Automatic Object Searching and Behavior Learning for Mobile Robots in Unstructured Environment by Deep Belief Networks

Abstract

Automatic object searching is one of the essential skills for domestic robots to operate in unstructured human environments. It involves concatenation of several capabilities, including object identification, obstacle avoidance, path planning, and navigation. In this paper, we propose an automatic object searching framework for a mobile robot equipped with a single RGB-D camera. The obstacle avoidance is achieved by a behavior learning algorithm based on deep belief networks. The target object is recognized using scale-invariant feature transform descriptors and the relative position between the target and mobile robot is estimated from the RGB-D data. Subsequently, the mobile robot makes a path planning to the target location using an improved bug-based algorithm. The framework is tested in indoor environments and requires the robot to perform obstacle avoidance and automatically search and approach the target object. The results indicate that the system is collision free and reliable in performing searching tasks. This system's functions make itself have the potential of being used for local navigation in unstructured environments.