13 research outputs found
Decentralized multi-agent path finding for UAV traffic management
The development of a real-world Unmanned Aircraft System (UAS) Traffic Management (UTM) system to ensure the safe integration of Unmanned Aerial Vehicles (UAVs) in low altitude airspace, has recently generated novel research challenges. A key problem is the development of Pre-Flight Conflict Detection and Resolution (CDR) methods that provide collision-free flight paths to all UAVs before their takeoff. Such problem can be represented as a Multi-Agent Path Finding (MAPF) problem. Currently, most MAPF methods assume that the UTM system is a centralized entity in charge of CDR. However, recent discussions on UTM suggest that such centralized control might not be practical or desirable. Therefore, we explore Pre-Flight CDR methods where independent UAS Service Providers (UASSPs) with their own interests, communicate with each other to resolve conflicts among their UAV operations--without centralized UTM directives. We propose a novel MAPF model that supports the decentralized resolution of conflicts, whereby different `agents', here UASSPs, manage their UAV operations. We present two approaches: (1) a prioritization approach and (2) a simple yet practical pairwise negotiation approach where UASSPs agents determine an agreement to solve conflicts between their UAV operations. We evaluate the performance of our proposed approaches with simulation scenarios based on a consultancy study of predicted UAV traffic for delivery services in Sendai, Japan, 2030. We demonstrate that our negotiation approach improves the ``fairness'' between UASSPs, i.e. the distribution of costs between UASSPs in terms of total delays and rejected operations due to replanning is more balanced when compared to the prioritization approach
Recommended from our members
Multi-agent path finding for UAV traffic management: Robotics track
Unmanned aerial vehicles (UAVs) are expected to provide a wide range of services, whereby UAV fleets will be managed by several independent service providers in shared low-altitude airspace. One important element, or redundancy, for safe and efficient UAV operation is pre-flight Conflict Detection and Resolution (CDR) methods that generate conflict-free paths for UAVs before the actual flight. Multi-Agent Path Finding (MAPF) has already been successfully applied to comparable problems with ground robots. However, most MAPF methods were tested with simplifying assumptions which do not reflect important characteristics of many real-world domains, such as delivery by UAVs where heterogeneous agents need to be considered, and new requests for flight operations are received continuously. In this paper, we extend CBS and ECBS to efficiently incorporate heterogeneous agents with computational geometry and we reduce the search space with spatio-temporal pruning. Moreover, our work introduces a “batching” method into CBS and ECBS to address increased amounts of requests for delivery operations in an efficient manner. We compare the performance of our “batching” approach in terms of runtime and solution cost to a “first-come first-served” approach. Our scenarios are based on a study on UAV usage predicted for 2030 in a real area in Japan. Our simulations indicate that our proposed ECBS based “batching” approach is more time efficient than incremental planning based on Cooperative A*, and hence can meet the requirements of timely and accurate response on delivery requests to users of such UTM services
Solving Multi-Agent Target Assignment and Path Finding with a Single Constraint Tree
Combined Target-Assignment and Path-Finding problem (TAPF) requires
simultaneously assigning targets to agents and planning collision-free paths
for agents from their start locations to their assigned targets. As a leading
approach to address TAPF, Conflict-Based Search with Target Assignment (CBS-TA)
leverages both K-best target assignments to create multiple search trees and
Conflict-Based Search (CBS) to resolve collisions in each search tree. While
being able to find an optimal solution, CBS-TA suffers from scalability due to
the duplicated collision resolution in multiple trees and the expensive
computation of K-best assignments. We therefore develop Incremental Target
Assignment CBS (ITA-CBS) to bypass these two computational bottlenecks. ITA-CBS
generates only a single search tree and avoids computing K-best assignments by
incrementally computing new 1-best assignments during the search. We show that,
in theory, ITA-CBS is guaranteed to find an optimal solution and, in practice,
is computationally efficient
EECBS: A Bounded-Suboptimal Search for Multi-Agent Path Finding
Multi-Agent Path Finding (MAPF), i.e., finding collision-free paths for
multiple robots, is important for many applications where small runtimes are
necessary, including the kind of automated warehouses operated by Amazon. CBS
is a leading two-level search algorithm for solving MAPF optimally. ECBS is a
bounded-suboptimal variant of CBS that uses focal search to speed up CBS by
sacrificing optimality and instead guaranteeing that the costs of its solutions
are within a given factor of optimal. In this paper, we study how to decrease
its runtime even further using inadmissible heuristics. Motivated by Explicit
Estimation Search (EES), we propose Explicit Estimation CBS (EECBS), a new
bounded-suboptimal variant of CBS, that uses online learning to obtain
inadmissible estimates of the cost of the solution of each high-level node and
uses EES to choose which high-level node to expand next. We also investigate
recent improvements of CBS and adapt them to EECBS. We find that EECBS with the
improvements runs significantly faster than the state-of-the-art
bounded-suboptimal MAPF algorithms ECBS, BCP-7, and eMDD-SAT on a variety of
MAPF instances. We hope that the scalability of EECBS enables additional
applications for bounded-suboptimal MAPF algorithms.Comment: Published at AAAI 202
A Competitive Analysis of Online Multi-Agent Path Finding
We study online Multi-Agent Path Finding (MAPF), where new agents are
constantly revealed over time and all agents must find collision-free paths to
their given goal locations. We generalize existing complexity results of
(offline) MAPF to online MAPF. We classify online MAPF algorithms into
different categories based on (1) controllability (the set of agents that they
can plan paths for at each time) and (2) rationality (the quality of paths they
plan) and study the relationships between them. We perform a competitive
