Planning for robotic exploration based on forward simulation

Abstract

We address the problem of controlling a mobile robot to explore a partially known environment. The robot's objective is the maximization of the amount of information collected about the environment. We formulate the problem as a partially observable Markov decision process (POMDP) with an information-theoretic objective function, and solve it applying forward simulation algorithms with an open-loop approximation. We present a new sample-based approximation for mutual information useful in mobile robotics. The approximation can be seamlessly integrated with forward simulation planning algorithms. We investigate the usefulness of POMDP based planning for exploration, and to alleviate some of its weaknesses propose a combination with frontier based exploration. Experimental results in simulated and real environments show that, depending on the environment, applying POMDP based planning for exploration can improve performance over frontier exploration.

19 pages, 11 figures in Robotics and Autonomous Systems (2016)

References in corpus (3)

• Value-Function Approximations for Partially Observable Markov Decision Processes
• Online Planning Algorithms for POMDPs
• Optimal Sensing via Multi-armed Bandit Relaxations in Mixed Observability Domains

Cited by in corpus (9)

• Partially Observable Markov Decision Processes in Robotics: A Survey
• Where to Look Next: Learning Viewpoint Recommendations for Informative Trajectory Planning
• Stochastic Motion Planning under Partial Observability for Mobile Robots with Continuous Range Measurements
• Hypermap Mapping Framework and its Application to Autonomous Semantic Exploration
• Learning to Track Dynamic Targets in Partially Known Environments
• Unsupervised Active Visual Search with Monte Carlo planning under Uncertain Detections
• Sensor Planning for Large Numbers of Robots
• Occupancy Map Building through Bayesian Exploration
• Multi-Robot Active Information Gathering with Periodic Communication