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首页> 外文会议>Mechatronics, 2009. ICM 2009 >A dead reckoning sensor system and a tracking algorithm for mobile robots
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A dead reckoning sensor system and a tracking algorithm for mobile robots

机译：移动机器人的航位推算传感器系统和跟踪算法

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We have developed a dead reckoning sensor system and a tracking algorithm for mobile robots to estimate the path when a mobile robot explores an unknown, enclosed region where GPS access or landmarks are unavailable. A dead reckoning sensor system consists of a low-cost MEMS IMU and a navigation sensor (used in laser mice), which provide complementary functions. The IMU has benefits such as compact size, a self-contained system, and an extremely low failure rate but has a bias drift problem, which can accumulate substantial error over time. A navigation sensor measures the motion of a mobile robot directly without the slip error in the case of a wheel-type odometer, but it often fails to read a surface. A tracking algorithm consists of an extended Kalman filter (EKF) to fuse data from the IMU and the navigation sensor and a least-squares method to estimate acceleration bias in the EKF. We obtained experimental data by driving a radio-controlled car equipped with the sensor system in a 3D pipeline and compared the path estimated by the tracking algorithm with the path of the pipeline. The tracking algorithm combined data from the IMU and the navigation sensor and correctly estimated the path of the radio-controlled car. Our study can be applied to estimate position or path of mobile robots without external aids such as GPS, landmarks, and beacons.
机译：我们已经开发了航位推算传感器系统和用于移动机器人的跟踪算法，以在移动机器人探索GPS通道或地标不可用的未知封闭区域时估算路径。航位推测传感器系统由低成本MEMS IMU和导航传感器（用于激光鼠标）组成，它们提供互补的功能。 IMU具有诸如紧凑的尺寸，独立的系统以及极低的故障率等优点，但存在偏差漂移问题，随着时间的流逝会累积大量误差。在轮式里程表的情况下，导航传感器可直接测量移动机器人的运动而不会产生滑动误差，但通常无法读取表面。跟踪算法由扩展的卡尔曼滤波器（EKF）组成，以融合来自IMU和导航传感器的数据，以及最小二乘法估计EKF中的加速度偏差。我们通过在3D管线中驾驶装有传感器系统的无线电遥控车来获得实验数据，并将跟踪算法估算的路径与管线的路径进行比较。跟踪算法结合了来自IMU和导航传感器的数据，并正确估计了无线电控制汽车的路径。我们的研究可用于估计移动机器人的位置或路径，而无需使用外部辅助设备（例如GPS，地标和信标）。

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