Active Area Coverage from Equilibrium

EasyChair Preprint 745

Abstract

This paper develops a method for robots to integrate stability into actively seeking out informative measurements through coverage. We derive a controller using hybrid systems theory that allows us to consider safe equilibrium policies during active data collection. We show that our method is able to maintain Lyapunov attractiveness while still actively seeking out data. Using incremental sparse Gaussian processes, we define distributions which allow a robot to actively seek out informative measurements. We illustrate our methods for shape estimation using a cart double pendulum, dynamic model learning of a hovering quadrotor, and generating galloping gaits starting from stationary equilibrium by learning a dynamics model for the half-cheetah system from the Roboschool environment.

Keyphrases: Optimization and Optimal Control, Task Planning and AI Reasoning, active data acquisition, probabilistic reasoning

@booklet{EasyChair:745,
  author    = {Ian Abraham and Ahalya Prabhakar and Todd Murphey},
  title     = {Active Area Coverage from Equilibrium},
  doi       = {10.29007/k34m},
  howpublished = {EasyChair Preprint 745},
  year      = {EasyChair, 2019}}