Global path planning for mobile robot with grid-type world model

This paper presents a new methodology for global path planning for an autonomous mobile robot in a grid-type world model. The value of a certainty grid representing the existence of an obstacle in the grid is calculated from readings of sonar sensors. In the calculation, a way of utilizing three sonar sensor readings at a time is introduced, resulting in a more accurate world model. Once the world model is obtained, a network for path planning is built by using the model. The global paths, defined as the shortest paths between all pairs of nodes in the network, are calculated. A fast algorithm using a decomposition technique is proposed for real-time calculation. The new methodology has been implemented on the mobile robot whose role is to transport materials in a flexible manufacturing system. The results show that the proposed method of certainty grids satisfactorily represents a precise environment, including the locations of obstacles. Thus, the robot successfully comprehends its surroundings, and navigates to its destinations along optimal paths.     

Efficient terrain coverage for deploying wireless sensor nodes on multi-robot system

Coverage and connectivity are the two main functionalities of wireless sensor network. Stochastic node deployment or random deployment almost always cause hole in sensing coverage and cause redundant nodes in area. In the other hand precise deployment of nodes in large area is very time consuming and even impossible in hazardous environment. One of solution for this problem is using mobile robots with concern on exploration algorithm for mobile robot. In this work an autonomous deployment method for wireless sensor nodes is proposed via multi-robot system which robots are considered as node carrier. Developing an exploration algorithm based on spanning tree is the main contribution and this exploration algorithm is performing fast localization of sensor nodes in energy efficient manner. Employing multi-robot system and path planning with spanning tree algorithm is a strategy for speeding up sensor nodes deployment. A novel improvement of this technique in deployment of nodes is having obstacle avoidance mechanism without concern on shape and size of obstacle. The results show using spanning tree exploration along with multi-robot system helps to have fast deployment behind efficiency in energy.     

WSN in cyber physical systems: Enhanced energy management routing approach using software agents

Recently, the cyber physical system has emerged as a promising direction to enrich the interactions between physical and virtual worlds. Meanwhile, a lot of research is dedicated to wireless sensor networks as an integral part of cyber physical systems. A wireless sensor network (WSN) is a wireless network consisting of spatially distributed autonomous devices that use sensors to monitor physical or environmental conditions. These autonomous devices, or nodes, combine with routers and a gateway to create a typical WSN system. Shrinking size and increasing deployment density of wireless sensor nodes implies the smaller equipped battery size. This means emerging wireless sensor nodes must compete for efficient energy utilization to increase the WSN lifetime. The network lifetime is defined as the time duration until the first sensor node in a network fails due to battery depletion. One solution for enhancing the lifetime of WSN is to utilize mobile agents. In this paper, we propose an agent-based approach that performs data processing and data aggregation decisions locally i.e., at nodes rather than bringing data back to a central processor (sink). Our proposed approach increases the network lifetime by generating an optimal routing path for mobile agents to transverse the network. The proposed approach consists of two phases. In the first phase, Dijkstra’s algorithm is used to generate a complete graph to connect all source nodes in a WSN. In the second phase, a genetic algorithm is used to generate the best-approximated route for mobile agents in a radio harsh environment to route the sensory data to the base-station. To demonstrate the feasibility of our approach, a formal analysis and experimental results are presented.     

Path planning of wheeled mobile robot with simultaneous free space locating capability

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.     

Cooperative localization for disconnected sensor networks and a mobile robot in friendly environments

Localization for a disconnected sensor network is highly unlikely to be achieved by its own sensor nodes, since accessibility of the information between any pair of sensor nodes cannot be guaranteed. In this paper, a mobile robot (or a mobile sensor node) is introduced to establish correlations among sparsely distributed sensor nodes which are disconnected, even isolated. The robot and the sensor network operate in a friendly manner, in which they can cooperate to perceive each other for achieving more accurate localization, rather than trying to avoid being detected by each other. The mobility of the robot allows for the stationary and internally disconnected sensor nodes to be dynamically connected and correlated. On one hand, the robot performs simultaneous localization and mapping (SLAM) based on the constrained local submap filter (CLSF). The robot creates a local submap composed of the sensor nodes present in its immediate vicinity. The locations of these nodes and the pose (position and orientation angle) of the robot are estimated within the local submap. On the other hand, the sensor nodes in the submap estimate the pose of the robot. A parallax-based robot pose estimation and tracking (PROPET) algorithm, which uses the relationship between two successive measurements of the robot's range and bearing, is proposed to continuously track the robot's pose with each sensor node. Then, tracking results of the robot's pose from different sensor nodes are fused by the Kalman filter (KF). The multi-node fusion result are further integrated with the robot's SLAM result within the local submap to achieve more accurate localization for the robot and the sensor nodes. Finally, the submap is projected and fused into the global map by the CLSF to generate localization results represented in the global frame of reference. Simulation and experimental results are presented to show the performances of the proposed method for robot-sensor network cooperative localization. Especially, if the robot (or the mobile sensor node) has the same sensing ability as the stationary sensor nodes, the localization accuracy can be significantly enhanced using the proposed method.     

