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1.
Santiago GarridoAuthor Vitae Luis MorenoAuthor Vitae 《Robotics and Autonomous Systems》2011,59(9):675-683
This paper presents the application of the Voronoi Fast Marching (VFM) method to path planning of mobile formation robots. The VFM method uses the propagation of a wave (Fast Marching) operating on the world model to determine a motion plan over a viscosity map (similar to the refraction index in optics) extracted from the updated map model. The computational efficiency of the method allows the planner to operate at high rate sensor frequencies. This method allows us to maintain good response time and smooth and safe planned trajectories. The navigation function can be classified as a type of potential field, but it has no local minima, it is complete (it finds the solution path if it exists) and it has a complexity of order n(O(n)), where n is the number of cells in the environment map. The results presented in this paper show how the proposed method behaves with mobile robot formations and generates trajectories of good quality without problems of local minima when the formation encounters non-convex obstacles. 相似文献
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针对未知环境中移动机器人同时定位和地图创建(Simultaneous Localization and Map Building,SLAM)由于机器人位姿和环境地图都不确定导致定位和地图创建变得更加复杂,提出一种局部最优(全局次优)参数法,即通过局部最优的位姿创建局部最优的环境地图,再通过局部最优的环境地图寻求局部最优的位姿,如此交替进行,直到得到全局确定性的位姿和确定性的环境地图。实验结果表明,同标准的基于粒子滤波的SLAM 算法(Particle Filtering-SLAM,PF-SLAM)比较,改进的算法提高了机器人SLAM过程中定位的准确度和地图创建的精确度,为机器人在未知的室外大环境同时定位和地图创建提供新的方法。 相似文献
3.
In field environments it is not usually possible to provide robots in advance with valid geometric models of its task and
environment. The robot or robot teams need to create these models by scanning the environment with its sensors. Here, an information-based
iterative algorithm to plan the robot's visual exploration strategy is proposed to enable it to most efficiently build 3D
models of its environment and task. The method assumes mobile robot (or vehicle) with vision sensors mounted at a manipulator
end-effector (eye-in-hand system). This algorithm efficiently repositions the systems' sensing agents using an information
theoretic approach and fuses sensory information using physical models to yield a geometrically consistent environment map.
This is achieved by utilizing a metric derived from Shannon's information theory to determine optimal sensing poses for the
agent(s) mapping a highly unstructured environment. This map is then distributed among the agents using an information-based
relevant data reduction scheme. This method is particularly well suited to unstructured environments, where sensor uncertainty
is significant. Issues addressed include model-based multiple sensor data fusion, and uncertainty and vehicle suspension motion
compensation. Simulation results show the effectiveness of this algorithm. 相似文献
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Exploration is one of the most important functions for a mobile service robot because a map is required to carry out various tasks. A suitable strategy is needed to efficiently explore an environment and to build an accurate map. This study proposed the use of several gains (information, driving, localization) that, if considered during exploration, can simultaneously improve the efficiency of the exploration process and quality of the resulting map. Considering the information and driving gains reduces behavior that leads a robot to explore a previously visited place, and thus the exploration distance is reduced. In addition, the robot can select a favorable path for localization by considering the localization gain during exploration, and the robot can estimate its pose more robustly than other methods that do not consider localizability during exploration. This proposed exploration method was verified by various experiments, which verified that a robot can build an accurate map fully autonomously and efficiently in various home environments using the proposed method. 相似文献
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Oriolo G. Ulivi G. Vendittelli M. 《IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics》1998,28(3):316-333
An algorithmic solution method is presented for the problem of autonomous robot motion in completely unknown environments. Our approach is based on the alternate execution of two fundamental processes: map building and navigation. In the former, range measures are collected through the robot exteroceptive sensors and processed in order to build a local representation of the surrounding area. This representation is then integrated in the global map so far reconstructed by filtering out insufficient or conflicting information. In the navigation phase, an A*-based planner generates a local path from the current robot position to the goal. Such a path is safe inside the explored area and provides a direction for further exploration. The robot follows the path up to the boundary of the explored area, terminating its motion if unexpected obstacles are encountered. The most peculiar aspects of our method are the use of fuzzy logic for the efficient building and modification of the environment map, and the iterative application of A*, a complete planning algorithm which takes full advantage of local information. Experimental results for a NOMAD 200 mobile robot show the real-time performance of the proposed method, both in static and moderately dynamic environments. 相似文献
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机器人在未知环境自主探索时,需要快速准确地获取环境地图信息。针对高效探索和未知环境的地图构建问题,将随机行走算法应用于群机器人的探索中,机器人模拟布朗运动,对搜索区域建图。然后,改进了布朗运动算法,通过设置机器人随机行走时的最大旋转角度,来避免机器人重复性地搜索一个区域,使机器人在相同时间内探索更多的区域,提高机器人的搜索效率。最后,通过搭载激光雷达的多个移动机器人进行了仿真实验,实验分析了最大转角增量、机器人数量以及机器人运动步数对搜索区域的影响。 相似文献
8.
