85 research outputs found
Decentralized Control of Partially Observable Markov Decision Processes using Belief Space Macro-actions
The focus of this paper is on solving multi-robot planning problems in
continuous spaces with partial observability. Decentralized partially
observable Markov decision processes (Dec-POMDPs) are general models for
multi-robot coordination problems, but representing and solving Dec-POMDPs is
often intractable for large problems. To allow for a high-level representation
that is natural for multi-robot problems and scalable to large discrete and
continuous problems, this paper extends the Dec-POMDP model to the
decentralized partially observable semi-Markov decision process (Dec-POSMDP).
The Dec-POSMDP formulation allows asynchronous decision-making by the robots,
which is crucial in multi-robot domains. We also present an algorithm for
solving this Dec-POSMDP which is much more scalable than previous methods since
it can incorporate closed-loop belief space macro-actions in planning. These
macro-actions are automatically constructed to produce robust solutions. The
proposed method's performance is evaluated on a complex multi-robot package
delivery problem under uncertainty, showing that our approach can naturally
represent multi-robot problems and provide high-quality solutions for
large-scale problems
FRAME: Fast and Robust Autonomous 3D point cloud Map-merging for Egocentric multi-robot exploration
This article presents a 3D point cloud map-merging framework for egocentric
heterogeneous multi-robot exploration, based on overlap detection and
alignment, that is independent of a manual initial guess or prior knowledge of
the robots' poses. The novel proposed solution utilizes state-of-the-art place
recognition learned descriptors, that through the framework's main pipeline,
offer a fast and robust region overlap estimation, hence eliminating the need
for the time-consuming global feature extraction and feature matching process
that is typically used in 3D map integration. The region overlap estimation
provides a homogeneous rigid transform that is applied as an initial condition
in the point cloud registration algorithm Fast-GICP, which provides the final
and refined alignment. The efficacy of the proposed framework is experimentally
evaluated based on multiple field multi-robot exploration missions in
underground environments, where both ground and aerial robots are deployed,
with different sensor configurations.Comment: to be publishe
Robust Collision-free Lightweight Aerial Autonomy for Unknown Area Exploration
Collision-free path planning is an essential requirement for autonomous
exploration in unknown environments, especially when operating in confined
spaces or near obstacles. This study presents an autonomous exploration
technique using a small drone. A local end-point selection method is designed
using LiDAR range measurement and then generates the path from the current
position to the selected end-point. The generated path shows the consistent
collision-free path in real-time by adopting the Euclidean signed distance
field-based grid-search method. The simulation results consistently showed the
safety, and reliability of the proposed path-planning method. Real-world
experiments are conducted in three different mines, demonstrating successful
autonomous exploration flight in environments with various structural
conditions. The results showed the high capability of the proposed flight
autonomy framework for lightweight aerial-robot systems. Besides, our drone
performs an autonomous mission during our entry at the Tunnel Circuit
competition (Phase 1) of the DARPA Subterranean Challenge.Comment: 8 page
Health Aware Stochastic Planning For Persistent Package Delivery Missions Using Quadrotors
In persistent missions, taking system’s health and capability degradation into account is an essential factor to predict and avoid failures. The state space in health-aware planning problems is often a mixture of continuous vehicle-level and discrete mission-level states. This in particular poses a challenge when the mission domain is partially observable and restricts the use of computationally expensive forward search methods. This paper presents a method that exploits a structure that exists in many health-aware planning problems and performs a two-layer planning scheme. The lower layer exploits the local linearization and Gaussian distribution assumption over vehicle-level states while the higher layer maintains a non-Gaussian distribution over discrete mission-level variables. This two-layer planning scheme allows us to limit the expensive online forward search to the mission-level states, and thus predict system’s behavior over longer horizons in the future. We demonstrate the performance of the method on a long duration package delivery mission using a quadrotor in a partially-observable domain in the presence of constraints and health/capability degradation
Where to Map? Iterative Rover-Copter Path Planning for Mars Exploration
In addition to conventional ground rovers, the Mars 2020 mission will send a
helicopter to Mars. The copter's high-resolution data helps the rover to
identify small hazards such as steps and pointy rocks, as well as providing
rich textual information useful to predict perception performance. In this
paper, we consider a three-agent system composed of a Mars rover, copter, and
orbiter. The objective is to provide good localization to the rover by
selecting an optimal path that minimizes the localization uncertainty
accumulation during the rover's traverse. To achieve this goal, we quantify the
localizability as a goodness measure associated with the map, and conduct a
joint-space search over rover's path and copter's perceptual actions given
prior information from the orbiter. We jointly address where to map by the
copter and where to drive by the rover using the proposed iterative
copter-rover path planner. We conducted numerical simulations using the map of
Mars 2020 landing site to demonstrate the effectiveness of the proposed
planner.Comment: 8 pages, 7 figure
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