41 research outputs found
Online Informative Path Planning for Active Classification on UAVs
We propose an informative path planning (IPP) algorithm for active
classification using an unmanned aerial vehicle (UAV), focusing on weed
detection in precision agriculture. We model the presence of weeds on farmland
using an occupancy grid and generate plans according to information-theoretic
objectives, enabling the UAV to gather data efficiently. We use a combination
of global viewpoint selection and evolutionary optimization to refine the UAV's
trajectory in continuous space while satisfying dynamic constraints. We
validate our approach in simulation by comparing against standard "lawnmower"
coverage, and study the effects of varying objectives and optimization
strategies. We plan to evaluate our algorithm on a real platform in the
immediate future.Comment: 7 pages, 4 figures, submission to International Symposium on
Experimental Robotics 201
Active Classification: Theory and Application to Underwater Inspection
We discuss the problem in which an autonomous vehicle must classify an object
based on multiple views. We focus on the active classification setting, where
the vehicle controls which views to select to best perform the classification.
The problem is formulated as an extension to Bayesian active learning, and we
show connections to recent theoretical guarantees in this area. We formally
analyze the benefit of acting adaptively as new information becomes available.
The analysis leads to a probabilistic algorithm for determining the best views
to observe based on information theoretic costs. We validate our approach in
two ways, both related to underwater inspection: 3D polyhedra recognition in
synthetic depth maps and ship hull inspection with imaging sonar. These tasks
encompass both the planning and recognition aspects of the active
classification problem. The results demonstrate that actively planning for
informative views can reduce the number of necessary views by up to 80% when
compared to passive methods.Comment: 16 page
Forward Stochastic Reachability Analysis for Uncontrolled Linear Systems using Fourier Transforms
We propose a scalable method for forward stochastic reachability analysis for
uncontrolled linear systems with affine disturbance. Our method uses Fourier
transforms to efficiently compute the forward stochastic reach probability
measure (density) and the forward stochastic reach set. This method is
applicable to systems with bounded or unbounded disturbance sets. We also
examine the convexity properties of the forward stochastic reach set and its
probability density. Motivated by the problem of a robot attempting to capture
a stochastically moving, non-adversarial target, we demonstrate our method on
two simple examples. Where traditional approaches provide approximations, our
method provides exact analytical expressions for the densities and probability
of capture.Comment: V3: HSCC 2017 (camera-ready copy), DOI updated, minor changes | V2:
Review comments included | V1: 10 pages, 12 figure
Decentralised Mission Monitoring with Spatiotemporal Optimal Stopping
© 2018 IEEE. We consider a multi-robot variant of the mission monitoring problem. This problem arises in tasks where a robot observes the progress of another robot that is stochastically following a known trajectory, among other applications. We formulate and solve a variant where multiple tracker robots must monitor a single target robot, which is important because it enables the use of multi-robot systems to improve task performance in practice, such as in marine robotics missions. Our algorithm coordinates the behaviour of the trackers by computing optimal single-robot paths given a probabilistic representation of the other robots' paths. We employ a decentralised scheme that optimises over probability distributions of plans and has useful analytical properties. The planned trajectories collectively maximise the probability of observing the target throughout the mission with respect to probabilistic motion and observation models. We report simulation results for up to 8 robots that support our analysis and indicate that our algorithm is a feasible solution for improving the performance of mission monitoring systems
Cops and Invisible Robbers: the Cost of Drunkenness
We examine a version of the Cops and Robber (CR) game in which the robber is
invisible, i.e., the cops do not know his location until they capture him.
Apparently this game (CiR) has received little attention in the CR literature.
We examine two variants: in the first the robber is adversarial (he actively
tries to avoid capture); in the second he is drunk (he performs a random walk).
Our goal in this paper is to study the invisible Cost of Drunkenness (iCOD),
which is defined as the ratio ct_i(G)/dct_i(G), with ct_i(G) and dct_i(G) being
the expected capture times in the adversarial and drunk CiR variants,
respectively. We show that these capture times are well defined, using game
theory for the adversarial case and partially observable Markov decision
processes (POMDP) for the drunk case. We give exact asymptotic values of iCOD
for several special graph families such as -regular trees, give some bounds
for grids, and provide general upper and lower bounds for general classes of
graphs. We also give an infinite family of graphs showing that iCOD can be
arbitrarily close to any value in [2,infinty). Finally, we briefly examine one
more CiR variant, in which the robber is invisible and "infinitely fast"; we
argue that this variant is significantly different from the Graph Search game,
despite several similarities between the two games