一种基于视觉的水下机器人动力定位方法

以7 000 m载人潜水器的工程需求为背景,以水下单目摄像机为视觉传感器,进行了水下机器人动力定位方法研究。该动力定位方法利用视觉系统测量得到水下机器人与被观察目标之间的三维位姿关系,通过路径规划、位置控制和姿态控制分解,逐步使机器人由初始位姿逼近期望位姿并最终定位于期望位姿,从而实现了机器人的4自由度动力定位。通过水池实验验证了提出的动力定位方法,并且机器人能够抵抗恒定水流干扰和人工位置扰动。同时,该动力定位方法还可以实现机器人对被观察目标的自动跟踪。

一种基于视觉的移动机器人定位系统

具有自主的全局定位能力是自主式移动机器人传感器系统的一项重要功能 .为了实现这个目的 ,国内外均在不断地研究发展各种定位传感器系统 .这里介绍了一种采用光学原理的全方位位置传感器系统 .该传感器系统由主动式路标、视觉传感器、图象采集与数据处理系统组成 .其视觉传感器和数据处理系统可安装在移动机器人上 ,然后可通过观测路标和视角定位的方法 ,计算出机器人在世界坐标系中的位置和方向 .实验证明 ,该系统可以实现机器人的在线定位 ,其采样速率和精度能够满足实用要求 .

一个简单的机器人视觉引导装配实验系统

本文对视觉控制下的一个简单实验室装配系统作了介绍,讨论了系统组成、机器人控制、二维图象特征的提取、对物体自动识别、定位定向、系统标定、实现垒积木装配工作.本实验系统用的是我所研制的国内第一台示教再现机器人.

PUMA760基于红外接近觉控制与编程实验系统

本文介绍 PUMA760 工业机器人基于红外接近觉传感器信号控制与编程实验系统。提出了搜索信号极值确定基准相对位置的方法,在位置控制系统中加入了基于传感器信号控制的指令,使 PUMA760完成了快速寻找、接近、定位和抓取非完全固定方位和高度的工件的动作.

Theoretical study of determining orientation and alignment of symmetric top molecule using laser-induced fluorescence

General expressions used for extracting the orientation and alignment parameters of a symmetric top molecule from laser-induced fluorescence (LIF) intensity are derived by employing the density matrix approach. The molecular orientation and alignment are described by molecular state multipoles. Excitation and detection are circularly and linearly polarized lights, respectively. In general cases, the LIF intensity is a complex function of the initial molecular state multipoles, the dynamic factors and the excitation-detection geometrical factors. It contains a population, ten orientation and fourteen alignment multipoles. The problem of how to extract the initial molecular state multipoles from the resolved LIF intensity is discussed.

Affine Matching with Bounded Sensor Error: A Study of Geometric Hashing and Alignment

Affine transformations are often used in recognition systems, to approximate the effects of perspective projection. The underlying mathematics is for exact feature data, with no positional uncertainty. In practice, heuristics are added to handle uncertainty. We provide a precise analysis of affine point matching, obtaining an expression for the range of affine-invariant values consistent with bounded uncertainty. This analysis reveals that the range of affine-invariant values depends on the actual $x$-$y$-positions of the features, i.e. with uncertainty, affine representations are not invariant with respect to the Cartesian coordinate system. We analyze the effect of this on geometric hashing and alignment recognition methods.

Calibração e validação de sensor dielétrico para estimativa de teor de água em solos do Cerrado.

2008

Declarative sensor knowledge in a robot monitoring system

Hardy, N. W., Barnes, D. P., Lee, M. (1987). Declarative sensor knowledge in a robot monitoring system. In: Languages for Sensor-Based Control in Robotics, Ulrich Rembold and Klaus H?rmann (eds), Springer-Verlag, p. 169-188.

A control and monitoring system for multiple-sensor industrial robots

Barnes, D. P., Lee, M. H., Hardy, N. W. (1983). A control and monitoring system for multiple-sensor industrial robots. In Proc. 3rd. Int. Conf. Robot Vision and Sensory Controls, Cambridge, MA. USA., 471-479.

