To connect the disparate pieces of code into a working whole, the robot needs some faculties for high level decision making. This might make you think of "AI" or machine learning, but our needs are closer to game AI: something simple, rules-based and easy to debug. After some research the dominant approaches to this task are HSM (hierarchical state machines) and behavior trees.

I thought behavior trees made more intuitive sense, so that's what I ended up using. Fortunately there is a pretty good looking implementation for ROS in python, created by the maker of the pirobot.

It comes out of the box with ROS-related blocks for performing actions on changes to a ROS topic as well as the basic building blocks of behavior trees like Selectors and Sequences. As I worked through the behaviors for the robot I had to implement a few new task blocks:

TwistTask - issues a constant twist (linear and angular velocity) to the robot for x seconds

PublishTask - publish on a ROS topic to trigger some action

DynamicActionTask - navigate to a target pose, same as SimpleActionTask, but takes the goal from the blackboard so it can be updated dynamically at runtime

As of now the behavior tree for the robot is quite simple, it just runs down a list of actions when a voice command is triggered. For more robustness there should be some recovery behaviors for example, if a navigation command fails.

code on github: https://github.com/Jack000/bunnybot/blob/master/launch/bunnybot.py