Joong-Tae Park Jae-Bok Song Se-Jin Lee Munsang Kim 《Journal of Intelligent and Robotic Systems》2011,63(3-4):465-480
This paper describes a sonar sensor-based exploration method. To build an accurate map in an unknown environment during exploration, a simultaneous localization and mapping problem must be solved. Therefore, a new type of sonar feature called a ??sonar salient feature?? (SS-feature), is proposed for robust data association. The key concept of an SS-feature is to extract circle feature clouds on salient convex objects from environments by associating sets of sonar data. The SS-feature is used as an observation in the extended Kalman filter (EKF)-based SLAM framework. A suitable strategy is needed to efficiently explore the environment. We used utilities of driving cost, expected information about an unknown area, and localization quality. Through this strategy, the exploration method can greatly reduce behavior that leads a robot to explore a previously visited place, and thus shorten the exploration distance. A robot can select a favorable path for localization by localization gain during exploration. Thus, the robot can estimate its pose more robustly than other methods that do not consider localizability during exploration. This proposed exploration method was verified by various experiments, and it ensures that a robot can build an accurate map fully autonomously with sonar sensors in various home environments. 相似文献
9.
This paper presents a hybrid path planning algorithm for the design of autonomous vehicles such as mobile robots. The hybrid
planner is based on Potential Field method and Voronoi Diagram approach and is represented with the ability of concurrent
navigation and map building. The system controller (Look-ahead Control) with the Potential Field method guarantees the robot generate a smooth and safe path to an expected position. The Voronoi
Diagram approach is adopted for the purpose of helping the mobile robot to avoid being trapped by concave environment while
exploring a route to a target. This approach allows the mobile robot to accomplish an autonomous navigation task with only
an essential exploration between a start and goal position. Based on the existing topological map the mobile robot is able
to construct sub-goals between predefined start and goal, and follows a smooth and safe trajectory in a flexible manner when
stationary and moving obstacles co-exist. 相似文献
10.
Jhielson M. Pimentel Mário S. Alvim Mario F. M. Campos Douglas G. Macharet 《Journal of Intelligent and Robotic Systems》2018,91(2):313-331
Exploration of unknown environments using autonomous mobile robots is essential in various scenarios such as, for instance, search and rescue missions following natural disasters. The task consists essentially in transversing the environment to build a complete and accurate map of it, and different applications may demand different exploration strategies. In the literature, the most used strategy is a simple greedy approach which visits closest unknown sites first, without considering whether they will likely yield significant information gain about the environment. In this paper, we propose a navigation strategy for efficient exploration of unknown environments that, based on local structures present in the map built so far, uses Shannon entropy to estimate the expected information gain of exploring each candidate frontier. A key advantage of our method over the state of the art is that it allows for the robot to simultaneously (i) select a destination likely to be most informative among all candidate frontiers; and (ii) compute its own path to that destination. This unified approach balances priority among candidate frontiers with highly expected information gain and those closer to the current position of the robot. We thoroughly evaluate our methodology in several experiments in a simulated environment, showing that our approach provides faster information gain about the environment when compared to other exploration strategies. 相似文献
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Luis Moreno Santiago Garrido Dolores Blanco M. Luisa Muñoz 《Robotics and Autonomous Systems》2009,57(4):441-450
A new solution to the Simultaneous Localization and Modelling problem is presented in this paper. The algorithm is based on the stochastic search for solutions in the state space to the global localization problem by means of a differential evolution algorithm. This non linear evolutive filter, called Evolutive Localization Filter (ELF), searches stochastically along the state space for the best robot pose estimate. The set of pose solutions (the population) focuses on the most likely areas according to the perception and up to date motion information. The population evolves using the log-likelihood of each candidate pose according to the observation and the motion errors derived from the comparison between observed and predicted data obtained from the probabilistic perception and motion model.The proposed SLAM algorithm operates in two steps: in the first step the ELF filter is used at local level to re-localize the robot based on the robot odometry, the laser scan at a given position and a local map where only a low number of the last scans have been integrated. In the second step, the aligned laser measures and the corrected robot poses are used to detect whether the robot is revisiting a previously crossed area (i.e., a cycle in the robot trajectory exists). Once a cycle is detected, the Evolutive Localization Filter is used again to estimate the accumulated residual drift in the detected loop and then to re-estimate the robot poses in order to integrate the sensor measures in the global map of the environment.The algorithm has been tested in different environments to demonstrate the effectiveness, robustness and computational efficiency of the proposed approach. 相似文献
13.