基于局部信息的移动感知网覆盖方法*

移动感知网是一个由许多带有传感器的自主移动机器人组成的分布式传感器网络。为了更好地部署这些移动机器人节点,形成最大化覆盖感知区域,提出了一种基于机器人局部信息的分布式感知网覆盖方法。每个节点利用与邻居节点之间的虚拟人工势场产生的虚拟作用力来控制移动节点的运动和节点间的避碰,使移动节点能够在允许的时间内,以较少的能量消耗移动到各自理想的位置。采用李亚普诺夫函数进行了感知网节点势场梯度的理论分析,用计算机仿真实验验证了该方法的有效性,并与模拟退火算法进行了性能比较。     

Sensor planning for mobile robot localization using Bayesian network inference

We propose a new method of sensor planning for mobile robot localization using Bayesian network inference. Since we can model causal relations between situations of the robot's behavior and sensing events as nodes of a Bayesian network, we can use the inference of the network for dealing with uncertainty in sensor planning and thus derive appropriate sensing actions. In this system we employ a multi-layered-behavior architecture for navigation and localization. This architecture effectively combines mapping of local sensor information and the inference via a Bayesian network for sensor planning. The mobile robot recognizes the local sensor patterns for localization and navigation using a learned regression function. Since the environment may change during the navigation and the sensor capability has limitations in the real world, the mobile robot actively gathers sensor information to construct and reconstruct a Bayesian network, and then derives an appropriate sensing action which maximizes a utility function based on inference of the reconstructed network. The utility function takes into account belief of the localization and the sensing cost. We have conducted some simulation and real robot experiments to validate the sensor planning system.     

Simultaneous localization of multiple unknown CSMA-based wireless sensor network nodes using a mobile robot with a directional antenna

We use a single mobile robot equipped with a directional antenna to simultaneously localize unknown carrier sensing multiple access (CSMA)-based wireless sensor network nodes. We assume the robot can only sense radio transmissions at the physical layer. The robot does not know network configuration such as size and protocol. We formulate this new localization problem and propose a particle filter-based localization approach. We combine a CSMA model and a directional antenna model using multiple particle filters. The CSMA model provides network configuration data while the directional antenna model provides inputs for particle filters to update. Based on the particle distribution, we propose a robot motion planning algorithm that assists the robot to efficiently traverse the field to search radio source. The final localization scheme consists of two algorithms: a sensing algorithms that runs in O(n) time for n particles and a motion planning algorithm that runs in O(nl) time for l radio sources. We have implemented the algorithm, and the results show that the algorithms are capable of localizing unknown networked radio sources effectively and robustly.     

基于机器人群的主动传感器网络自组织的运动规划

主动传感器网络的自组织通常要求移动节点群(机器人群)通过障碍物环境移动到指定地点后, 重新调整并按预定布局组网. 在网络的自组织过程中要保证每个移动节点(机器人)与整个网络之间的连通性. 在对移动机器人的保持连通性进行优化的基础上, 提出了单步位置预测与群体势场相结合的分布式运动规划方法进行主动传感器网络的部署和重置, 证明了机器人运动控制的稳定性和网络的连通性保持, 进行了有和无障碍物环境下超过40个机器人的仿真, 结果表明该方法适用于大规模的主动传感器网络重置, 并对不同规模的网络具有可扩展性.     

