128 research outputs found
Autonomous 3D Exploration of Large Structures Using an UAV Equipped with a 2D LIDAR
This paper addressed the challenge of exploring large, unknown, and unstructured
industrial environments with an unmanned aerial vehicle (UAV). The resulting system combined
well-known components and techniques with a new manoeuvre to use a low-cost 2D laser to measure
a 3D structure. Our approach combined frontier-based exploration, the Lazy Theta* path planner, and
a flyby sampling manoeuvre to create a 3D map of large scenarios. One of the novelties of our system
is that all the algorithms relied on the multi-resolution of the octomap for the world representation.
We used a Hardware-in-the-Loop (HitL) simulation environment to collect accurate measurements
of the capability of the open-source system to run online and on-board the UAV in real-time. Our
approach is compared to different reference heuristics under this simulation environment showing
better performance in regards to the amount of explored space. With the proposed approach, the UAV
is able to explore 93% of the search space under 30 min, generating a path without repetition that
adjusts to the occupied space covering indoor locations, irregular structures, and suspended obstaclesUnión Europea Marie Sklodowska-Curie 64215Unión Europea MULTIDRONE (H2020-ICT-731667)Uniión Europea HYFLIERS (H2020-ICT-779411
Autonomous Reality Modelling for Cultural Heritage Sites employing cooperative quadrupedal robots and unmanned aerial vehicles
Nowadays, the use of advanced sensors, such as terrestrial 3D laser scanners,
mobile LiDARs and Unmanned Aerial Vehicles (UAV) photogrammetric imaging, has
become the prevalent practice for 3D Reality Modeling and digitization of
large-scale monuments of Cultural Heritage (CH). In practice, this process is
heavily related to the expertise of the surveying team, handling the laborious
planning and time-consuming execution of the 3D mapping process that is
tailored to the specific requirements and constraints of each site. To minimize
human intervention, this paper introduces a novel methodology for autonomous 3D
Reality Modeling for CH monuments by employing au-tonomous biomimetic
quadrupedal robotic agents and UAVs equipped with the appropriate sensors.
These autonomous robotic agents carry out the 3D RM process in a systematic and
repeatable ap-proach. The outcomes of this automated process may find
applications in digital twin platforms, facilitating secure monitoring and
management of cultural heritage sites and spaces, in both indoor and outdoor
environments
Sampling-based Motion Planning for Active Multirotor System Identification
This paper reports on an algorithm for planning trajectories that allow a
multirotor micro aerial vehicle (MAV) to quickly identify a set of unknown
parameters. In many problems like self calibration or model parameter
identification some states are only observable under a specific motion. These
motions are often hard to find, especially for inexperienced users. Therefore,
we consider system model identification in an active setting, where the vehicle
autonomously decides what actions to take in order to quickly identify the
model. Our algorithm approximates the belief dynamics of the system around a
candidate trajectory using an extended Kalman filter (EKF). It uses
sampling-based motion planning to explore the space of possible beliefs and
find a maximally informative trajectory within a user-defined budget. We
validate our method in simulation and on a real system showing the feasibility
and repeatability of the proposed approach. Our planner creates trajectories
which reduce model parameter convergence time and uncertainty by a factor of
four.Comment: Published at ICRA 2017. Video available at
https://www.youtube.com/watch?v=xtqrWbgep5
Heuristic-based Incremental Probabilistic Roadmap for Efficient UAV Exploration in Dynamic Environments
Autonomous exploration in dynamic environments necessitates a planner that
can proactively respond to changes and make efficient and safe decisions for
robots. Although plenty of sampling-based works have shown success in exploring
static environments, their inherent sampling randomness and limited utilization
of previous samples often result in sub-optimal exploration efficiency.
Additionally, most of these methods struggle with efficient replanning and
collision avoidance in dynamic settings. To overcome these limitations, we
propose the Heuristic-based Incremental Probabilistic Roadmap Exploration
(HIRE) planner for UAVs exploring dynamic environments. The proposed planner
adopts an incremental sampling strategy based on the probabilistic roadmap
constructed by heuristic sampling toward the unexplored region next to the free
space, defined as the heuristic frontier regions. The heuristic frontier
regions are detected by applying a lightweight vision-based method to the
different levels of the occupancy map. Moreover, our dynamic module ensures
that the planner dynamically updates roadmap information based on the
environment changes and avoids dynamic obstacles. Simulation and physical
experiments prove that our planner can efficiently and safely explore dynamic
environments
Exploration with Global Consistency Using Real-Time Re-integration and Active Loop Closure
Despite recent progress of robotic exploration, most methods assume that
drift-free localization is available, which is problematic in reality and
causes severe distortion of the reconstructed map. In this work, we present a
systematic exploration mapping and planning framework that deals with drifted
localization, allowing efficient and globally consistent reconstruction. A
real-time re-integration-based mapping approach along with a frame pruning
mechanism is proposed, which rectifies map distortion effectively when drifted
localization is corrected upon detecting loop-closure. Besides, an exploration
planning method considering historical viewpoints is presented to enable active
loop closing, which promotes a higher opportunity to correct localization
errors and further improves the mapping quality. We evaluate both the mapping
and planning methods as well as the entire system comprehensively in simulation
and real-world experiments, showing their effectiveness in practice. The
implementation of the proposed method will be made open-source for the benefit
of the robotics community
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