The effect of market and learning orientation on strategy dynamics : The contributing effect of organisational change capability

McGuinness, T. and Morgan, R. (2005). The effect of market and learning orientation on strategy dynamics: The contributing effect of organisational change capability. European Journal of Marketing. 39(11-12), pp.1306-1326 RAE2008

SEP: A Stable Election Protocol for clustered heterogeneous wireless sensor networks

We study the impact of heterogeneity of nodes, in terms of their energy, in wireless sensor networks that are hierarchically clustered. In these networks some of the nodes become cluster heads, aggregate the data of their cluster members and transmit it to the sink. We assume that a percentage of the population of sensor nodes is equipped with additional energy resources-this is a source of heterogeneity which may result from the initial setting or as the operation of the network evolves. We also assume that the sensors are randomly (uniformly) distributed and are not mobile, the coordinates of the sink and the dimensions of the sensor field are known. We show that the behavior of such sensor networks becomes very unstable once the first node dies, especially in the presence of node heterogeneity. Classical clustering protocols assume that all the nodes are equipped with the same amount of energy and as a result, they can not take full advantage of the presence of node heterogeneity. We propose SEP, a heterogeneous-aware protocol to prolong the time interval before the death of the first node (we refer to as stability period), which is crucial for many applications where the feedback from the sensor network must be reliable. SEP is based on weighted election probabilities of each node to become cluster head according to the remaining energy in each node. We show by simulation that SEP always prolongs the stability period compared to (and that the average throughput is greater than) the one obtained using current clustering protocols. We conclude by studying the sensitivity of our SEP protocol to heterogeneity parameters capturing energy imbalance in the network. We found that SEP yields longer stability region for higher values of extra energy brought by more powerful nodes.

DIP: Density Inference Protocol for wireless sensor networks and its application to density-unbiased statistics

Wireless sensor networks have recently emerged as enablers of important applications such as environmental, chemical and nuclear sensing systems. Such applications have sophisticated spatial-temporal semantics that set them aside from traditional wireless networks. For example, the computation of temperature averaged over the sensor field must take into account local densities. This is crucial since otherwise the estimated average temperature can be biased by over-sampling areas where a lot more sensors exist. Thus, we envision that a fundamental service that a wireless sensor network should provide is that of estimating local densities. In this paper, we propose a lightweight probabilistic density inference protocol, we call DIP, which allows each sensor node to implicitly estimate its neighborhood size without the explicit exchange of node identifiers as in existing density discovery schemes. The theoretical basis of DIP is a probabilistic analysis which gives the relationship between the number of sensor nodes contending in the neighborhood of a node and the level of contention measured by that node. Extensive simulations confirm the premise of DIP: it can provide statistically reliable and accurate estimates of local density at a very low energy cost and constant running time. We demonstrate how applications could be built on top of our DIP-based service by computing density-unbiased statistics from estimated local densities.

On the Interaction between Data Aggregation and Topology Control in Wireless Sensor Networks

Wireless sensor networks are characterized by limited energy resources. To conserve energy, application-specific aggregation (fusion) of data reports from multiple sensors can be beneficial in reducing the amount of data flowing over the network. Furthermore, controlling the topology by scheduling the activity of nodes between active and sleep modes has often been used to uniformly distribute the energy consumption among all nodes by de-synchronizing their activities. We present an integrated analytical model to study the joint performance of in-network aggregation and topology control. We define performance metrics that capture the tradeoffs among delay, energy, and fidelity of the aggregation. Our results indicate that to achieve high fidelity levels under medium to high event reporting load, shorter and fatter aggregation/routing trees (toward the sink) offer the best delay-energy tradeoff as long as topology control is well coordinated with routing.

M^2RC: Multiplicative-increase/additive-decrease Multipath Routing Control for Wireless Sensor Networks

Routing protocols in wireless sensor networks (WSN) face two main challenges: first, the challenging environments in which WSNs are deployed negatively affect the quality of the routing process. Therefore, routing protocols for WSNs should recognize and react to node failures and packet losses. Second, sensor nodes are battery-powered, which makes power a scarce resource. Routing protocols should optimize power consumption to prolong the lifetime of the WSN. In this paper, we present a new adaptive routing protocol for WSNs, we call it M^2RC. M^2RC has two phases: mesh establishment phase and data forwarding phase. In the first phase, M^2RC establishes the routing state to enable multipath data forwarding. In the second phase, M^2RC forwards data packets from the source to the sink. Targeting hop-by-hop reliability, an M^2RC forwarding node waits for an acknowledgement (ACK) that its packets were correctly received at the next neighbor. Based on this feedback, an M^2RC node applies multiplicative-increase/additive-decrease (MIAD) to control the number of neighbors targeted by its packet broadcast. We simulated M^2RC in the ns-2 simulator and compared it to GRAB, Max-power, and Min-power routing schemes. Our simulations show that M^2RC achieves the highest throughput with at least 10-30% less consumed power per delivered report in scenarios where a certain number of nodes unexpectedly fail.