Robotic data mules for collecting data over sparse sensor fields

We present a robotic system for collecting data from wireless devices dispersed across a large environment. In such applications, deploying a network of stationary wireless sensors may be infeasible because many relay nodes must be deployed to ensure connectivity. Instead, our system utilizes robots that act as data mules and gather the data from wireless sensor network nodes. We address the problem of planning paths of multiple robots so as to collect the data from all sensors in the shortest time. In this new routing problem, which we call the data gathering problem (DGP), the total download time depends on not only the robots' travel time but also the time to download data from a sensor and the number of sensors assigned to the robot. We start with a special case of DGP in which the robots' motion is restricted to a curve that contains the base station at one end. For this version, we present an optimal algorithm. Next, we study the two‐dimensional version and present a constant factor approximation algorithm for DGP on the plane. Finally, we present field experiments in which an autonomous robotic data mule collects data from the nodes of a wireless sensor network deployed over a large field. © 2011 Wiley Periodicals, Inc.     

Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field

A new fuzzy-based potential field method is presented in this paper for autonomous mobile robot motion planning with dynamic environments including static or moving target and obstacles. Two fuzzy Mamdani and TSK models have been used to develop the total attractive and repulsive forces acting on the mobile robot. The attractive and repulsive forces were estimated using four inputs representing the relative position and velocity between the target and the robot in the x and y directions, in one hand, and between the obstacle and the robot, on the other hand. The proposed fuzzy potential field motion planning was investigated based on several conducted MATLAB simulation scenarios for robot motion planning within realistic dynamic environments. As it was noticed from these simulations that the proposed approach was able to provide the robot with collision-free path to softly land on the moving target and solve the local minimum problem within any stationary or dynamic environment compared to other potential field-based approaches.     

IMMAS算法在复杂环境下路径规划中的应用

移动机器人路径规划是机器人学的一个重要研究领域.文章将改进的MMAS蚁群算法引入路径规划,在栅格法建模的基础上,改进初始蚁群设置,使用可变的终点定义,终合考虑可选点与终点的距离、可选点的被访问次数以及各可选路径上的信息素强度来设计启发式因子,使用奖励机制更新信息素,动态确定最大最小信息素范围,建立了一种新型优化算法.仿真结果表明,利用改进的MMAS算法,可充分发挥蚁群算法的优越性,并减小了陷入停滞状态的可能性,快速搜索到最优解.     

Fuzzy maps: A new tool for mobile robot perception and planning

An essential component of an autonomous mobile robot is the exteroceptive sensory system. Sensing capabilities should be integrated with a method for extracting a representation of the environment from uncertain sensor data and with an appropriate planning algorithm. In this article, fuzzy logic concepts are used to introduce a tool useful for robot perception as well as for planning collision-free motions. In particular, a map of the environment is defined as the fuzzy set of unsafe points, whose membership function quantifies the possibility for each point to belong to an obstacle. The computation of this set is based on a specific sensor model and makes use of intermediate sets generated from range measures and aggregated by means of fuzzy set operators. This general approach is applied to a robot with ultrasonic rangefinders. The resulting map building algorithm performs well, as confirmed by a comparison with stochastic methods. The planning problem on fuzzy maps can be solved by defining various path cost functions, corresponding to different strategies, and by searching the map for optimal paths. To this end, proper instances of the A* algorithm are devised. Experimental results for a Nomad 200™ robot moving in a real-world environment are presented. © 1997 John Wiley & Sons, Inc.     

Intelligent mobile manipulator navigation using adaptive neuro-fuzzy systems

The work presented in this paper deals with the problem of autonomous and intelligent navigation of mobile manipulator, where the unavailability of a complete mathematical model of robot systems and uncertainties of sensor data make the used of approximate reasoning to the design of autonomous motion control very attractive.A modular fuzzy navigation method in changing and dynamic unstructured environments has been developed. For a manipulator arm, we apply the robust adaptive fuzzy reactive motion planning developed in [J.B. Mbede, X. Huang, M. Wang, Robust neuro-fuzzy sensor-based motion control among dynamic obstacles for robot manipulators, IEEE Transactions on Fuzzy Systems 11 (2) (2003) 249-261]. But for the vehicle platform, we combine the advantages of probabilistic roadmap as global planner and fuzzy reactive based on idea of elastic band. This fuzzy local planner based on a computational efficient processing scheme maintains a permanent flexible path between two nodes in network generated by a probabilistic roadmap approach. In order to consider the compatibility of stabilization, mobilization and manipulation, we add the input of system stability in vehicle fuzzy navigation so that the mobile manipulator can avoid stably unknown and/or dynamic obstacles. The purpose of an integration of robust controller and modified Elman neural network (MENN) is to deal with uncertainties, which can be translated in the output membership functions of fuzzy systems.     

