ProbabilisticRobotics.md (2070B)
1 # Probabilistic Robotics 2 3 ## Links 4 5 ### First Semester 6 7 - [Localization](Localization.md) 8 - [Histogram Filters](HistogramFilters.md) 9 - Discrete, Multi-modal, Exponential TC, Approximate 10 - [Kalman Filters](KalmanFilters.md) 11 - Continuous, Uni-modal, Quadratic TC (sometimes), Approximate 12 - [Particle Filters](ParticleFilters.md) 13 - Continuous, Multi-modal, TC differs, Approximate 14 - [Bicycle Motion](BicycleMotion.md) 15 16 ### Second Semester 17 18 - Videos 233 - 268 19 - Search / Motion Planning 20 - Shortest Path 21 - BFS 22 - [A\*](AStar.md) - uses heuristic function 23 - Dynamic Programming 24 - Optimal distance from any location is sometimes useful 25 - Videos 280 - 312 26 - Smoothing 27 - Interpolate between turns to smooth across different steps 28 - This uses gradient descent along with $\alpha$ and $\beta$ which are hyperparams for smoothing 29 - PID Control 30 - Cross track error 31 - Lateral distance between reference trajectory and the vehicle 32 - We want to minimize cross track error 33 - This often overshoots though 34 - To achieve marginal stability we then use PD control 35 - PD Control 36 - When we are reducing error, we counter-steer to stop overshoot. 37 - Systematic Bias 38 - These are biases in our system that should be accounted for to stop oscillation 39 - Like tire alignment 40 - PID 41 - P = Proportional 42 - I = Integral (solves for bias term) 43 - D = Differential (solves oscillation without considering bias) 44 - These are the three parts of the equation for control 45 - Control gains 46 - These are the hyper-params for the PID 47 - Twiddle can solve this (coordinate descent) 48 - We change the hyperparams individually, grading each, and updating bumping factors 49 - Videos 323 - 363 50 - SLAM 51 - Simultaneous Localization and Mapping 52 - Localization is assuming we have a map