基于主动视觉和超声的机器人目标跟踪系统

运动目标跟踪技术是未知环境下移动机器人研究领域的一个重要研究方向。该文提出了一种基于主动视觉和超声信息的移动机器人运动目标跟踪设计方法,利用一台SONY EV-D31彩色摄像机、自主研制的摄像机控制模块、图像采集与处理单元等构建了主动视觉系统。移动机器人采用了基于行为的分布式控制体系结构,利用主动视觉锁定运动目标,通过超声系统感知外部环境信息,能在未知的、动态的、非结构化复杂环境中可靠地跟踪运动目标。实验表明机器人具有较高的鲁棒性,运动目标跟踪系统运行可靠。

移动机器人运动目标跟踪系统设计

利用SONYEV-D31摄像机和自主研发的摄像机控制模块,构建了一套主动视觉子系统,并将该子系统应用于RIRA-II型移动机器人上,实现了移动机器人运动目标自动跟踪功能。RIRA-II移动机器人采用了由一组分布式行为模块和集中命令仲裁器组成的基于行为的分布式控制体系结构。各行为模块基于领域知识通过反应方式产生投票,由仲裁器产生动作指令,机器人完成相应的动作。在设置了障碍、窄通道以及模拟墙体的复杂环境下进行运动目标跟踪实验,实验表明运动目标跟踪系统运行可靠,具有较高的鲁棒性。

An ambient proof and wide tuning range LC VCO with automatic amplitude control for tuner applications

This paper represents a LC VCO with AAC (Auto Amplitude Control), in which PMOS FETs are used as active components, and the varactors are directly connected to ground to widen Kvco linear range. The AAC circuitry adds little noise to the VCO and provides it with robust performance over a wide temperature and carrier frequency range. The VCO is fabricated in 50-GHz 0.35-mu m SiGe BiCMOS process. The measurement results show that it has -127.27-dBc/Hz phase noise at 1-MHz offset and a linear gain of 32.4-MHz/V between 990-MHz and 1.14-GHz. The whole circuit draws 6.6-mA current from 5.0-V supply.

A 1.1GHz LC VCO with Automatic Amplitude Control for Tuner Applications

This paper presents an LC VCO with auto-amplitude control （AAC）, in which pMOS FETs are used,and the varactors are directly connected to ground to widen the linear range of Kvco. The AAC circuitry adds little noise to the VCO but provides it with robust performance over a wide temperature and carrier frequency range.The VCO is fabricated in a chartered 50GHz 0.35μm SiGe BiCMOS process. The measurements show that it has - 127. 27dBc/Hz phase noise at 1MHz offset and a linear gain of 32.4MHz/V between 990MHz and 1.14GHz.The whole circuit draws 6. 6mA current from 5V supply.

a state-adaptive access control model for web-based idrs system

Huazhong Univ Sci & Technol, Natl Tech Univ Ukraine, Huazhong Normal Univ, Harbin Inst Technol, IEEE Ukraine Sect, I& M/CI Joint Chapter

Photoperiodic long-day control of sporophyll and hair formation in the brown alga Undaria pinnatifida

Two photoperiodic responses, the development of sporophylls and hairs, have been quantified in sporophytes of the brown alga Undaria pinnatifida. In a final experiment, the algae were cultivated in outdoor, 2000-L seawater tanks in a greenhouse for up to 12 weeks, and daylength was regulated by automatic blinds mounted on top of the tanks. Vegetative young sporophytes were treated under short-day (SD; 8 h light per day) or long-day conditions (LD; 16 h light per day), at 12 h light per day or in a night-break regime (NB; 8 h light per day, 7.5 h dark, 1 h light, 7.5 h dark). The earliest sporophyll development was observed 6, 7 or 9 weeks under LD, NB or SD conditions, respectively. After 12 weeks the sporophylls were significantly longer and wider under LD or NB conditions than in the SD regime, and only half of the experimental algae had formed sporophylls under SD conditions, but all algae under LD or NB conditions. In a foregoing 7-week culture experiment performed in 300-L indoor tanks, enhanced sporophyll formation had also been observed under LD and not under SD conditions (NB omitted). In both experiments, blade elongation rates remained high until the end of the experiments in SD, but declined during sporophyll initiation in LD, NB or at 12 h light per day. Another difference caused by photoperiod was observed in regard to the development of surface hair spots which occurred in both experiments on the blades in LD, NB or at 12 h light per day with identical densities, but were completely lacking under SD conditions. It is concluded that U. pinnatifida is a facultatative long-day plant in regard to reproduction forming vigorously sporophylls in long days, and an obligate long-day plant in regard to hair formation.

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。