
# How to solve for depth perception
# move measured amount
# focal length of camera
# object/feature track
# calculate depth of object(s)

# Building profiles
# of objects encountered
# remember individuals (dog, human, cat, etc?)
# breed of dog
# give a unique name
# api interaction allows settings based on individuals

# Give it measurement (learning) ability
# Place a mark on the floor a measured distance from the wall
# Place two marks on the wall, one aligned with the floor mark
# Use measure driving
# Don't need to measure when using localization

# Navigate apartment using localization
# Plan moves to reach another room**
# Distinguish between rooms
# Find center of driving space with penalty for off planned path

# Pups from Prometheus
# 3D map and measure apartment
# System for map
# detect walls and objects, remember sequences of objects
# remember relative positions of object

# Hirearchy of planning algorithms
# Planning small moves for navigation within a room
# Planning larger moves for search

# Reinforcement learning: ensure dog-stress-free operation
# override plans when it knows a better move
# introduce randomness to actions outside of plans
# pass it features about it's environment
# reward fewer moves
# limit stress of dog

# Simulated environment
# teach agent to learn in simulation
# limiting dog stress is main objective
# monitoring dog is second




















