Course Notes from Programming for: UASs, UAVs / Autonomous Aerial Agents

90319 Update:

  • UAS (Unmanned Aerial Systems)
  • UAV (Unmanned Aerial Vehicle)

  • Introduction to Autonomous Flight
  • History
  • Morphologies
  • Components
  • Airframe
  • ESC (electronic speed controllers) motor speed control
  • Propellers
  • Batteries
  • Attitude Control
    • Balance of Net Forces – Hovering
    • Unequal Net Force – Directional Movement
  • Autopilot
  • Sensors – MEMS (micro electronic mechanical systems)
  • IMU Gyros (measure relative change in attitude) 3ea.
  • IMU accelerometers 3ea.
  • GPS
  • Other sensor devices
    • Cameras
    • Lazer Range Finders
    • Compasses
    • Barometers
  • Flight Computer – responsible for higher level functions
    • High Level Functions at roughly 5 hz. per second
      • Position Control Loop
        • Flight Computer
        • Using GPS
        • Calculates the Required Target Thrust Vector
        • Sends inputs to the Attitude Control Loop
    • Lower level functions at roughly 50 hz. per second
      • Attitude Control Loop
        • Autopilot
        • IMU
        • Motors

  • Search Planning
  • Planning Factors / 2 D Search Space
    • Includes Dynamic Vehicular Contraints
    • Search Space Discretization
      • Possible paths
      • Start location
      • Goal location
      • Actions (L,R,U,D,Angular)
      • Costs
        • Cost functions enable plan comparisons
    • Path representations
      • Grids Size Tradeoffs
      • Partial Plans as Part of the Planning Process
      • Generate a List of Visited Actions and Obstacles
      • Types of Searches
        • Breadth First Search
          • Always Expands Shortest Current Plan First
          • Guaranteed to Find Shortest Path First
          • Can be Computationally Expensive Because it must keep a number of partial plans in memory / storage while it is expanding all new plans
        • Depth-First Search
          • Expands Last Successful Action
          • Requires a Bit of Luck
          • You never know if this is possibly the shortest plan
          • Can Fail Completely
        • Neither of the above plans make use of a map
        • Uniform Cost Search
          • Expands the Plan with the Lowest Cost First
          • Guaranteed to Find the Lowest Cost Plan First
          • Types of Costs
            • Euclidean Distance = sqrt(sum(x^2 + y^2))
            • Manhatten Distance = sum of x & y distances remaining to get to the goal
          • Heuristics Admissible and Consistent
            • Admissibility = always needs to be an underestimate of the true cost
            • Ignoring the cost to go around obstacles ensures admissibility
          • G = cost function (i.e., sum of the actions taken so far)
          • H = heuristics (i.e., underestimate of the remaining cost to get from the last state of a partial plan to the goal)
          • F = G + H (estimate of total cost)
          • Consistent
            • Triangle inequality theorem where: H (x1 -> x3) <= H(x1 -> x2) + H(x2 -> x3)
        • A star or A*
          • expanding the partial plan that has the lowest total cost in terms of the sum of the actual costs of actions in the partial plan plus the heuristic from the last state in the partial plan is the A* algorithm
          • Guaranteed to find the lowest cost plan first
          • A* search doesn’t suffer the inefficiencies of Breadth First Search in terms of memory or storage of large numbers of partial plans during execution but is dependent on the quality of the chosen heuristic.

  • Grids to Graphs
    • Graph methods reduce memory overhead
    • Waypoint Extraction
      • Collinearity
        • p1 = (x1, y1, z1)
        • p2 = (x2, y2, z2)
        • p3 = (x3, y3, z3)
        • Area = det(matrix([p1,p2,p3])) = 0 = colinear
          • Area = x1(y2 – y3) + x2(y3 – y1) + x3(y1 -y2)
      • Discretization
      • Search from start to goal
      • Test for collinearity & remove elements or cells
      • Ray Tracing
      • Bresenham
      • Grids to Graphs
      • Graph Tradeoffs
      • Medial Axis
      • Voronoi Graphs
      • Graph Search
      • Dead bands

    • 3D Grids
    • Voxel maps
    • 2D Environment
      • 2D maps
      • Pixels = picture elements  2D
      • Planning Factors 3D Search Space
    • 3D Environment
      • 2.5D maps
      • Random Sampling
      • Probabilistic Roadmap
      • Local Planning
      • Receding Horizon
      • Replanning

  • Real World Planning
    • Constraints
    • Modeling Dynamics
    • Dubins Car
    • Steering
    • RRT
    • Adding Obstacles
    • Potential Field Planning

  • Voxels = 3D elements
  • Voxel-map
  • k-d-tree
  • k-nearest neighbors
  • Probabilistic road map
  • Receding horizon path planning
        • Planning as search
          • A-star search algorithm
          • Dubins-car
          • Steering
          • RRT (rapidly exploring random tree)
          • Potential field
        • Path pruning
          • Collinearity
          • Bresenham
          • Medial Axis
          • Voronoi diagrams
          • Graph search
            • BFS
            • DFS

      • Motion planning -p1
      • Vehicle dynamics
        • Vehicle dynamics
          • F = ma
          • thrust
          • torque – moments
      • 2D Vehicle Control
        • Control Architecture
          • P control (proportional control)
          • PD control (proportional, derivative control)
      • 3D Vehicle Control
        •  Control Architecture
          • PD cascaded control (proportional, derivative, nested control loops)
          • PID architecture
            • (proportional, derivative, integral -multi-rate nested control loops
        • Configuration space
          • Geodetic / World Frame
            • long, lat, alt.
          • NED / Body / Local Frame
            • north, east, down(pos)
          • Linear algebra
            • Integration
            • Derivatives
        • Linearization
        • Matrix math
          • Rgb (rotation matrices) global frame to body frame
        • Euler angles
          • V3F function
        • Quaternion transformations
      • State estimation

  • UAVs (unmanned aerial vehicles), UASs (unmanned aerial systems)
  • Geodetic Frame or World Frame
  • ECEF – Earth Centered / Earth Fixed
  • NED (North, East, Down)
    • Local or Body Frame used in Aeronautics
  • Euler Angles
  • Gimbal lock
  • Rotation Matrices
  • Quaternions
  • Motions as Transformations
  • Configuration Space