282,724 research outputs found
Project scheduling under uncertainty using fuzzy modelling and solving techniques
In the real world, projects are subject to numerous uncertainties at different levels of planning. Fuzzy project scheduling is one of the approaches that deal with uncertainties in project scheduling problem. In this paper, we provide a new technique that keeps uncertainty at all steps of the modelling and solving procedure by considering a fuzzy modelling of the workload inspired from the fuzzy/possibilistic approach. Based on this modelling, two project scheduling techniques, Resource Constrained Scheduling and Resource Leveling, are considered and generalized to handle fuzzy parameters. We refer to these problems as the Fuzzy Resource Constrained Project Scheduling Problem (FRCPSP) and the Fuzzy Resource Leveling Problem (FRLP). A Greedy Algorithm and a Genetic Algorithm are provided to solve FRCPSP and FRLP respectively, and are applied to civil helicopter maintenance within the framework of a French industrial project called Helimaintenance
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
Unified Task Force Report to the Rebuild Iowa Advisory Commission
A unified report from the nine disaster recovery task forces outlining the way to a long-term recovery for Iowa surpassing just a return to normal
A Hierarchal Planning Framework for AUV Mission Management in a Spatio-Temporal Varying Ocean
The purpose of this paper is to provide a hierarchical dynamic mission
planning framework for a single autonomous underwater vehicle (AUV) to
accomplish task-assign process in a limited time interval while operating in an
uncertain undersea environment, where spatio-temporal variability of the
operating field is taken into account. To this end, a high level reactive
mission planner and a low level motion planning system are constructed. The
high level system is responsible for task priority assignment and guiding the
vehicle toward a target of interest considering on-time termination of the
mission. The lower layer is in charge of generating optimal trajectories based
on sequence of tasks and dynamicity of operating terrain. The mission planner
is able to reactively re-arrange the tasks based on mission/terrain updates
while the low level planner is capable of coping unexpected changes of the
terrain by correcting the old path and re-generating a new trajectory. As a
result, the vehicle is able to undertake the maximum number of tasks with
certain degree of maneuverability having situational awareness of the operating
field. The computational engine of the mentioned framework is based on the
biogeography based optimization (BBO) algorithm that is capable of providing
efficient solutions. To evaluate the performance of the proposed framework,
firstly, a realistic model of undersea environment is provided based on
realistic map data, and then several scenarios, treated as real experiments,
are designed through the simulation study. Additionally, to show the robustness
and reliability of the framework, Monte-Carlo simulation is carried out and
statistical analysis is performed. The results of simulations indicate the
significant potential of the two-level hierarchical mission planning system in
mission success and its applicability for real-time implementation
Formalized Resolution of the Scientific Task for Improving the Effective Planning Special Purpose Unmanned Aerial Vehicles Routes of Tactical Level
In the article, the features of application of special purpose unmanned aerial vehicles (UAVs) of the tactical level have resulted during the operation of the joint forces. The role of routes planning of special purpose UAVs of tactical level in the conditions of a concrete battle situation is determined. The necessity of the mathematical development for planning a flight route of special purpose UAVs is justified. Essence and maintenance of scientific task are set. Its formalized setting is guided
Prospects of a mathematical theory of human behavior in complex man-machine systems tasks
A hierarchy of human activities is derived by analyzing automobile driving in general terms. A structural description leads to a block diagram and a time-sharing computer analogy. The range of applicability of existing mathematical models is considered with respect to the hierarchy of human activities in actual complex tasks. Other mathematical tools so far not often applied to man machine systems are also discussed. The mathematical descriptions at least briefly considered here include utility, estimation, control, queueing, and fuzzy set theory as well as artificial intelligence techniques. Some thoughts are given as to how these methods might be integrated and how further work might be pursued
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
Lifelong Multi-Agent Path Finding in Large-Scale Warehouses
Multi-Agent Path Finding (MAPF) is the problem of moving a team of agents to
their goal locations without collisions. In this paper, we study the lifelong
variant of MAPF, where agents are constantly engaged with new goal locations,
such as in large-scale automated warehouses. We propose a new framework
Rolling-Horizon Collision Resolution (RHCR) for solving lifelong MAPF by
decomposing the problem into a sequence of Windowed MAPF instances, where a
Windowed MAPF solver resolves collisions among the paths of the agents only
within a bounded time horizon and ignores collisions beyond it. RHCR is
particularly well suited to generating pliable plans that adapt to continually
arriving new goal locations. We empirically evaluate RHCR with a variety of
MAPF solvers and show that it can produce high-quality solutions for up to
1,000 agents (= 38.9\% of the empty cells on the map) for simulated warehouse
instances, significantly outperforming existing work.Comment: Published at AAAI 202
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