly, we show how our approach can be utilized by a moving robot for an online classification of the poses traversed along its path using a hidden markov model. in this case we additionally use as features objects extracted from images. secondly, we introduce an approach to learn topological maps from geometric maps by applying our semantic classification procedure in combination with a probabilistic relaxation method. alternatively, we apply associative markov networks to classify geometric maps and compare the results with a relaxation approach. experimental results obtained in simulation and with real robots demonstrate the effectiveness of our approach in various indoor environments.
hybrid deliberative/reactive control of a scanning system for landmine detection. antipersonnel mines infest fields all over the world. according to recent estimates, landmines are killing and maiming more than 2000 innocent civilians per month. the problem of landmine detection and removal requires the cooperation of a number of engineering fields, which in turn poses a need for new technologies, such as improved sensors, efficient manipulators and mobile robots. this paper describes the configuration and control architecture of a scanning manipulator for detecting antipersonnel landmines. the scanning system is part of a demining system based on a walking robot that acts as the carrier for the scanning manipulator. broadly speaking, the scanning system consists of a sensor head that can detect certain kinds of landmines and, to move the sensor head over large areas, a manipulator that has been appropriately sensorized to scan irregular terrains in the presence of obstacles. the proposed control architecture is of the hybrid deliberative/reactive type: a deliberative controller defines a sweep trajectory that furnishes complete coverage of the infested area, while two reactive controllers are involved in on-line adaptation to the environment. experiments show good performance of the whole system.
programmable springs: developing actuators with programmable compliance for autonomous robots. developing real robots that can exploit dynamic interactions with the environment requires the use of actuators whose behaviour can vary from high stiffness to complete compliance or zero impedance. we will outline our design for an electric actuator, called a programmable spring, which can easily be configured using a high-level programming interface to emulate complex multimodal spring damping systems. the types of behaviour that our actuator can exhibit are explored, including antagonistic actuation, cyclic behaviour and hysteresis. this system is intended as the basis for a cost-effective 'off-the-shelf' component for robotics research and development.
developmental learning for autonomous robots. developmental robotics is concerned with the design of algorithms that promote robot adaptation and learning through qualitative growth of behaviour and increasing levels of competence. this paper uses ideas and inspiration from early infant psychology (up to three months of age) to examine how robot systems could discover the structure of their local sensory-motor spaces and learn how to coordinate these for the control of action. an experimental learning model is described and results from robotic experiments using the model are presented and discussed.
sensor-based path planning for nonholonomic mobile robots subject to dynamic constraints. it is generally not easy to achieve smooth path planning in an unknown environment for nonholonomic mobile robots, which are subject to various robot constraints. in this paper, a hybrid approach is proposed for smooth path planning with global convergence for differential drive nonholonomic robots. we first investigate the use of a polar polynomial curve (ppc) to produce a path changing continuously in curvature and satisfying dynamic constraints. in order to achieve path generation in real-time, a computationally effective method is proposed for collision test of the complex curve. then, a hybrid path planning approach is presented to guide the robot to move forward along the boundary of an obstacle of arbitrary shape, by generating a proper ''instant goal'' (and a series of deliberate motions through ppc curve based path generation) and planning reactively when needed using a fuzzy controller for wall following. the choice of an instant goal is limited to the set of candidates that are practically reachable by the robot and that enable the robot to continue following the obstacle. the effectiveness of the proposed approach is verified by simulation experiments.
task identification and characterisation in mobile robotics through non-linear modelling. the lack of a theory-based design methodology for mobile robot control programs means that control programs have to be developed through an empirical trial-and-error process. this can be costly, time consuming and error prone. in this paper we show how to develop a theory of robot-environment interaction, which would overcome the above problem. we show how we can model a mobile robot's task (so-called ''task identification'') using non-linear polynomial models (narmax), which can subsequently be formally analysed using established mathematical methods. this provides an understanding of the underlying phenomena governing the robot's behaviour. apart from the paper's main objective of formally analysing robot-environment interaction, the task identification process has further benefits, such as the fast and convenient cross-platform transfer of robot control programs (''robot java''), parsimonious task representations (memory issues) and very fast control code execution times.
decentralized cooperative control of heterogeneous vehicle groups. we coordinate in discrete time the interaction of two heterogeneous groups of mobile agents: a group of ground vehicles (ugvs) and a group of aerial vehicles (uavs). the ground agents interact with each other through time-invariant, nearest-neighbour rules. they synchronize their velocities through a specific communication protocol, and maintain cohesion and separation behaviour by means of interagent potential forces. ground vehicles estimate their formation's centroid using only locally available delayed information. that same information is transmitted to the aerial group, which orbits above the ground formation's centroid, while avoiding midair collisions. stability of the ground group motion is established in a lyapunov framework. a lyapunov analysis is also used to ensure that uavs track the ground group's centroid.
the reverse monte carlo localization algorithm. global localization is a very fundamental and challenging problem in robotic soccer. here, the main aim is to find the best method which is very robust and fast and requires less computational resources and memory compared to similar approaches and is precise enough for robot soccer games and technical challenges. in this work, the reverse monte carlo localization (r-mcl) method is introduced. the algorithm is designed for fast, precise and robust global localization of autonomous robots in the robotic soccer domain, to overcome the uncertainties in the sensors, environment and the motion model. r-mcl is a hybrid method based on markov localization (ml) and monte carlo localization (mcl), where the ml based module finds the region where the robot should be and the mcl based part predicts the geometrical location with high precision by selecting samples in this region. it is called reverse since the mcl routine is applied in a reverse manner in this algorithm. in this work, this method is tested on a challenging data set that is used by many other researchers and compared in terms of error rate against different levels of noise, and sparsity. additionally, the time required to recover from kidnapping and the processing time of the methods are tested and compared. according to the test results r-mcl is a considerable method against high sparsity and noise. it is preferable when its recovery from kidnapping and processing times are considered. it gives robust and fast but relatively coarse position estimations against imprecise and inadequate perceptions, and coarse action data, including regular misplacements, and false perceptions.