Application of arachnid prey localisation theory for a robot sensorimotor controller

We extend an existing spiking neural model of arachnid prey orientation sensing with a view to potentially using it in robotics applications. Firstly, we have added ‘motor’ behaviour by implementing a simulated arachnid in a physics simulation so that sensory signals from the neural model can be translated into movement to orient towards the prey. We have also created a spiking neural distance estimation model with a complementary motor model that enables walking towards the prey. Results from testing of the neural and motor aspects show that the neural models can represent actual prey angle and distance to a high degree of accuracy: an average error of approximately 7° in estimating prey angle and 1 cm in the estimation of distance to prey. The motor models consistently show the correct turning and walking responses but the overall accuracy is reduced with an average error of around 15° for angle and 1.25 cm for distance. In the case of orientation this is still in line with the error rate of between 12° and 15°, which has been observed in real arachnids.     

通过WiFi移动IP网络操控家用机器人方案在PDA上的实现

本文提出了一种利用PDA通过WiFi移动IP网络操控家用机器人的新方法。该方法充分发挥了WiFi移动IP网络的稳定、高带宽和移动性等优点,开发了一个全新的运行在PDA上的机器人控制软件,命名为Robot PDA Teleoperation。实现了使用PDA通过WiFi移动IP网络操控家用机器人的新方法,完善了家用机器人的控制方式。该方案可以解决现有家用机器人控制方式单一、操作不方便、便携性差、使用成本高、不易于推广等不足。我们基于加拿大DrRobot公司的X80-H型机器人进行了大量实验。实验结果表本文提出的家用机器人控制方案是可行的。     

一种基于WiFi无线网络具有认字功能的家庭教育机器人

针对前景广阔的家庭机器人市场以及解决传统家庭机器人存在的技术不成熟、成本高等问题,提出了一种基于WiFi无线网络的家庭教育机器人实现方案。该方案由机器人、PC机、路由器构成,组网简单,成本较低,非常适合家庭环境。方案中机器人能够朗读汉字、课文以及通过摄像头识别书写在白纸上的汉字,适用于儿童早期认字教育。为此,为认字功能专门设计了汉字图像提取、识别算法,单字识别率达到97%。基于DrRobot公司X80-H型机器人上实现了该系统并对各个功能进行了大量测试实验。实验结果表明系统具有较强的鲁棒性,本方案是有效可     

Extended Kalman and Particle Filtering for sensor fusion in motion control of mobile robots

Motion control of mobile robots and efficient trajectory tracking is usually based on prior estimation of the robots’ state vector. To this end Gaussian and nonparametric filters (state estimators from position measurements) have been developed. In this paper the Extended Kalman Filter which assumes Gaussian measurement noise is compared to the Particle Filter which does not make any assumption on the measurement noise distribution. As a case study the estimation of the state vector of a mobile robot is used, when measurements are available from both odometric and sonar sensors. It is shown that in this kind of sensor fusion problem the Particle Filter has better performance than the Extended Kalman Filter, at the cost of more demanding computations.     

基于颜色特征与SIFT特征自适应融合的粒子滤波跟踪算法

针对序列图像中的运动目标在跟踪过程中发生运动模糊以及部分遮挡的问题进行了研究, 提出一种将改进的颜色直方图特征模型与尺度不变特征（SIFT）模型相融合的粒子滤波跟踪算法。采用基于模糊逻辑的方法, 根据当前跟踪环境自适应调节两种特征信息的权重, 从而实现特征信息间的融合, 提高描述目标观测的可靠性。实验结果证明, 该算法优于传统的单特征或采用固定权值的多特征目标跟踪算法。     

基于无线传感器网络的机器人定位跟踪研究

针对基于无线传感器网络的机器人定位提出了一种分段极大似然质心算法。将质心法引入极大似然估计算法中,通过计算已预测结果的质心提高目标位置的预测精度。考虑到WSN系统的超声定位实时性较差,采用扩展卡尔曼滤波算法将WSN系统改进定位算法与机器人航位推算进行融合以跟踪机器人位姿,从而提高了定位精度和系统动态性能。仿真结果表明：在不同锚节点个数和不同测距误差条件下,分段极大似然质心算法均能取得良好的定位效果;采用扩展卡尔曼滤波算法的数据融合,进一步提高了机器人轨迹跟踪的精度。     

Robust face tracking control of a mobile robot using self‐tuning Kalman filter and echo state network

This paper presents a novel design of face tracking algorithm and visual state estimation for a mobile robot face tracking interaction control system. The advantage of this design is that it can track a user's face under several external uncertainties and estimate the system state without the knowledge about target's 3D motion‐model information. This feature is helpful for the development of a real‐time visual tracking control system. In order to overcome the change in skin color due to light variation, a real‐time face tracking algorithm is proposed based on an adaptive skin color search method. Moreover, in order to increase the robustness against colored observation noise, a new visual state estimator is designed by combining a Kalman filter with an echo state network‐based self‐tuning algorithm. The performance of this estimator design has been evaluated using computer simulation. Several experiments on a mobile robot validate the proposed control system. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society