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未知環境中機器人巡航問題之研究

未知環境中機器人巡航問題之研究. 王銀添 副教授 機器人實驗室 淡江大學機械與機電工程學系. 目錄. 機器人巡航 感測器輔助機器人執行巡航任務 可能遭遇的問題與解決方案 不確定性 (uncertainty) 現象 機率式狀態估測方法 貝氏規則、 Kalman Filter 、 Particle filter 同時定位、建圖、物件追蹤之高維度非線性系統 同時定位、建圖、物件追蹤實測範例 結論與未來之研究議題. 機器人巡航. 在未知的環境中巡航時，機器人想知道 自己在哪裡 ? 環境是什樣的長相 ? 是否有移動的障礙物 ?

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未知環境中機器人巡航問題之研究

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  1. 未知環境中機器人巡航問題之研究 王銀添 副教授 機器人實驗室 淡江大學機械與機電工程學系 Applied Electronics Technology, NTNU

  2. 目錄 • 機器人巡航 • 感測器輔助機器人執行巡航任務 • 可能遭遇的問題與解決方案 • 不確定性(uncertainty)現象 • 機率式狀態估測方法 • 貝氏規則、Kalman Filter、Particle filter • 同時定位、建圖、物件追蹤之高維度非線性系統 • 同時定位、建圖、物件追蹤實測範例 • 結論與未來之研究議題 Applied Electronics Technology, NTNU

  3. 機器人巡航 • 在未知的環境中巡航時，機器人想知道 • 自己在哪裡? • 環境是什樣的長相? • 是否有移動的障礙物? Example: Dead reckoning(deduced reckoning) Applied Electronics Technology, NTNU

  4. Calibration of Errors for Robot with Odometry (Borenstein [1992]) • The unidirectional square path experiment Applied Electronics Technology, NTNU

  5. Calibration of Errors for Robot with Odometry (Borenstein [1992]) • The bi-directional square path experiment Applied Electronics Technology, NTNU

  6. 感測器輔助巡航 • 在未知的環境中巡航時，機器人必須依賴自身的移動與(外部、多個)感測器對環境特徵的感測，執行以下任務： • 自我定位(self-localization)任務 • 環境地圖建構(mapping)任務 • 移動物體偵測與追蹤(detection and tracking of moving objects)任務 Applied Electronics Technology, NTNU

  7. 機器人感測(Robot Perception)系統 Sensor Classification • Proprioceptive sensors • measure values internally to the system (robot) • e.g. motor speed, wheel load, heading of the robot, battery status • Exteroceptive sensors • information from the robots environment • distances to objects, intensity of the ambient light, unique features • Passive sensors • energy coming for the environment • e.g. temperature probe, microphones, and CCD or CMOS camera. • Active sensors • emit their proper energy and measure the environmental reaction • better performance, but some influence on environment • e.g. wheel quadrature encoders, ultrasonic sensors, and laser rangefinders. Applied Electronics Technology, NTNU

  8. Sensor Classification (1) [Siegwartand Nourbakhsh 2004] Applied Electronics Technology, NTNU

  9. Sensor Classification (2) Applied Electronics Technology, NTNU

  10. Laser Range Finder (LRF) Applied Electronics Technology, NTNU

  11. Mapping Using LRF Applied Electronics Technology, NTNU

  12. Inertial Measurement Unit (IMU) • A unit has 3-axis gyroscope (pitch, roll, yaw ) and 3-axis accelerometer. • Localization using IMU • A unit with a tri-axis accelerometer, tri-axis magnetometer and a tri-axis gyro Applied Electronics Technology, NTNU

  13. Vision Sensors Image projection model (3D to 2D) Applied Electronics Technology, NTNU

  14. Web Camera Based 3D Scanner Applied Electronics Technology, NTNU

  15. Light Detection and Ranging (LiDAR) Applied Electronics Technology, NTNU

  16. Airborne LiDAR Applied Electronics Technology, NTNU

  17. Microsoft Kinect • 3D depth image and RGB color image in 30fps. • Low-cost. (NT$4,550 tax. included) • Software development kit provided by Microsoft. Applied Electronics Technology, NTNU

