Robotics Commons^™

Formal Performance Guarantees For Behavior-Based Localization Missions, Damian Lyons, Ron Arkin, Shu Jiang, Matt O'Brien, Feng Tang, Peng Tang

Faculty Publications

Abstract— Localization and mapping algorithms can allow a robot to navigate well in an unknown environment. However, whether such algorithms enhance any specific robot mission is currently a matter for empirical validation. In this paper we apply our MissionLab/VIPARS mission design and verification approach to an autonomous robot mission that uses probabilistic localization software.

Two approaches to modeling probabilistic localization for verification are presented: a high-level approach, and a sample-based approach which allows run-time code to be embedded in verification. Verification and experimental validation results are presented for two different missions, each using each method, demonstrating the accuracy …

Landmark Detection With Surprise Saliency Using Convolutional Neural Networks, Feng Tang, Damian Lyons, Daniel Leeds

Faculty Publications

Abstract—Landmarks can be used as reference to enable people or robots to localize themselves or to navigate in their environment. Automatic definition and extraction of appropriate landmarks from the environment has proven to be a challenging task when pre-defined landmarks are not present. We propose a novel computational model of automatic landmark detection from a single image without any pre-defined landmark database. The hypothesis is that if an object looks abnormal due to its atypical scene context (what we call surprise saliency), it then may be considered as a good landmark because it is unique and easy to spot by …

Establishing Performance Guarantees For Behavior-Based Robot Missions Using An Smt Solver, Feng Tang, Damian M. Lyons, Ronald Arkin

Faculty Publications

In prior work we developed an approach to formally representing behavior-based multi-robot programs, and the uncertain environments in which they operate, as process networks. We automatically extract a set of probabilistic equations governing program execution in that environment using a static analysis module called VIPARS, and solve these using a Dynamic Bayesian Network (DBN) to establish whether stated performance guarantees hold for the program in that environment. In this paper we address the challenge of expanding the range of performance guarantees that are possible by using an SMT-solver instead of a DBN. We translate flow functions, which are recursive probabilistic …