107,170 research outputs found
Multi-agent pathfinding for unmanned aerial vehicles
Unmanned aerial vehicles (UAVs), commonly known as drones, have become more and
more prevalent in recent years. In particular, governmental organizations and companies
around the world are starting to research how UAVs can be used to perform tasks such
as package deliver, disaster investigation and surveillance of key assets such as pipelines,
railroads and bridges. NASA is currently in the early stages of developing an air traffic
control system specifically designed to manage UAV operations in low-altitude airspace.
Companies such as Amazon and Rakuten are testing large-scale drone deliver services in
the USA and Japan.
To perform these tasks, safe and conflict-free routes for concurrently operating UAVs must
be found. This can be done using multi-agent pathfinding (mapf) algorithms, although
the correct choice of algorithms is not clear. This is because many state of the art mapf
algorithms have only been tested in 2D space in maps with many obstacles, while UAVs
operate in 3D space in open maps with few obstacles. In addition, when an unexpected
event occurs in the airspace and UAVs are forced to deviate from their original routes
while inflight, new conflict-free routes must be found. Planning for these unexpected
events is commonly known as contingency planning. With manned aircraft, contingency
plans can be created in advance or on a case-by-case basis while inflight. The scale at
which UAVs operate, combined with the fact that unexpected events may occur anywhere
at any time make both advanced planning and planning on a case-by-case basis impossible.
Thus, a new approach is needed. Online multi-agent pathfinding (online mapf) looks to
be a promising solution. Online mapf utilizes traditional mapf algorithms to perform path
planning in real-time. That is, new routes for UAVs are found while inflight.
The primary contribution of this thesis is to present one possible approach to UAV
contingency planning using online multi-agent pathfinding algorithms, which can be used
as a baseline for future research and development. It also provides an in-depth overview
and analysis of offline mapf algorithms with the goal of determining which ones are likely
to perform best when applied to UAVs. Finally, to further this same goal, a few different
mapf algorithms are experimentally tested and analyzed
Resilience of multi-robot systems to physical masquerade attacks
The advent of autonomous mobile multi-robot systems has driven innovation in both the industrial and defense sectors. The integration of such systems in safety-and security-critical applications has raised concern over their resilience to attack. In this work, we investigate the security problem of a stealthy adversary masquerading as a properly functioning agent. We show that conventional multi-agent pathfinding solutions are vulnerable to these physical masquerade attacks. Furthermore, we provide a constraint-based formulation of multi-agent pathfinding that yields multi-agent plans that are provably resilient to physical masquerade attacks. This formalization leverages inter-agent observations to facilitate introspective monitoring to guarantee resilience.Accepted manuscrip
Applying MAPP Algorithm for Cooperative Path Finding in Urban Environments
The paper considers the problem of planning a set of non-conflict
trajectories for the coalition of intelligent agents (mobile robots). Two
divergent approaches, e.g. centralized and decentralized, are surveyed and
analyzed. Decentralized planner - MAPP is described and applied to the task of
finding trajectories for dozens UAVs performing nap-of-the-earth flight in
urban environments. Results of the experimental studies provide an opportunity
to claim that MAPP is a highly efficient planner for solving considered types
of tasks
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