Sensor management – what, why and how

Ng, Gee Wah; Ng, Khin Hua

doi:10.1016/s1566-2535(00)00009-9

Cited by 59 publications

(34 citation statements)

References 24 publications

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“…The reference angle data are derived from the experimental data stored in the PC software of the commercial attitude measurement equipment. The angular velocity data measured by the gyroscope and the angle data measured by the accelerometer, can be seen, the complementary filtering algorithm to get the angle after the elimination of the Gyroscope drift and high-frequency disturbance of the accelerometer [9], the following conclusions can be drawn:…”

Section: The Experimental Resultsmentioning

confidence: 94%

Application of Multi-order Complementary Filtering Technique in Dynamic Attitude Measurement of Forestry Special Robots

Liu¹,

Ren²,

Deng³

2017

Proceedings of the 2017 International Conference on Electronic Industry and Automation (EIA 2017)

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Abstract-The wide application foreground of forestry special robots makes it become the hotspot of current research. The attitude angle measurement [1] of the special crawler-type robot is one of the key problems. Based on the complementary filtering algorithm, the attitude and angle measuring circuit and data processing algorithm of the special crawler robot are designed. The signal filtering and preprocessing are used to combine the two sensor data. The calculation method of the key parameters in the complementary filtering algorithm is analyzed. The method is applied to the angle measurement of the special crawler robot, and the verification test is carried out. And the experiment result shows that this experiment method is applicable to forestry robots.Keywords-forestry special robots; multi-order complementary filtering; integral drift; attitude solving I. INTRODUCTION Attitude angle measurement is a prerequisite for the operation and control of special crawler-type robots. The accuracy and speed of the attitude angle measurement [2] will directly affect the stability and reliability of the control algorithm of the special crawler robot. With the miniaturization of the inertial measurement components and the improvement of the computing power of the microprocessors, the low-cost inertial measurement unit (IMU) is widely used in posture measurement of forestry special crawler-type robots, and high accuracy attitude measurement is realized by combining the microprocessor data processing algorithms. IMU is mainly composed of low cost MEMS gyroscope and three axis accelerometer. MEMS gyroscope has good autonomy [3], low power consumption, good mechanical and electrical performance advantages of integration. However, the MEMS gyroscope has the characteristic of temperature drift; the measurement error will be accumulated with the accumulation of time, thus affecting the measurement accuracy. The accelerometer will be affected by the vibration of the robot, aliasing the additional amount of vibration interference. So a single sensor measurement is difficult to get accurate attitude angle.

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Section: The Experimental Resultsmentioning

confidence: 94%

Application of Multi-order Complementary Filtering Technique in Dynamic Attitude Measurement of Forestry Special Robots

Liu¹,

Ren²,

Deng³

2017

Proceedings of the 2017 International Conference on Electronic Industry and Automation (EIA 2017)

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“…Sensor management aims at managing and coordinating limited sensor and system resources to accomplish specific and dynamic mission objectives [3,4]. Algorithms and methods solving realistic sensor management problems are computationally very demanding, since realistic models of the environment, sensors and platforms are very complex due to the nonlinear and stochastic properties of the world.…”

Section: Background and Literature Surveymentioning

confidence: 99%

Road Target Search and Tracking with Gimballed Vision Sensor on an Unmanned Aerial Vehicle

et al. 2012

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This article considers a sensor management problem where a number of road bounded vehicles are monitored by an unmanned aerial vehicle (UAV) with a gimballed vision sensor. The problem is to keep track of all discovered targets and simultaneously search for new targets by controlling the pointing direction of the vision sensor and the motion of the UAV. A planner based on a state-machine is proposed with three different modes; target tracking, known target search, and new target search. A high-level decision maker chooses among these sub-tasks to obtain an overall situational awareness. A utility measure for evaluating the combined search and target tracking performance is also proposed. By using this measure it is possible to evaluate and compare the rewards of updating known targets versus searching for new targets in the same framework. The targets are assumed to be road bounded and the road network information is used both to improve the tracking and sensor management performance. The tracking and search are based on flexible target density representations provided by particle mixtures and deterministic grids.

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“…The ultimate goal of the SM is to optimize the overall performance of the multi-sensor system [42]. This goal is based on predefined system goals and priorities.…”

Section: Decisionmentioning

confidence: 99%

“…SM seeks to answer the following questions [43]: which sensor or sensors, which task, how to control the sensors, and when to start. The role and function of SM can be best understood if structured into different levels based on their functionality [42]:…”

Section: Decisionmentioning

confidence: 99%