9 research outputs found
Search and Rescue under the Forest Canopy using Multiple UAVs
We present a multi-robot system for GPS-denied search and rescue under the
forest canopy. Forests are particularly challenging environments for
collaborative exploration and mapping, in large part due to the existence of
severe perceptual aliasing which hinders reliable loop closure detection for
mutual localization and map fusion. Our proposed system features unmanned
aerial vehicles (UAVs) that perform onboard sensing, estimation, and planning.
When communication is available, each UAV transmits compressed tree-based
submaps to a central ground station for collaborative simultaneous localization
and mapping (CSLAM). To overcome high measurement noise and perceptual
aliasing, we use the local configuration of a group of trees as a distinctive
feature for robust loop closure detection. Furthermore, we propose a novel
procedure based on cycle consistent multiway matching to recover from incorrect
pairwise data associations. The returned global data association is guaranteed
to be cycle consistent, and is shown to improve both precision and recall
compared to the input pairwise associations. The proposed multi-UAV system is
validated both in simulation and during real-world collaborative exploration
missions at NASA Langley Research Center.Comment: IJRR revisio
N-MPC for Deep Neural Network-Based Collision Avoidance exploiting Depth Images
This paper introduces a Nonlinear Model Predictive Control (N-MPC) framework
exploiting a Deep Neural Network for processing onboard-captured depth images
for collision avoidance in trajectory-tracking tasks with UAVs. The network is
trained on simulated depth images to output a collision score for queried 3D
points within the sensor field of view. Then, this network is translated into
an algebraic symbolic equation and included in the N-MPC, explicitly
constraining predicted positions to be collision-free throughout the receding
horizon. The N-MPC achieves real time control of a UAV with a control frequency
of 100Hz. The proposed framework is validated through statistical analysis of
the collision classifier network, as well as Gazebo simulations and real
experiments to assess the resulting capabilities of the N-MPC to effectively
avoid collisions in cluttered environments. The associated code is released
open-source along with the training images.Comment: Accepted to the IEEE International Conference on Robotics and
Automation (ICRA) 202
Air-Aided Communication Between Ground Assets in a Poisson Forest
Ground assets deployed in a cluttered environment with randomized obstacles
(e.g., a forest) may experience line of sight (LoS) obstruction due to those
obstacles. Air assets can be deployed in the vicinity to aid the communication
by establishing two-hop paths between the ground assets. Obstacles that are
taller than a position-dependent critical height may still obstruct the LoS
between a ground asset and an air asset. In this paper, we provide an
analytical framework for computing the probability of obtaining a LoS path in a
Poisson forest. Given the locations and heights of a ground asset and an air
asset, we establish the critical height, which is a function of distance. To
account for this dependence on distance, the blocking is modeled as an
inhomogenous Poisson point process, and the LoS probability is its void
probability. Examples and closed-form expressions are provided for two
obstruction height distributions: uniform and truncated Gaussian. The examples
are validated through simulation. Additionally, the end-to-end throughput is
determined and shown to be a metric that balances communication distance with
the impact of LoS blockage. Throughput is used to determine the range at which
it is better to relay communications through the air asset, and, when the air
asset is deployed, its optimal height.Comment: Military Communications Conference, MILCOM 202
Towards Multi-robot Exploration: A Decentralized Strategy for UAV Forest Exploration
Efficient exploration strategies are vital in tasks such as search-and-rescue
missions and disaster surveying. Unmanned Aerial Vehicles (UAVs) have become
particularly popular in such applications, promising to cover large areas at
high speeds. Moreover, with the increasing maturity of onboard UAV perception,
research focus has been shifting toward higher-level reasoning for single- and
multi-robot missions. However, autonomous navigation and exploration of
previously unknown large spaces still constitutes an open challenge, especially
when the environment is cluttered and exhibits large and frequent occlusions
due to high obstacle density, as is the case of forests. Moreover, the problem
of long-distance wireless communication in such scenes can become a limiting
factor, especially when automating the navigation of a UAV swarm. In this
spirit, this work proposes an exploration strategy that enables UAVs, both
individually and in small swarms, to quickly explore complex scenes in a
decentralized fashion. By providing the decision-making capabilities to each
UAV to switch between different execution modes, the proposed strategy strikes
a great balance between cautious exploration of yet completely unknown regions
and more aggressive exploration of smaller areas of unknown space. This results
in full coverage of forest areas of variable density, consistently faster than
the state of the art. Demonstrating successful deployment with a single UAV as
well as a swarm of up to three UAVs, this work sets out the basic principles
for multi-root exploration of cluttered scenes, with up to 65% speed up in the
single UAV case and 40% increase in explored area for the same mission time in
multi-UAV setups
Exploring the Technical Advances and Limits of Autonomous UAVs for Precise Agriculture in Constrained Environments
In the field of precise agriculture with autonomous unmanned aerial vehicles (UAVs), the utilization of drones holds significant potential to transform crop monitoring, management, and harvesting techniques. However, despite the numerous benefits of UAVs in smart farming, there are still several technical challenges that need to be addressed in order to render their widespread adoption possible, especially in constrained environments. This paper provides a study of the technical aspect and limitations of autonomous UAVs in precise agriculture applications for constrained environments
RACER: Rapid Collaborative Exploration with a Decentralized Multi-UAV System
Although the use of multiple Unmanned Aerial Vehicles (UAVs) has great
potential for fast autonomous exploration, it has received far too little
attention. In this paper, we present RACER, a RApid Collaborative ExploRation
approach using a fleet of decentralized UAVs. To effectively dispatch the UAVs,
a pairwise interaction based on an online hgrid space decomposition is used. It
ensures that all UAVs simultaneously explore distinct regions, using only
asynchronous and limited communication. Further, we optimize the coverage paths
of unknown space and balance the workloads partitioned to each UAV with a
Capacitated Vehicle Routing Problem(CVRP) formulation. Given the task
allocation, each UAV constantly updates the coverage path and incrementally
extracts crucial information to support the exploration planning. A
hierarchical planner finds exploration paths, refines local viewpoints and
generates minimum-time trajectories in sequence to explore the unknown space
agilely and safely. The proposed approach is evaluated extensively, showing
high exploration efficiency, scalability and robustness to limited
communication. Furthermore, for the first time, we achieve fully decentralized
collaborative exploration with multiple UAVs in real world. We will release our
implementation as an open-source package.Comment: Conditionally accpeted by TR
Search and rescue under the forest canopy using multiple UAVs
© The Author(s) 2020. We present a multi-robot system for GPS-denied search and rescue under the forest canopy. Forests are particularly challenging environments for collaborative exploration and mapping, in large part due to the existence of severe perceptual aliasing which hinders reliable loop closure detection for mutual localization and map fusion. Our proposed system features unmanned aerial vehicles (UAVs) that perform onboard sensing, estimation, and planning. When communication is available, each UAV transmits compressed tree-based submaps to a central ground station for collaborative simultaneous localization and mapping (CSLAM). To overcome high measurement noise and perceptual aliasing, we use the local configuration of a group of trees as a distinctive feature for robust loop closure detection. Furthermore, we propose a novel procedure based on cycle consistent multiway matching to recover from incorrect pairwise data associations. The returned global data association is guaranteed to be cycle consistent, and is shown to improve both precision and recall compared with the input pairwise associations. The proposed multi-UAV system is validated both in simulation and during real-world collaborative exploration missions at NASA Langley Research Center