waashk/dblp huggingface dataset:
motion planning for cooperative unicycle-type mobile robots with limited sensing ranges: a distributed receding horizon approach. this paper presents a decentralized motion planner for a team of nonholonomic mobile robots subject to constraints imposed by sensors and the communication network. the motion planning scheme consists of decentralized receding horizon planners that reside on each vehicle to achieve coordination among flocking agents. the advantage of the proposed algorithm is that each vehicle only requires local knowledge of its neighboring vehicles. the main requirement for designing an optimal conflict-free trajectory in a decentralized way is that each robot does not deviate too far from its presumed trajectory designed without taking the coupling constraints into account. a comparative study between the proposed algorithm and other existing algorithms is provided in order to show the advantages, especially in terms of computing time. finally, experiments are performed on a team of three mobile robots to demonstrate the validity of the proposed approach.
modeling of tracked mobile manipulators with consideration of track-terrain and vehicle-manipulator interactions. this paper presents a systematic method to establish the kinematics model for a tracked mobile manipulator on firm grounds, with consideration of the interactive motions between the tracks and the terrain, as well as those between the tracked vehicle and the onboard manipulator. kinematics analysis is essential for real-time pose estimation and online autonomous navigation of tracked mobile manipulators. furthermore, to improve the effectiveness of motion planning, and to simulate or control tracked mobile manipulators, a reliable kinematics model is required. however, kinematics modeling for a tracked mobile manipulator is complicated by the fact that there are infinite number of contact points between the tracks and the terrain, which makes slippage unavoidable. the track-terrain and vehicle-manipulator interactions make the problem even more complicated as the motion of the onboard manipulator and the centrifugal forces during moderate or high speed motion give rise to transfer of the load distribution, which will affect the longitudinal and lateral tractive forces and the resistance. also, the motion of the mobile platform contributes to the inertial forces of the manipulator, and the track-terrain interactive forces help balance the gravity as well as the manipulation forces. the developed kinematics modeling approach is presented on the basis of a tracked mobile manipulator in our laboratory, but the forward kinematics analysis method, and the track-terrain and vehicle-manipulator interaction analysis algorithm are general, and can be used for any tracked mobile manipulators with little modification. this work lays a solid foundation for autonomous control, online slippage estimation, real-time traction optimization as well as tip-over prediction and prevention of tracked mobile manipulators.
two different approaches to a macroscopic model of a bio-inspired robotic swarm. by compiling macroscopic models we analyze the adaptive behavior in a swarm of autonomous robots generated by a bio-inspired, distributed control algorithm. we developed two macroscopic models by taking two different perspectives: a stock & flow model, which is simple to implement and fast to simulate, and a spatially resolved model based on diffusion processes. these two models were compared concerning their prediction quality and their analytical power: one model allowed easy identification of the major feedback loops governing the swarm behavior. the other model allowed analysis of the expected shapes and positions of observable robot clusters. we found a high correlation in the challenges posed by both modeling techniques and we highlighted the inherent problems of inferring emergent macroscopic rules from a microscopic description of swarm behavior.
recognizing places using spectrally clustered local matches. place recognition is a fundamental perceptual problem at the heart of many basic robot operations, most notably mapping. failures can result from ambiguous sensor readings and environments with similar appearances. in this paper, we describe a robust place recognition algorithm that fuses a number of uncertain local matches into a high-confidence global match. we describe the theoretical basis of the approach and present extensive experimental results from a variety of sensor modalities and environments.
effect of global position information in unknown world exploration - a case study using the teleworkbench. this paper presents empirical results of the effect of the global position information on the performance of the modified local navigation algorithm (mlna) for unknown world exploration. the results show that global position information enables the algorithm to maintain 100% success rate irrespective of initial robot position, movement speed, and environment complexity. most mobile robot systems accrue an odometry error while moving, and hence need to use external sensors to recalibrate their position on an ongoing basis. we deal with position calibration to compensate the odometry error using the global position information provided by the teleworkbench, which is a teleoperated platform and test bed for managing experiments using mini-robots. in this paper we demonstrate how we incorporate the global position information during and after the experiments.
robot learning language - integrating programming and learning for cognitive systems. one central property of cognitive systems is the ability to learn and to improve continually. we present a robot control language that combines programming and learning in order to make learning executable in the normal robot program. the language constructs of our learning language roll rely on the concept of hierarchical hybrid automata to enable a declarative, explicit specification of learning problems. using the example of an autonomous household robot, we point out some instances where learning-and especially continued learning-makes the robot control program more cognitive.
