2 research outputs found
Risk-Aware Planning and Assignment for Ground Vehicles using Uncertain Perception from Aerial Vehicles
We propose a risk-aware framework for multi-robot, multi-demand assignment
and planning in unknown environments. Our motivation is disaster response and
search-and-rescue scenarios where ground vehicles must reach demand locations
as soon as possible. We consider a setting where the terrain information is
available only in the form of an aerial, georeferenced image. Deep learning
techniques can be used for semantic segmentation of the aerial image to create
a cost map for safe ground robot navigation. Such segmentation may still be
noisy. Hence, we present a joint planning and perception framework that
accounts for the risk introduced due to noisy perception. Our contributions are
two-fold: (i) we show how to use Bayesian deep learning techniques to extract
risk at the perception level; and (ii) use a risk-theoretical measure, CVaR,
for risk-aware planning and assignment. The pipeline is theoretically
established, then empirically analyzed through two datasets. We find that
accounting for risk at both levels produces quantifiably safer paths and
assignments
Perception-Based Temporal Logic Planning in Uncertain Semantic Maps
This paper addresses a multi-robot planning problem in partially unknown
semantic environments. The environment is assumed to have known geometric
structure (e.g., walls) and to be occupied by static labeled landmarks with
uncertain positions and classes. This modeling approach gives rise to an
uncertain semantic map generated by semantic SLAM algorithms. Our goal is to
design control policies for robots equipped with noisy perception systems so
that they can accomplish collaborative tasks captured by global temporal logic
specifications. To account for environmental and perceptual uncertainty, we
extend a fragment of Linear Temporal Logic (LTL), called co-safe LTL, by
including perception-based atomic predicates allowing us to incorporate
uncertainty-wise and probabilistic satisfaction requirements directly into the
task specification. The perception-based LTL planning problem gives rise to an
optimal control problem, solved by a novel sampling-based algorithm, that
generates open-loop control policies that are updated online to adapt to a
continuously learned semantic map. We provide extensive experiments to
demonstrate the efficiency of the proposed planning architecture