1,555 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
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
Micro Rapid Mapping: Automatic UAV-based Remote Sensing for Chemical Emergencies
In chemical emergencies, response units rely on the speedy provision of detailed information about the area affected by potentially hazardous substances in order to decide on efficient response actions. Unmanned Aerial Vehicles (UAVs) equipped with remote sensing equipment offer a flexible way of providing this information. Hence, these systems are becoming more and more interesting for firefighters and plant operators alike. Although having to actively control the UAVs makes high demands on the response squad in an already stressful situation, cyber-physical systems that allow the automatic deployment of UAVs have rarely been studied to date. We present and evaluate a system for planning UAV missions in emergency situations. We propose two different planning algorithms: (1) a mapping approach for covering the entire target area; (2) an algorithm that selects sensing locations across a wide area in order to allow quicker exploration. We verify the applicability in an extensive simulative study and demonstrate the information gain achieved, as well as the remaining uncertainty, after a flight, about the spatial phenomenon observed
- …