  18. Skanect – Real-time Kinect-based 3D Scanner manctl.com Applied Electronics Technology, NTNU

  19. Mapping Using Kinect Applied Electronics Technology, NTNU

  20. 可能遭遇的問題與解決方案 有幾個問題會造成巡航的任務相當棘手，包括 感測器的侷限性 移動與量測都具有不確定性質(uncertainty) 定位、建圖、追蹤物體 系統變成高維度與非線性 本研究針對以上問題進行探討，考慮的議題包括 感測器的選用、移動偵測 不確定性現象的描述 同時求解定位、建圖、追蹤物體等問題 並且以機率理論解決機器人在未知環境中巡航問題。 Applied Electronics Technology, NTNU

  21. 不確定性(Uncertainty)現象 • The structurederrors • The locations of the CW and CCW clusters. • The randomerrors • The random distribution of errors in the cluster. • Uncertainty in motion. Applied Electronics Technology, NTNU Borenstein [1992]

  22. Uncertainty in Robot Motion Applied Electronics Technology, NTNU

  23. Uncertainty in Robotics • 不確定性現象的描述 • 以參數函數描述不確定性，例如高斯常態分佈 • 以非參數函數描述不確定性，例如蒙地卡羅模擬 • 以機率理論求解具不確定性的機器人巡航問題 N(x;m,s2) m : mean value s : deviation f(x) is the probability density function (pdf) Applied Electronics Technology, NTNU

  24. 系統的狀態與量測 • Measurement sequence • z is the measurement of the system; • g is nonlinear measurement function; • v is the uncertainty of the measurement. Projection model Uncertainty Model-based state transition Uncertainty • State sequence x is the state of the system; u is the input; f is a nonlinear function of the state; w is the uncertainty of the state. Applied Electronics Technology, NTNU

  25. Basic Probability Theory • Joint probability： P(X=x and Y=y) = P(x,y) • Conditional probability: P(x|y)is the probability of x, given y, • Theorem of total probability: If yiconstitute a partition of the sample space, then for x in the same space • Bayes rule: if x is a quantity that we would like to infer from y, Conditioning Bayes rule on z. Applied Electronics Technology, NTNU

  26. Probabilistic Generative Laws • The emergence ofstate xk might be conditioned on all past states, measurements, and controls, • If the state x is complete then xk-1 is a sufficient statistic of all previous controls and measurements, u1:k-1 and z1:k-1. Only the control uk matters if we know the state xk-1, called state transition probability. • If xk is complete, the measurement probability is also generated by The state xk is sufficient to predict the measurement zk. Applied Electronics Technology, NTNU

  27. Dynamic Bayes Network (DBN) • The temporal generative model is known as hidden Markov model (HMM) or dynamic Bayes network (DBN). • The state at time k is stochastically dependent on the state at time k-1 and the control uk. • The measurement zk depends stochastically on the state at time k. • The dynamic Bayes network that characterizes the evolution of controls, states, and measurements. Applied Electronics Technology, NTNU

  28. State Estimation Using Bayes’ Rule • From a Bayesian perspective, the state estimation is to recursively calculate some degree of belief in the state xk at time k, given the data z1:k and u1:k, • Thus, the probability density function (pdf) of state is constructed via Bayes’ rule • The initial pdf p(x0|z0)=p(x0) of the state vector, which is also known as the prior, is available. • z0 is the set of no measurements. • Then, in principle, the pdf p(xk|z1:k,u1:k) may be obtained, recursively, in two stages: prediction and update. Applied Electronics Technology, NTNU

