In order to move out of the laboratory, robots must must be capable of safely navigating through crowds of people, robots, and everyday objects, all of which are dynamically moving and cannot be pre-mapped. This project aims to develop a new method for navigation that will allow a robot to reactively modify a nominal path in order to avoid dynamic unmapped objects. The system is made up of four main components: Localization, Object Recognition, Multi-Target Tracker, and CNN-based Controller. The robot’s onboard sensors feed data into each of these components, the details of which can be found below. The robot will begin the task with a nominal path through the environment that avoids collisions with shelves and other static infrastructure and which comes from applying a standard path planning algorithm in a map of the store. The robot will modify this nominal path using its sensor data and the output of the multi-target tracker to avoid collisions with unmapped obstacles to the map (e.g., customers and temporary product displays).