keypoint design and evaluation for place recognition in 2d lidar maps. we address the place recognition problem, which we define as the problem of establishing whether an observed location has been previously seen, and if so, determining the transformation aligning the current observations to an existing map. in the contexts of robot navigation and mapping, place recognition amounts to globally localizing a robot or map segment without being given any prior estimate. an efficient method of solving this problem involves first selecting a set of keypoints in the scene which store an encoding of their local region, and then utilizing a sublinear-time search into a database of keypoints previously generated from the global map to identify places with common features. we present an algorithm to embed arbitrary keypoint descriptors in a reduced-dimension metric space, in order to frame the problem as an efficient nearest neighbor search. given that there are a multitude of possibilities for keypoint design, we propose a general methodology for comparing keypoint location selection heuristics and descriptor models that describe the region around the keypoint. with respect to selecting keypoint locations, we introduce a metric that encodes how likely it is that the keypoint will be found in the presence of noise and occlusions during mapping passes. metrics for keypoint descriptors are used to assess the distinguishability between the distributions of matches and non-matches and the probability the correct match will be found in an approximate k-nearest neighbors search. verification of the test outcomes is done by comparing the various keypoint designs on a kilometers-scale place recognition problem. we apply our design evaluation methodology to three keypoint selection heuristics and six keypoint descriptor models. a full place recognition system is presented, including a series of match verification algorithms which effectively filter out false positives. results from city-scale and long-term mapping problems illustrate our approach for both offline and online slam, map merging, and global localization and demonstrate that our algorithm is able to produce accurate maps over trajectories of hundreds of kilometers.
kinematic design optimization of a parallel ankle rehabilitation robot using modified genetic algorithm. rehabilitation robotics is an evolving area of active research and recently novel mechanisms have been proposed to reinstate complex human movements. parallel robots are of particular interest to researchers since they are rigid and can provide enough load capacity for human joint movements. this paper proposes a soft parallel robot (spr) for ankle joint rehabilitation. kinematic workspace analysis is carried out and the singularity criterion of the spr's jacobian matrix is used to define the feasible workspace. a global conditioning number (gcn) is defined using the jacobian matrix as a performance index for the evaluation of the robot design. an optimization problem is formulated to minimize the gcn using modified genetic algorithm (ga). results from simple ga and modified ga are compared and discussed. as a result of the optimization, an optimal robot design is obtained which has a near unity gcn with almost uniform distribution in the entire feasible workspace of the robot.
tackling the premature convergence problem in monte-carlo localization. monte-carlo localization uses particle filtering to estimate the position of the robot. the method is known to suffer from the loss of potential positions when there is ambiguity present in the environment. since many indoor environments are highly symmetric, this problem of premature convergence is problematic for indoor robot navigation. it is, however, rarely studied in particle filters. we introduce a number of so-called niching methods used in genetic algorithms, and implement them on a particle filter for monte-carlo localization. the experiments show a significant improvement in the diversity maintaining performance of the particle filter.
shared potential fields and their place in a multi-robot co-ordination taxonomy. previously our novel shared potential field (spf) method has been introduced and compared against a non-sharing control in both simulation and laboratory settings. in this paper, extended from a paper presented at the ciras 2008 conference, we compare the spf against an existing type of robot architecture, a hybrid robotic system. the spf method is compared to the traditional potential field method, and it is shown that the spf is less susceptible to the traditional limitations of potential fields. the spf method's position in farinelli's multi-robot taxonomy is also discussed, and it is shown that rather than being placed in one category it encompasses two, corresponding to the two levels of control within the architecture. in experiments, the multi-robot systems are given the task of traversing an unknown environment, in an attempt to locate a target. the metric of performance for this task was the time taken to find the target. experiments show that the hybrid system showed similar performance to the non-sharing control. in contrast, our pessimistic variant of the spf outperformed the hybrid system in the cluttered environment, and the optimistic spf variant outperformed the hybrid system in the sparse environment. we conclude that the spf method reacts more robustly to the dynamic nature of the real world, and so performed significantly better throughout the experimentation.
