基于自適應卡爾曼濾波的側(cè)滑移動機器人運動模型估計

吳耀; 王田苗; 王曉剛; 劉淼

doi:10.11999/JEIT150289

基于自適應卡爾曼濾波的側(cè)滑移動機器人運動模型估計

doi: 10.11999/JEIT150289 cstr: 32379.14.JEIT150289

基金項目:

國家863計劃(2011AA040202)

計量
- 文章訪問數(shù): 1801
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- PDF下載量: 772
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2015-03-09
- 修回日期: 2015-09-09
- 刊出日期: 2015-12-19

Kinematics Model Prediction of Skid-steering Robot Using Adaptive Kalman Filter Estimation

Funds:

The National 863 Program of China (2011AA 040202)

摘要

摘要: 精確實時在線的運動模型對于側(cè)滑移動機器人的運動控制和軌跡規(guī)劃至關重要，相比于離線模型估計，該文在基于速度瞬心(ICRs)的側(cè)滑移動機器人運動學模型基礎上，采用擴展卡爾曼濾波(EKF)，在同一特定地形下在線準確得到ICRs的參數(shù)值；并針對不同的地形情況，采用k-近鄰法對地形進行分類，實時判別機器人當前運行的路面，采用自適應的卡爾曼濾波器(AKF)調(diào)整濾波器參數(shù)。仿真和實驗對比表明，該方法在同一地形和變化地形下均能快速估計出側(cè)滑移動機器人的運動學模型，收斂時間均為3 s以內(nèi)，可以滿足實際使用的需要。
- 移動機器人 /
- 側(cè)滑移動 /
- 自適應卡爾曼濾波 /
- 速度瞬心 /
- k-近鄰法
Abstract: Exact and real-time kinematics model plays a very important role in the mobile robot motion control and path planning. Compared to the off-line model estimation, based on an Instantaneous Centers of Rotation (ICRs) based kinematic model of skid-steering, an Extend Kalman Filter (EKF) method is used to estimate ICRs values on specific terrain on line. Terrains are identified by introducing k-Nearest Neighbors (kNN) algorithm when the robot moves on different terrains. Based on terrain classification, an Adaptive Kalman Filter (AKF) is used to adjust the filter parameters. The simulation and experiment results show that this method can converge very fast and estimate the ICRs value accurately with 3 seconds.
- Mobile robot /
- Skid-steering /
- Adaptive Kalman Filter (AKF) /
- Instantaneous Centers of Rotation (ICRs) /
- k-Nearest Neighbors (kNN)

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參考文獻(29)

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