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C.1 Task 1 – Motion Estimation Motion estimation requires keeping track of robot pose based exclusively on PositionSensors and IMU readings. Use the world from Figure 2(a). Implement two robot controllers: (1) “lab3_task1_basic.py”, without noise. (2) “lab3_task1_noise.py”, with noise. (Extra Credit)

C.2 Task 2 – Measurement Estimation to Unique Landmarks Measurement estimation will be based on robot sensors and camera to recognize and measure distance to the colored cylinders. Use the world from Figure 2(b). You are required to use Triangulation or Trilateration methods to determine robot pose. Note that trilateration methods use relative distance to landmarks, while triangulation methods use both distances and angles relative to landmarks. Implement two robot controllers: (1) “lab3_task2_basic.py”, without noise. (2) “lab3_task2_noise.py”, with noise. (Extra Credit) C.3 Task 3 –Localization with Internal Walls Localization will be based on the particle filter using robot sensor readings to estimate the robot pose in the given world. Use the world from Figure 3(a). The maze will be given in advance to the robot and may be different from the one shown in Figure 3(a). For the noise version, apply the Particle Filter algorithm using both the motion and measurement models you choose. Use at least 80 particles, initially distributed evenly throughout the 16 grid cells. The robot should be able to estimate its current position based on highest accumulation of particles at a given cell. Implement two robot controllers: (1) “lab3_task3_basic.py”, without noise. (2) “lab3_task3_noise.py”, with noise. (Extra Credit) C.4 Task 4 –Localization with Yellow Landmarks and No External Walls (Extra Credit) Localization will be based on the particle filter using robot sensor readings to estimate the robot pose in the given world. Use the world from Figure 3(b). You may use any localization method you want. Note that IMU will be useful in discriminating between yellow landmarks for robot starting from an unknown location. Implement two robot controllers: (1) “lab3_task4_basic.py”, without noise. (2) “lab3_task4_noise.py”, with noise. D. Task Evaluation Task evaluation involves: (1) programs, each task as a different controller, and (2) report, including links to a video showing each different task. Points will be taken off for any robot that crashes into walls or gets stuck in any way. Note that the TA will be testing the different tasks under slightly different mazes to evaluate your solutions. D.1 Task Presentation (90 points) The following section shows the rubric for the tasks shown in the video: • Task 1 (25 points) (Extra Credit “noise” task: 5 points max) o Prints specified information at each cell ( 7.5 points) o Keeps track of X and Y coordinates within a range of 3 inches ( 10 points) o Keeps track of robot’s current cell ( 2.5 points) o Navigates to all cells in grid ( 7.5 points) o Prints sensor information at each timestep (-5 points) o Robot hits walls (-5 points) o Noise solution can keep track of position accurately while considering noise in the IMU and Position Sensors ( 5 points) (Extra Credit portion) • Task 2 (35 points) (Extra Credit “noise” task: 5 points max) o Prints specified information at each cell ( 5 points) o Utilizes Triangulation or Trilateration to estimate X and Y coordinates within a range of 3 inches ( 20 points) o Keeps track of robot’s current cell ( 5 points

Navigates to all cells in grid from known starting location ( 2.5 points) o Navigates to all cells in grid from unknown starting location ( 2.5 points) o Prints sensor information at each timestep (-5 points) o Robot hits walls (-5 points) o Noise solution can keep track of position accurately while considering noise in the IMU and Distance Sensors ( 5 points) (Extra Credit portion) • Task 3 (30 points) (Extra Credit “noise” task: 10 points max) o Prints specified information at each cell ( 5 points) o Navigates to all grid cells from unknown start location ( 10 points) o Localizes based on cell wall layouts ( 10 points) o Keeps track of robot current location ( 5 points) o Prints sensor information at each timestep (-5 points) o Robot hits walls (-5 points) o Utilizes measurement estimation to estimate the probability the robot is in each cell at each step ( 2.5 points) (Extra Credit portion) o Applies Measurement Model (Using correct values for noise), Measurement Estimation (including normalization), Importance Factor (including normalization), and resampling. ( 2.5 points) (Extra Credit portion) o Robot can estimate orientation and current cell after all particles accumulate in one cell after several movements ( 5 points) (Extra Credit portion) • Task 4 (25 points) (Extra Credit: 25 points max) o Navigates all grid cells from known start location without “noise” ( 5 points) o Navigates all grid cells from unknown start location without “noise” ( 5 points) o Navigates all grid cells from known start location with “noise” ( 5 points) o Navigates all grid cells from unknown start location with “noise” ( 10 points) o Prints sensor information at each timestep (-5 points) o Robot leaves tiled world (-5 points) o Correct noise solution in keeping track of X and Y coordinates (-10 points) NOTE: Do not make calls to print statements inside the “while” loop or functions called from the “while” loop more than once per cell (do not print at each time step). Otherwise, you will be penalized up to 15% of your lab grade. You may have print statements outside the “while” loop

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