Low-cost thermal explorer robot using a hybrid neural networks and intelligent bug algorithm model

Autonomous navigation requires an artificial agent able to independently move adapted to the environment. The robot sensors analyse the surrounding environment and learn from successful exploration experiences, to plot the best routes and avoid obstacles. This paper proposes a new algorithm for navigation being an adaptation of the Intelligent Bug Algorithm (IBA) combined with artificial neural networks. In addition, this approach also aims to reduce costs using low-cost sensors and a proposed thermal measurement system composed of a matrix infrared sensor superposed with a regular camera. The experimental results show that the novel algorithm is efficient, the prototype avoids collisions and manages to optimise the route, and thermal camera demonstrates accuracy in measuring temperatures and identifying different thermal zones. Moreover, the robot cost reduction and simple operation characteristics make possible its use in destructive missions in inhospitable terrain for humans, facilitating its implementation for research and testing.