collision-free control of an omni-directional vehicle. this study addresses the problem of controlling an omni-directional vehicle with both state and control dependent constraints. the task of the vehicle is to attain its desired final position given in the task space. the control constraints resulting from the physical abilities of actuators driving the vehicle wheels are also taken into account during the robot movement. the problem of collision avoidance is solved here based on an exterior penalty function approach which results in smooth vehicle velocities near obstacles. provided that, a solution to the aforementioned vehicle task exists, the lyapunov stability theory is used to derive the control scheme. the numerical simulation results carried out for the omni-directional vehicle operating in both a constraint-free task space and task space including obstacles, illustrate the performance of the proposed controllers.
efficient data association for view based slam using connected dominating sets. loop closing in vision based slam applications is a difficult task. comparing new image data with all previously acquired image data is practically impossible because of the high computational costs. most approaches therefore compare new data with only a subset of the old data, for example by sampling the data over time or over space by using a position estimate. in this paper, we propose a more natural approach, which dynamically determines a subset of images that best describes the complete image data in the space of all previously seen images. the actual problem of finding such a subset is called the ''connected dominating set'' (cds) problem, which is well studied in the field of graph theory. application on large indoor datasets results in approximately the same map using only 13% of the computational resources with respect to comparing with all previous images. also, it outperforms other sampling approaches. the proposed method is particularly beneficial for realistic mapping scenarios including moving objects and persons, motion blur and changing light conditions.
metric-topological maps from laser scans adjusted with incremental tree network optimizer. several adaptations of maximum likelihood approaches to incremental map learning have been proposed recently. in particular, an incremental network optimizer based on stochastic gradient descent provides a fast and easy-to-implement solution to the problem. in this paper, we illustrate two map builders that process laser scans in order to extract the constraint network for the optimization algorithm. the first algorithm builds a map in the form of a collection of scans corresponding to a subset of the poses of a robot moving in the environment. even though such a solution has the advantage of simplicity, it requires careful processing of data associations. after a preliminary selection of pose constraints candidates, a relative pose is computed through standard scan matching techniques. the second map builder stores a hybrid metric-topological representation: the map consists of a graph whose nodes contain local occupancy grid maps and whose edges are labeled with the relative pose between pairs of nodes. each patch map summarizes the information of consecutive raw scans and such a richer representation better solves loop closure. association between pairs of local maps is then performed and tested using correlation-based techniques. our aim is to illustrate the effectiveness of a tree network optimizer integrated with simple methods for data association. experiments reported in the paper show that a compact system integrating the optimizer and one of two versions of the map builder works reasonably well with commonly used benchmarks.
contributions to non-identifier based adaptive control in mechatronics. funnel-control (fc)-an adaptive (time-varying) mimo/siso control strategy-is re-introduced and its applicability introductory for position control of nonlinear, coupled (rigid) robotic systems and for speed control of nonlinear two-mass flexible servo systems is shown. additionally error reference control (erc)-a direct derivative of fc-is established. erc is specially designed with asymmetric boundaries and auxiliary reference, ensuring that the control error evolves within a prespecified tube (a shrinked funnel region, achieving even better tracking performance). fc and hence erc are based on the high-gain stabilizability of minimum-phase systems with relative degree one and known sign of the high-frequency gain. although the plant only needs to be known in structure, both concepts assure prescribed transient behavior without identification and/or parameter estimation. as most industrial applications, also the considered robotic and two-mass flexible servo systems, exhibit relative degrees greater than one, fc and erc cannot directly be applied. therefore a special state feedback is introduced, reducing the relative degree and retaining the minimum-phase property. the additional implementation of a nominal pi-like extension guarantees good disturbance rejection and asymptotic tracking of (constant) velocity and position reference trajectories. simulation results for a 6-dof manutec r3 robot underpin and compare the achievable position control performance of one overall mimo funnel controller for all joints and one siso funnel controller for each joint (6 controllers). for nonlinear two-mass flexible servo systems, measurement results demonstrate the achievable load speed control performance of fc and erc in comparison to optimal lq state feedback.