  29. State Estimation Using Bayes’ Rule Predict and update the state p(xk|z1:k,u1:k) recursively: • The prediction stage involves using the system model to obtain the prior pdf of the state at time k, Suppose that the required pdf p(xk-1|z1:k-1,u1:k) at time k-1 is available. • At time step k, a measurement zk is used to update the prior (update stage) via Bayes’ rule where the normalizing constant Applied Electronics Technology, NTNU

  30. State Estimation Using Particle Filter • 引用貝氏規則之遞迴預測與更新系統狀態； • 每個粒子代表一個解，在取樣空間中隨機規劃數量L個粒子進行求解。規劃的粒子數量越多越趨近最佳解。第l個粒子的pdf表示為 每個粒子l遞迴地依據感測訊息更新取樣空間中的狀態 之權重值，用以顯示狀態在該數值區段的機率。 Applied Electronics Technology, NTNU

  31. Procedure of Particle Filter 遞迴地預測與更新系統狀態 • 所有粒子l所存的機器人狀態依據運動模型進行移動，此為粒子的機器人狀態之預測； • 擷取新的視覺感測訊息，並且透過感測模型zk重新分配各粒子之權重值； • 必要時進行重新取樣(resampling)； • 正規化(normalizing)權重值，以及更新機器人的狀態。 Applied Electronics Technology, NTNU

  32. Particle Filter for Robot Localization Applied Electronics Technology, NTNU

  33. KF-Based State Estimation • Kalman filter (KF)estimator • Adapt the concept of recursive prediction and update estimate process. • Prediction: • (Linear prediction of statesand measurements) • Update: • (Linear update equation for system states) • Example: KF-based SLAM • State vector of robot (camera) • State vector of static objects • State vector of moving objects • Measurement models Ojk=[ojksjk]T Applied Electronics Technology, NTNU

  34. Visual Sensors for SLAM • Camera carried by robot • Free-moving camera • The camera is presumed to move at constant velocity (CV); • The acceleration is caused by an impulse noise from the external force. • Velocity noise: Monocular vision Binocular vision Applied Electronics Technology, NTNU

  35. 淡江機電系機器人實驗室 • 機器人視覺式同時定位、建圖、與移動物體追蹤(visual simultaneous localization, mapping, and moving-object tracking) SLAMMOT Applied Electronics Technology, NTNU SLAM

  36. Monocular SLAM Applied Electronics Technology, NTNU

  37. Monocular SLAM Applied Electronics Technology, NTNU

  38. Monocular People Detection and Tracking Applied Electronics Technology, NTNU

  39. Binocular SLAM Applied Electronics Technology, NTNU

  40. Binocular SLAM Applied Electronics Technology, NTNU

  41. Binocular SLAM Applied Electronics Technology, NTNU

  42. Differential-drive Mobile Robot [2010] Binocular Vision PC-based Controller Laser Range Finder Sonar Wheel Encoder Mobile Robot Applied Electronics Technology, NTNU

  43. Visual SLAM of Mobile Robots Applied Electronics Technology, NTNU

  44. Visual SLAM of Mobile Robots Applied Electronics Technology, NTNU

  45. Visual SLAM of Mobile Robots Applied Electronics Technology, NTNU

  46. 理論上，同時定位、建圖、與移動物體追蹤的問題已經有解理論上，同時定位、建圖、與移動物體追蹤的問題已經有解 實現技術方面，仍有挑戰性： 辨識移動物體 使用移動感測器偵測與追蹤移動物體 實際應用時，依需求選擇完整求解或簡化求解 解答的一致性(consistency) 計算複雜性(computational complexity) 與路徑規劃、運動控制器的結合 新感測器的發展與應用 新的應用領域 結論與未來研究議題 Applied Electronics Technology, NTNU

  47. Visual SLAM of Robot Vacuum Cleaner (Samsung Hauzen RE70V) Applied Electronics Technology, NTNU

  48. Autonomous Quadrotor Mapping, Localization and Trajectory Following Using LiDARUniversity of Pennsylvania Applied Electronics Technology, NTNU

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