基于联合节点行为策略的WSN覆盖控制算法

针对无线传感器网络在对移动目标节点覆盖过程中出现网络能量快速消耗问题，提出了一种基于联合节点行为策略的覆盖算法。根据网络模型建立传感器节点与目标节点从属关系，确定覆盖关联模型；利用概率理论求解邻居节点冗余覆盖度，确定最少传感器节点数量；给出了邻居节点覆盖期望值的求解方法；仿真实验表明，该算法与其他算法在网络覆盖率和网络生存周期两个性能指标上均提升了12.39%和15.01%，从而验证了算法的有效性。     

基于移动机器人的无线传感器网络数据收集方法

稀疏无线传感器网络中各传感器节点距离较远,而传统的静态数据收集方法要求各传感器节点直接通信,导致网络延迟时间长,能耗高。针对该问题,提出一种基于移动机器人的无线传感器数据收集方法。该方法首先由静态节点选择与路径最短的移动机器人作为簇头,移动机器人比较一定周期内检测到的邻居节点的平均剩余能量与整个网络传感器节点平均剩余能量,根据比较结果决定其是否移动,若移动则采用范围可控的随机移动策略;当移动机器人移动到新位置时,传感器节点更新路由,选择新的移动机器人作为簇头。仿真结果表明,与传统的静态无线传感器网络数据收集方法相比,基于移动机器人的无线传感器网络数据收集方法大大降低了数据传输延迟和节点能量消耗。     

Autonomous mobile robot global self-localization using Kohonen and region-feature neural networks

This article presents and compares two neural network-based approaches to global self-localization (GSL) for autonomous mobile robots using: (1) a Kohonen neural network, and (2) a region-feature neural network (RFNN). Both approaches categorize discrete regions of space (topographical nodes) in a manner similar to optical character recognition (OCR). That is, the mapped sonar data assumes the form of a character unique to that region. Hence, it is believed that an autonomous vehicle can determine which room it is in from sensory data gathered from exploration. With a robust exploration routine, the GSL solution can be time-, translation-, and rotation-invariant. The GSL solution can also become independent of the mobile robot used to collect the sensor data. This suggests that a single robot can transfer its knowledge of various learned regions to other mobile robots. The classification rate of both approaches are comparable and, thus, worthy of presentation. The observed pros and cons of both approaches are also discussed. © 1997 John Wiley & Sons, Inc.     

基于模糊Elman网络算法的移动机器人路径规划分析

为了调正移动机器人避障线路,建立了基于模糊Elman网络算法的移动机器人路径规划模型,并应用进行Matlab仿真分析。利用现有障碍物的距离信息来实现机器人步长的实施可控制与调节,防止移动机器人在做出准确避障行为之后因为没有设定合适的步长而导致撞上障碍物,以0.5作为机器人的最初运动步长。仿真结果表明,采用模糊Elman网络可以获得比其它两种方法更优的路径规划效果,同时对障碍物进行高效避让,由此实现最优的路径规划。采用模糊Elman网络来构建得到的路径规划算法能够满足规划任务的要求,同时还能够根据机器人处于不同工作空间中的情况进行灵活调整。     

Real-Time Dynamic Visual Tracking Using PSD Sensors and Extended Trapezoidal Motion Planning

A real-time visual servo tracking system for an industrial robot has been implemented using PSD (Position Sensitive Detector) cameras, neural networks, and an extended trapezoidal motion planning method. PSD and directly transduces the light's projected position on its sensor plane into an analog current and lends itself to fast real-time tracking. A neural network, after proper training, transforms the PSD sensor reading into a 3D position of the target, which is then input to an extended trapezoidal motion planning algorithm. This algorithm implements a continuous motion update strategy in response to an ever-changing sensor information from the moving target, while greatly reducing the tracking delay. This planning method is found to be very useful for sensor-based control such as moving target tracking or weld-seam tracking in which the robot needs to change its motion in real time in response to incoming sensor information. Further, for real-time usage of the neural net, a new architecture called LANN (Locally Activated Neural Network) has been developed based on the concept of CMAC input partitioning and local learning. Experimental evidence shows that an industrial robot can smoothly track a moving target of unknown motion with speeds of up to 1 m/s and with oscillation frequency up to 5 Hz.