This paper describes the design and implementation of a deployable, multi-modal system executed using Deep Learning with Computer Vision. The system was majorly created using Transfer Learning Algorithms, GoogleNet, and MATLAB, which were then made portable and deployable using a Raspberry Pi with Pi Cam. The neural networks were trained on a dataset consisting of several images and is highly accurate. This system consists of Fire Detection, Face and Intruder Detection, Object Identification, and Animal Identification, and follow me features which are meant to be deployed on a mobile robot such as an autonomous rover, drone, glider, and aquatic robot

This paper depicts the design and implementation of a multi-level, multi-faceted software and hardware architecture that we have used to create a swarm of intelligent robots that can communicate basic instructions to each other. We have several layers in the architecture that can control a particular part of the system, with humans as the major drivers for the whole system. The individual rovers can navigate autonomously and avoid obstacles in their way. Our system utilizes several microcontrollers and companion computers such as Raspberry Pi, Pixhawk Flight Controller, and Arduino Mega Microcontroller. We have also done the circuit design and PCB implementation for the rovers. We have conducted this project as a proof of concept for the feasibility of Surveillance Robots. A swarm of intelligent robots would undoubtedly be helpful in situations such as disaster management and search and rescue missions.