notes

commit 452549eb675e98eab51ee3b6a6aadd5eea811a0d
parent 76d92d2413ef7efee9497c242820124a49ce6e9d
Author: Andrew Laack <andrew@laack.co>
Date:   Sat, 18 Oct 2025 22:28:06 -0500

Took final notes + took midterm exam

Diffstat:
M
docs/BicycleMotion.md
 | 
40
+++++++++++++++++++++++++++++++++++++++-
M
docs/ParticleFilters.md
 | 
12
+++++++++++-
M
docs/ProbabilisticRobotics.md
 | 
6
+++---

3 files changed, 53 insertions(+), 5 deletions(-)
diff --git a/docs/BicycleMotion.md b/docs/BicycleMotion.md
@@ -4,4 +4,42 @@
 
 **Chapter:** 5
 
-**Definition:**
+**Definition:** Bicycle motion is a motion system that accounts for two wheels along with the rotation of the front wheel. 
+
+Given that car wheels are generally not independent wrt their lateral counterparts, this model can be used to model the motion of cars.
+
+## The Model
+
+### Controls
+
+There are two controls, the steering angle ($\alpha$), and the forward movement ($d$).
+
+#### Steering Angle
+
+Positive steering angle is angling the wheels to the left.
+
+Negative steering angle is angling the wheels to the right.
+
+If we consider a top down view of a car where the car is facing to our right, we the steering angle matches our notion of radians on a unit circle. 
+
+#### Forward Movement
+
+The forward movement describes the distance the robot will travel in a given step.
+
+### Pose
+
+The pose of a robot is described by the (x,y) coordinates and theta. 
+
+## Position Calculation
+
+### Basic Movement
+
+This is simple trig.
+
+$y_{dist} = sin(\theta) * d$
+$x_{dist} = cos(\theta) * d$
+
+$y_{new} = y + y_{dist}$
+$x_{new} = x + x_{dist}$
+
+This assumes no steering angle.
diff --git a/docs/ParticleFilters.md b/docs/ParticleFilters.md
@@ -4,4 +4,14 @@
 
 **Chapter:** 4.2
 
-**Definition:**
+**Definition:** Particle filters are an approach to localization where many particles are placed in an environment where each particle has the same sensing abilities as the robot. At each sense step we then compute importance weights for each particle and sample on the basis of this value.
+
+## Steps
+
+1. Initialize random, valid particles
+2. Main Loop
+    1. Move each particle based on the robot movement command
+    2. Compute the importance weight for each particle
+        1. This is proportional to how likely the particle measurement is given the robot's measurement. 
+    3. Sample particles proportional to their importance weight
+        1. This involves sampling particles with replacement to survive to the next step. This ensures we have a consistent number of particles from turn to turn, and that they are decent representations of the robot's true state.
diff --git a/docs/ProbabilisticRobotics.md b/docs/ProbabilisticRobotics.md
@@ -6,11 +6,11 @@
 
 - [Localization](Localization.md)
 - [Histogram Filters](HistogramFilters.md)
-    - Discrete, Multi-modal
+    - Discrete, Multi-modal, Exponential TC, Approximate
 - [Kalman Filters](KalmanFilters.md)
-    - Continuous, Uni-modal
+    - Continuous, Uni-modal, Quadratic TC (sometimes), Approximate
 - [Particle Filters](ParticleFilters.md)
-    - Continuous, Multi-modal
+    - Continuous, Multi-modal, TC differs, Approximate
 - [Bicycle Motion](BicycleMotion.md)
 
 ### Second Semester