In field environments it is not usually possible to provide robots in advance with valid geometric models of its environment and task element locations. The robot or robot teams need to create and use these models to locate critical task elements by performing appropriate sensor based actions. This paper presents a multi-agent algorithm for a manipulator guidance task based on cooperative visual feedback in an unknown environment. First, an information-based iterative algorithm to intelligently plan the robots visual exploration strategy is used to enable it to efficiently build 3D models of its environment and task elements. The algorithm uses the measured scene information to find the next camera position based on expected new information content of that pose. This is achieved by utilizing a metric derived from Shannons information theory to determine optimal sensing poses for the agent(s) mapping a highly unstructured environment. Second, after an appropriate environment model has been built, the quality of the information content in the model is used to determine the constraint-based optimum view for task execution. The algorithm is applicable for both an individual agent as well as multiple cooperating agents. Simulation and experimental demonstrations on a cooperative robot platform performing a two component insertion/mating task in the field show the effectiveness of this algorithm. 相似文献
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《Robotics and Autonomous Systems》2006,54(7):546-558
This paper presents an efficient geometric approach to the Simultaneous Localization and Mapping problem based on an Extended Kalman Filter. The map representation and building process is formulated, fully implemented and successfully experimented in different indoor environments with different robots. The use of orthogonal shape constraints is proposed to deal with the inconsistency of the estimation. Built maps are successfully used for the navigation of two different service robots: an interactive tour guide robot, and an assistive walking aid for the frail elderly. 相似文献
16.
Fredy TungadiAuthor Vitae Lindsay Kleeman Author Vitae 《Robotics and Autonomous Systems》2011,59(6):428-443
This paper describes an object rearrangement system for an autonomous mobile robot. The objective of the robot is to autonomously explore and learn about an environment, to detect changes in the environment on a later visit after object disturbances and finally, to move objects back to their original positions. In the implementation, it is assumed that the robot does not have any prior knowledge of the environment and the positions of the objects. The system exploits Simultaneous Localisation and Mapping (SLAM) and autonomous exploration techniques to achieve the task. These techniques allow the robot to perform localisation and mapping which is required to perform the object rearrangement task autonomously. The system includes an arrangement change detector, object tracking and map update that work with a Polar Scan Match (PSM) Extended Kalman Filter (EKF) SLAM system. In addition, a path planning technique for dragging and pushing an object is also presented in this paper. Experimental results of the integrated approach are shown to demonstrate that the proposed approach provides real-time autonomous object rearrangements by a mobile robot in an initially unknown real environment. Experiments also show the limits of the system by investigating failure modes. 相似文献
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Making 2D Map of Environments Based upon Routes Scenes 总被引:1,自引:0,他引:1
This paper proposes a method for making a map of large scale environment based upon route scenes, assuming that the topological relation of routes at intersections is known. A panoramic representation is used for describing route scenes, and the number of routes connecting at an intersection is assumed to be known. The idea is to decompose a 2D graph into a number of closed loops. By detecting the closed loops and storing the relation among them, we can describe the 2D map based upon route scenes. A robot can obtain a closed loop by taking the same turn (leftmost for example) at every intersection when it moves along routes. According to the information on routes at intersections, the robot can select unmoved routes for finding new closed loops. By fusing new closed loops with found ones, the robot can, further, build the map of environments. The effectiveness of our method are shown by experiment in a real-world environment. 相似文献
19.
Castejon C. Blanco D. Moreno L. 《IEEE transactions on systems, man, and cybernetics. Part A, Systems and humans : a publication of the IEEE Systems, Man, and Cybernetics Society》2008,38(1):9-24
In this paper, a new methodology to build compact local maps in real time for outdoor robot navigation is presented. The environment information is obtained from a 3-D scanner laser. The navigation model, which is called traversable region model, is based on a Voronoi diagram technique, but adapted to large outdoor environments. The model obtained with this methodology allows a definition of safe trajectories that depend on the robot's capabilities and the terrain properties, and it will represent, in a topogeometric way, the environment as local and global maps. The application presented is validated in real outdoor environments with the robot called GOLIAT. 相似文献
20.
This paper addresses the problem of Simultaneous Localization and Mapping (SLAM) for very large environments. A hybrid architecture is presented that makes use of the Extended Kalman Filter to perform SLAM in a very efficient form and a Monte Carlo localizer to resolve data association problems potentially present when returning to a known location after a large exploration period. Algorithms to improve the convergence of the Monte Carlo filter are presented that consider vehicle and sensor uncertainty. The proposed algorithm incorporates significant integrity to the standard SLAM algorithms by providing the ability to handle multimodal distributions over robot pose in real time during the re‐localization process. Experimental results in outdoor environments are presented to demonstrate the performance of the algorithm proposed. © 2003 Wiley Periodicals, Inc. 相似文献