Advanced Control for Mobile Robots

• 1.1 What a model is; inputs, outputs and states
• 1.2 Mechanical, electrical and electromechanical systems
• 1.3 The DC motor model derived
• 1.4 Linearisation about an operating point
• 1.5 Simulating models in Python
• 1.6 Case study: a model of a robot's wheel drive

Take the Chapter 1 assessment

• 2.1 The Laplace transform and its key properties
• 2.2 Transfer functions, poles and zeros
• 2.3 Block-diagram algebra
• 2.4 First- and second-order responses
• 2.5 Computing with transfer functions
• 2.6 Case study: a position servo for a camera mount

Take the Chapter 2 assessment

• 3.1 State equations and their relation to transfer functions
• 3.2 Controllability and observability
• 3.3 Case study: the inverted pendulum on a cart

Take the Chapter 3 assessment

• 4.1 Step-response identification
• 4.2 Least-squares parameter estimation
• 4.3 Instantaneous gradient descent (iGD) identification
• 4.4 Validating a model against data
• 4.5 Case study: identifying a robot wheel drive from encoder data

Take the Chapter 4 assessment

• 5.1 BIBO stability and the s-plane
• 5.2 The Routh-Hurwitz criterion
• 5.3 Root locus
• 5.4 Frequency response
• 5.5 Case study: steering a line-following robot with a camera

Take the Chapter 5 assessment

• 6.1 PID design in the s-domain
• 6.2 Ziegler-Nichols and Cohen-Coon tuning
• 6.3 Tuning by simulation and by experiment
• 6.4 Cascade control
• 6.5 Case study: speed and position control of a wheeled robot

Take the Chapter 6 assessment

• 7.1 Sampling and the z-transform
• 7.2 Discretisation
• 7.3 Stability in the z-plane
• 7.4 Digital PID implementation
• 7.5 Case study: how slowly can the camera mount be sampled?

Take the Chapter 7 assessment

• 8.1 Pole placement
• 8.2 The linear quadratic regulator
• 8.3 Worked example: balancing an inverted pendulum

Take the Chapter 8 assessment

• 9.1 Noise, uncertainty and Bayesian estimation
• 9.2 Observers
• 9.3 The Kalman filter derived
• 9.4 Extended Kalman filter
• 9.5 Case study: fusing odometry, an IMU and GPS

Take the Chapter 9 assessment

• 10.1 Receding-horizon optimisation
• 10.2 Constraints and the quadratic program
• 10.3 MPC for trajectory tracking
• 10.4 Case study: constrained balancing of the cart-pole

Take the Chapter 10 assessment

• 11.1 Fuzzy logic control
• 11.2 Neural-network controllers
• 11.3 Reinforcement learning for control
• 11.4 Adaptive control and online identification
• 11.5 Machine learning and iGD for a self-balancing robot
• 11.6 Case study: three intelligent controllers on test

Take the Chapter 11 assessment

• 12.1 Single-board computers; GPIO layout
• 12.2 Headless setup and remote access
• 12.3 Python GPIO control: LEDs, buttons and servos
• 12.4 Raspberry Pi with microcontrollers
• 12.5 Case study: a framed link between a Raspberry Pi and an ESP32

Take the Chapter 12 assessment

• 13.1 ROS 2 architecture: nodes, topics, services, actions and parameters
• 13.2 Workspaces, packages and colcon
• 13.3 Writing publishers, subscribers, services and action servers in Python
• 13.4 Launch files, TF2 and coordinate frames
• 13.5 Visualisation and simulation: RViz and Turtlesim
• 13.6 Coordinating controllers without ROS: serial and sockets
• 13.7 Case study: testing a ROS 2 robot's algorithms without ROS

Take the Chapter 13 assessment

• 14.1 Wheel encoders and odometry
• 14.2 LIDAR: principles and point clouds
• 14.3 IMUs and cameras
• 14.4 Occupancy grids
• 14.5 Case study: odometry and a grid map for a robot in a room

Take the Chapter 14 assessment

• 15.1 Differential-drive kinematics
• 15.2 Ackermann steering
• 15.3 Holonomic and non-holonomic constraints
• 15.4 Case study: motions of three robot types

Take the Chapter 15 assessment

• 16.1 Configuration space
• 16.2 Graph search on occupancy grids
• 16.3 Sampling-based planning: PRM and RRT
• 16.4 Trajectory smoothing and following (pure pursuit)
• 16.5 Case study: planning across the room

Take the Chapter 16 assessment

• 17.1 Localisation: Monte Carlo localisation
• 17.2 The SLAM problem
• 17.3 SLAM in ROS 2 and the Nav2 stack
• 17.4 Case study: global localisation and a loop closure

Take the Chapter 17 assessment

• 18.1 What a digital twin is
• 18.2 How a physics simulator advances time
• 18.3 Gazebo: worlds, models and plugins
• 18.4 Unity for robotics simulation
• 18.5 Browser-based twins with JavaScript
• 18.6 Synchronising a physical robot with its twin
• 18.7 Case study: keeping a wheel's twin in step

Take the Chapter 18 assessment

• 19.1 UAV types: multirotor, fixed-wing and single-rotor
• 19.2 Components: frame, motors, ESCs, propellers and batteries
• 19.3 Flight controllers and their sensors
• 19.4 Transmitters, receivers and protocols
• 19.5 Regulations: NCAA rules for drones in Nigeria
• 19.6 Case study: sizing a survey quadrotor

Take the Chapter 19 assessment

• 20.1 Rigid-body dynamics and Euler angles
• 20.2 Thrust, torque and the motor mixing matrix
• 20.3 Attitude and position control loops
• 20.4 PX4 and ArduPilot
• 20.5 Case study: a cascaded controller on a nonlinear quadrotor

Take the Chapter 20 assessment

• 21.1 Rotation matrices and homogeneous transforms
• 21.2 Denavit-Hartenberg parameters
• 21.3 Forward kinematics of a 3-link arm
• 21.4 Inverse kinematics: geometric and numerical solutions
• 21.5 The Jacobian and singularities
• 21.6 Case study: kinematics of a small articulated arm

Take the Chapter 21 assessment