Solving Complex Multi-UAV Mission Planning Problems using Multi-objective Genetic Algorithms

• URL: http://arxiv.org/abs/2402.06504v1
• Date: Fri, 9 Feb 2024 16:13:21 GMT
• Title: Solving Complex Multi-UAV Mission Planning Problems using Multi-objective Genetic Algorithms
• Authors: Cristian Ramirez-Atencia, Gema Bello-Orgaz, Maria D R-Moreno, David Camacho
• Abstract summary: This paper presents a new Multi-Objective Genetic Algorithm for solving complex Mission Planning Problems (MPP) A hybrid fitness function has been designed using a Constraint Satisfaction Problem (CSP) to check if solutions are valid. Experimental results show that the new algorithm is able to obtain good solutions, however as the problem becomes more complex, the optimal solutions also become harder to find.
• Score: 4.198865250277024
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to recent booming of UAVs technologies, these are being used in many fields involving complex tasks. Some of them involve a high risk to the vehicle driver, such as fire monitoring and rescue tasks, which make UAVs excellent for avoiding human risks. Mission Planning for UAVs is the process of planning the locations and actions (loading/dropping a load, taking videos/pictures, acquiring information) for the vehicles, typically over a time period. These vehicles are controlled from Ground Control Stations (GCSs) where human operators use rudimentary systems. This paper presents a new Multi-Objective Genetic Algorithm for solving complex Mission Planning Problems (MPP) involving a team of UAVs and a set of GCSs. A hybrid fitness function has been designed using a Constraint Satisfaction Problem (CSP) to check if solutions are valid and Pareto-based measures to look for optimal solutions. The algorithm has been tested on several datasets optimizing different variables of the mission, such as the makespan, the fuel consumption, distance, etc. Experimental results show that the new algorithm is able to obtain good solutions, however as the problem becomes more complex, the optimal solutions also become harder to find.

Related papers

• A Multiagent Path Search Algorithm for Large-Scale Coalition Structure Generation [61.08720171136229]
  Coalition structure generation is a fundamental computational problem in multiagent systems. We develop SALDAE, a multiagent path finding algorithm for CSG that operates on a graph of coalition structures.
  arXiv  Detail & Related papers  (2025-02-14T15:21:27Z)
• Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles [36.3157805511305]
  Unmanned surface vehicles (USVs) offer low-cost, flexible aerial services. Ground stations (GSs) can provide powerful supports, which can cooperate to help the USVs in complex scenarios. We propose a cooperative UAV and GS based robust multi-access edge computing framework to assist USVs in completing computational tasks.
  arXiv  Detail & Related papers  (2025-02-12T04:42:59Z)
• Task Delay and Energy Consumption Minimization for Low-altitude MEC via Evolutionary Multi-objective Deep Reinforcement Learning [52.64813150003228]
  The low-altitude economy (LAE), driven by unmanned aerial vehicles (UAVs) and other aircraft, has revolutionized fields such as transportation, agriculture, and environmental monitoring. In the upcoming six-generation (6G) era, UAV-assisted mobile edge computing (MEC) is particularly crucial in challenging environments such as mountainous or disaster-stricken areas. The task offloading problem is one of the key issues in UAV-assisted MEC, primarily addressing the trade-off between minimizing the task delay and the energy consumption of the UAV.
  arXiv  Detail & Related papers  (2025-01-11T02:32:42Z)
• Genetic Algorithm Based System for Path Planning with Unmanned Aerial Vehicles Swarms in Cell-Grid Environments [42.72938925647165]
  Path Planning methods for autonomously controlling swarms of unmanned aerial vehicles (UAVs) are gaining momentum. An increasing number of scenarios now require autonomous control of multiple UAVs, as autonomous operation can significantly reduce labor costs. Many of these scenarios, however, involve obstacles such as power lines and trees, which complicate Path Planning. This paper presents an evolutionary-based system employing genetic algorithms to address this problem in environments with obstacles.
  arXiv  Detail & Related papers  (2024-12-04T16:24:41Z)
• Robotic warehousing operations: a learn-then-optimize approach to large-scale neighborhood search [84.39855372157616]
  This paper supports robotic parts-to-picker operations in warehousing by optimizing order-workstation assignments, item-pod assignments and the schedule of order fulfillment at workstations. We solve it via large-scale neighborhood search, with a novel learn-then-optimize approach to subproblem generation. In collaboration with Amazon Robotics, we show that our model and algorithm generate much stronger solutions for practical problems than state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-08-29T20:22:22Z)
• UAV-enabled Collaborative Beamforming via Multi-Agent Deep Reinforcement Learning [79.16150966434299]
  We formulate a UAV-enabled collaborative beamforming multi-objective optimization problem (UCBMOP) to maximize the transmission rate of the UVAA and minimize the energy consumption of all UAVs. We use the heterogeneous-agent trust region policy optimization (HATRPO) as the basic framework, and then propose an improved HATRPO algorithm, namely HATRPO-UCB.
  arXiv  Detail & Related papers  (2024-04-11T03:19:22Z)
• Weighted strategies to guide a multi-objective evolutionary algorithm for multi-UAV mission planning [12.97430155510359]
  This work proposes a weighted random generator for the creation and mutation of new individuals. The main objective of this work is to reduce the convergence rate of the MOEA solver for multi-UAV mission planning.
  arXiv  Detail & Related papers  (2024-02-28T23:05:27Z)
• Constrained multi-objective optimization for multi-UAV planning [5.574995936464475]
  In this work, this problem has been solved using a multi-objective evolutionary algorithm combined with a constraint satisfaction problem model. The algorithm has been tested on several missions of increasing complexity, and the computational complexity of the different element considered in the missions has been studied.
  arXiv  Detail & Related papers  (2024-02-09T17:39:02Z)
• A Multi-UAV System for Exploration and Target Finding in Cluttered and GPS-Denied Environments [68.31522961125589]
  We propose a framework for a team of UAVs to cooperatively explore and find a target in complex GPS-denied environments with obstacles. The team of UAVs autonomously navigates, explores, detects, and finds the target in a cluttered environment with a known map. Results indicate that the proposed multi-UAV system has improvements in terms of time-cost, the proportion of search area surveyed, as well as successful rates for search and rescue missions.
  arXiv  Detail & Related papers  (2021-07-19T12:54:04Z)
• Efficient UAV Trajectory-Planning using Economic Reinforcement Learning [65.91405908268662]
  We introduce REPlanner, a novel reinforcement learning algorithm inspired by economic transactions to distribute tasks between UAVs. We formulate the path planning problem as a multi-agent economic game, where agents can cooperate and compete for resources. As the system computes task distributions via UAV cooperation, it is highly resilient to any change in the swarm size.
  arXiv  Detail & Related papers  (2021-03-03T20:54:19Z)
• Three Dimensional Route Planning for Multiple Unmanned Aerial Vehicles using Salp Swarm Algorithm [0.0]
  Route planning is a series of translation and rotational steps from a given start location to the destination goal location. The proposed approach improves the average cost and overall time by 1.25% and 6.035% respectively.
  arXiv  Detail & Related papers  (2019-11-24T12:36:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.