sphero-monash

This repository contains the labs and codebase for working with Sphero robots at Monash. The code is built on a sphero-unsw interface.

Throughout this unit, you will modify and extend this codebase by adding functionality across a sequence of labs. By the end of the unit, you will have built enough capability to complete the final project.

Each lab has two components:

1. Automarked individual assessment
2. Team demo on a real robot

Your mark is a combination of both components.

The final project is submitted in teams and also consists of both a simulated demo and a real robot demo. Time on the real robots is intentionally limited — the goal is for you to conquer the sim-to-real gap.

Robot Interface

Both the real robot and the provided simulator implement a gymnasium-like interface.

Action Space

The robot action is defined as:

action = [speed, heading_angle]

Index	Variable	Description
0	speed	Desired robot speed
1	heading_angle	Desired heading angle

Observation Space

The robot observation is defined as:

observation = [
    x_meas,
    y_meas,
    heading_meas,
    speed_meas,
    collision_flag,
]

Index	Variable	Description
0	x_meas	Measured x-position
1	y_meas	Measured y-position
2	heading_meas	Measured heading angle
3	speed_meas	Measured speed
4	collision_flag	Collision indicator

Goals

The environment can be configured with a 2d positional goal and goal tolerance:

• env.goal_pos
• env.goal_tolerance

Teleoperation example

An example using the simulator and robot is available in: examples/teleop.py

Lab 1 — PID Control, Calibration, and Simulation

In Lab 1, you will design PID controllers to drive your robot to a given pose set-point.

A simulator environment is provided so that you can develop and test your controller in simulation first. You will also need to override the simulator dynamics model so that it matches your real robot’s behaviour as closely as possible.

This calibration is very important — it will make or break the performance of your robot in all remaining labs.

Click here for Lab 1 instructions.

Lab 2 — EKF Localisation

In Lab 2, you will use a motion model of the robot together with contact measurements to improve the robot’s odometry estimate.

For the lab demo, you will move through a sequence of points and report on your estimation accuracy.

Again, calibration between the simulator, model, and real robot is key to performance. The more closely the real robot behaves like the simulator, the more likely your solution is to work in the real demo.

Click here for Lab 2 instructions.

Lab 3 — A* Path Planning

In Lab 3, you will discretise the robot’s operating space and investigate A* motion planning in a known environment.

Performance in this task will depend heavily on how well your Lab 2 localisation system worked.

Click here for Lab 3 instructions.

Lab 4 — Navigation in a Known Environment

In Lab 4, you will implement a learning-based method to find a sequence of moves that helps your robot navigate around a race track.

Click here for Lab 4 instructions.

Final Demo

For the final demo, you will program your robot to race another robot in an unknown environment, without knowing the environment layout beforehand.

You may consider training a reinforcement learning approach, or chaining together planning, mapping, and control modules.

Click here for final demo instructions.