complementing visual tracking of moving targets by fusion of tactile sensing. robot control in uncertain and dynamic environments can be greatly improved using sensor-based control. vision is a versatile low-cost sensory modality, but low sample rate, high sensor delay and uncertain measurements limit its usability, especially in strongly dynamic environments. vision can be used to estimate a 6-dof pose of an object by model-based pose-estimation methods, but the estimate is typically not accurate along all degrees of freedom. force is a complementary sensory modality allowing accurate measurements of local object shape when a tooltip is in contact with the object. in multimodal sensor fusion, several sensors measuring different modalities are combined together to give a more accurate estimate of the environment. as force and vision are fundamentally different sensory modalities not sharing a common representation, combining the information from these sensors is not straightforward. we show that the fusion of tactile and visual measurements enables to estimate the pose of a moving target at high rate and accuracy. making assumptions of the object shape and carefully modeling the uncertainties of the sensors, the measurements can be fused together in an extended kalman filter. experimental results show greatly improved pose estimates with the proposed sensor fusion.
a comparison of loop closing techniques in monocular slam. loop closure detection systems for monocular slam come in three broad categories: (i) map-to-map, (ii) image-to-image and (iii) image-to-map. in this paper, we have chosen an implementation of each and performed experiments allowing the three approaches to be compared. the sequences used include both indoor and outdoor environments and single and multiple loop trajectories.
energy based control of a hydromechanical system. this contribution deals with an energy based controller design for an under-actuated mechanical system with a hydraulic piston actuator. in particular, the example consists of a single acting piston actuator and a rigid mass between two springs. for the resulting system a static feedback control is designed based on energy consideration. since the control algorithm requires the velocities, which are not measurable in this application, it is extended by a reduced observer. in addition, the stability of the desired equilibrium of the closed loop system is proven and the results are illustrated by simulation.
global indoor self-localization based on the ambient magnetic field. there is evidence that animals utilize local anomalities of earth's magnetic field not just for orientation detection but also for true navigation, i.e., some animals are not only able to detect the direction of earth's magnetic field (compass heading), they are able to derive positional information from local cues arising from the local anomalities of earth's magnetic field. similarly to earth's non-constant magnetic field, the magnetic field inside buildings can be highly non-uniform. the magnetic field fluctuations inside buildings arise from both natural and man-made sources, such as steel and reinforced concrete structures, electric power systems, electric and electronic appliances, and industrial devices. assuming that the anomalities of the magnetic field inside a building are nearly static and they have sufficient local variability, the anomalies provide a unique magnetic fingerprint that can be utilized in global self-localization. based on the evidence presented in this article it can be argued that this hypothesis is valid. in this article, a monte carlo localization (mcl) technique based on the above hypothesis is proposed. the feasibility of the technique is demonstrated by presenting a series of global self-localization experiments conducted in four arbitrarily selected buildings, including a hospital. the experiment setup consists of a mobile robot instrumented with a 3-axis magnetometer and a computer. in addition to global robot self-localization experiments, successful person self-localization experiments were also conducted by using a wireless, wearable magnetometer. the reported experiments suggest that the ambient magnetic field may remain sufficiently stable for longer periods of time giving support for self-localization techniques utilizing the local deviations of the magnetic field.
auto-organized visual perception using distributed camera network. camera networks are complex vision systems difficult to control if the number of sensors is getting higher. with classic approaches, each camera has to be calibrated and synchronized individually. these tasks are often troublesome because of spatial constraints, and mostly due to the amount of information that need to be processed. cameras generally observe overlapping areas, leading to redundant information that are then acquired, transmitted, stored and then processed. we propose in this paper a method to segment, cluster and codify images acquired by cameras of a network. the images are decomposed sequentially into layers where redundant information are discarded. without the need of any calibration operation, each sensor contributes to build a global representation of the entire network environment. the information sent by the network is then represented by a reduced and compact amount of data using a codification process. this framework allows structures to be retrieved and also the topology of the network. it can also provide the localization and trajectories of mobile objects. experiments will present practical results in the case of a network containing 20 cameras observing a common scene.
dynamics analysis of the star parallel manipulator. matrix relations in kinematics and dynamics of the star parallel manipulator are established in this paper. the prototype of the manipulator is a three-degree-of-freedom mechanism, which consists of a system of parallel kinematical chains connecting to a moving platform. knowing the translation motion of the platform, we develop first the inverse kinematics problem and determine the position, velocity and acceleration of each robot's link. further, the inverse dynamics problem is solved using an approach based on the principle of virtual work, but it has been verified the results in the framework of the lagrange equations with their multipliers. recursive formulae offer expressions and graphs for the power requirement comparison of each of three actuators in two computational complexities: complete dynamic model and simplified dynamic model.