To look or not to look: A hierarchical representation for visual planning on mobile robots

Abstract

Mobile robots are increasingly being used in real-world applications due to the ready availability of high-fidelity sensors and the development of sophisticated information processing algorithms. However, one key challenge to the widespread deployment of mobile robots equipped with multiple sensors and processing algorithms is the ability to autonomously tailor sensing and information processing to the task at hand. This paper poses this challenge as the task of planning under uncertainty, and more specifically as an instance of probabilistic sequential decision-making. A novel hierarchy of partially observable Markov decision processes (POMDPs) is incorporated, which uses constrained-convolutional policies and automatic belief propagation to achieve efficient and reliable operation on mobile robots. All algorithms are implemented and evaluated on simulated and physical robot platforms for the task of searching for target objects in dynamic indoor environments.