analysis for each category of online MAPF algorithms with respect to
commonly-used objective functions. We show that a naive algorithm that routes
newly-revealed agents one at a time in sequence achieves a competitive ratio
that is asymptotically bounded from both below and above by the number of
agents with respect to flowtime and makespan. We then show a counter-intuitive
result that, if rerouting of previously-revealed agents is not allowed, any
rational online MAPF algorithms, including ones that plan optimal paths for all
newly-revealed agents, have the same asymptotic competitive ratio as the naive
algorithm, even on 2D 4-neighbor grids. We also derive constant lower bounds on
the competitive ratio of any rational online MAPF algorithms that allow
rerouting. The results thus provide theoretical insights into the effectiveness
of using MAPF algorithms in an online setting for the first time.Comment: Published at ICAPS 202
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
Rule-based conflict management for unmanned traffic management scenarios
The growing use of Unmanned Aerial Vehicles (UAVs) operations will require effective conflict management to keep the shared airspace safe and avoid conflicts among airspace users. Conflicts pose high risk and hazard to human lives and assets as they ma may result in financial and human loss. The proposed rule-based conflict management model consists of three main stages. The first stage includes strategic deconfliction during the flight plan generation. The second stage, pre-tactical deconfliction, applies a ground delay to the agent to resolve the conflict. The third stage corresponds to the tactical deconfliction, where the drone hovers or loiter in the last waypoint before the conflict area until the conflict time window passes. The proposed method differs from most existing conflict management approaches in that it applies deconfliction methods sequentially using a rule-based strategy. Furthermore, a high number of published studies do not consider realistic airspace constraints and potential airspace modernization concepts such as dynamic flight restrictions Assessment and validation are performed in three simulation scenarios that consider different patterns of the airspace availability in the areas where flights may be restricted, such as airfields, recreational areas, and prisons. The Particle Swarm Optimization (PSO) algorithm was used for drone path planning. For the simulated scenarios all of the conflicts were resolved after implementation of the proposed method. The implemented method is simple, flexible and suitable for the management of more complex and dense airspaces
Recommended from our members
Pre-flight conflict detection and resolution for UAV integration in shared airspace: Sendai 2030 model case
The increasing demand for services performed by Unmanned Aerial Vehicles (UAVs) requires the simulation of Unmanned Aircraft System Traffic Management (UTM) systems. In particular, Pre-Flight Conflict Detection and Resolution (CDR) methods need to scale to future demand levels and generate conflict-free paths for a potentially large number of UAVs before actual takeoff. However, few studies have examined realistic scenarios and the requirements for the UTM system. In this paper, we focus on the Sendai 2030 model case, a realistic projection of UAV usage for deliveries in one area in Japan. This model case considers up to 21,000 requests for Unmanned Aircraft Systems (UAS) operations over a 13 hour service time, and thus poses a challenge for the Pre-Flight CDR methods. Therefore, we propose an airspace reservation method based on 4DT (3D plus time Trajectories) and map the Pre-Flight CDR problem to a Multi-Agent Path Finding (MAPF) problem. We study first-come first-served (FCFS) and “batch” processing of UAS operation requests, and compare the throughput of those methods. We analyze the air traffic topology of deliveries by UAVs, and discuss several metrics to better understand the complexity of air traffic in the Sendai model case
White shark optimizer with optimal deep learning based effective unmanned aerial vehicles communication and scene classification.
Unmanned aerial vehicles (UAVs) become a promising enabler for the next generation of wireless networks with the tremendous growth in electronics and communications. The application of UAV communications comprises messages relying on coverage extension for transmission networks after disasters, Internet of Things (IoT) devices, and dispatching distress messages from the device positioned within the coverage hole to the emergency centre. But there are some problems in enhancing UAV clustering and scene classification using deep learning approaches for enhancing performance. This article presents a new White Shark Optimizer with Optimal Deep Learning based Effective Unmanned Aerial Vehicles Communication and Scene Classification (WSOODL-UAVCSC) technique. UAV clustering and scene categorization present many deep learning challenges in disaster management: scene understanding complexity, data variability and abundance, visual data feature extraction, nonlinear and high-dimensional data, adaptability and generalization, real-time decision making, UAV clustering optimization, sparse and incomplete data. the need to handle complex, high-dimensional data, adapt to changing environments, and make quick, correct decisions in critical situations drives deep learning in UAV clustering and scene categorization. The purpose of the WSOODL-UAVCSC technique is to cluster the UAVs for effective communication and scene classification. The WSO algorithm is utilized for the optimization of the UAV clustering process and enables to accomplish effective communication and interaction in the network. With dynamic adjustment of the clustering, the WSO algorithm improves the performance and robustness of the UAV system. For the scene classification process, the WSOODL-UAVCSC technique involves capsule network (CapsNet) feature extraction, marine predators algorithm (MPA) based hyperparameter tuning, and echo state network (ESN) classification. A wide-ranging simulation analysis was conducted to validate the enriched performance of the WSOODL-UAVCSC approach. Extensive result analysis pointed out the enhanced performance of the WSOODL-UAVCSC method over other existing techniques. The WSOODL-UAVCSC method achieved an accuracy of 99.12%, precision of 97.45%, recall of 98.90%, and F1-score of 98.10% when compared to other existing techniques