Weighted strategies to guide a multi-objective evolutionary algorithm for multi-UAV mission planning

Unmanned Aerial Vehicles (UAVs) are gaining preference for mapping and monitoring ground activities, partially due to the cost efficiency and availability of lightweight high-resolution imaging sensors. Recent advances in solar-powered High Altitude Pseudo-Satellites (HAPSs) widen the future use of multiple UAVs of this sort for long-endurance remote sensing, from the lower stratosphere of vast ground areas. However, to increase mission success and safety, the effect of the wind on the platform dynamics and of the cloud coverage on the quality of the images must be considered during mission planning. For this reason, this article presents a new planner that, considering the weather conditions, determines the temporal hierarchical decomposition of the tasks of several HAPSs. This planner is supported by a Multiple Objective Evolutionary Algorithm (MOEA) that determines the best Pareto front of feasible high-level plans according to different objectives carefully defined to consider the uncertainties imposed by the time-varying conditions of the environment. Meanwhile, the feasibility of the plans is assured by integrating constraints handling techniques in the MOEA. Leveraging historical weather data and realistic mission settings, we analyze the performance of the planner for different scenarios and conclude that it is capable of determining overall good solutions under different conditions.

Section: Related Workmentioning

confidence: 99%

Hierarchical Mission Planning with a GA-Optimizer for Unmanned High Altitude Pseudo-Satellites

Kiam

Besada-Portas

Schulte

2021

“…These constraints include temporal constraints implying the start and end times of tasks, path constraints assuring that vehicles avoid NFZs in their paths, coverage constraints assuring that the UAV is inside the range of the GCS controlling it, LOS is maintained, etc. More information about this problem is presented in [ 5 ].…”

Section: Automated Mission Planner and Dssmentioning

confidence: 99%

“…Currently, UAVs are controlled remotely by human operators using rudimentary planning systems, such as pre-configured plans, classical planners that are not able to cope with the entire complexity of the problem or manually provided schedules. Some recent works [ 4 , 5 ] have provided more efficient approaches to solve the Multi-UAV Cooperative Mission Planning Problem (MCMPP) considering several features of the problem such as time constraints, fuel constraints, sensor constraints, etc. Due to its complexity and multiple conflicting criteria (e.g., makespan, cost or risk of the mission), multi-objective solvers such as Multi-Objective Evolutionary Algorithm (MOEAs) have been used in these works.…”

Section: Introductionmentioning

confidence: 99%

Extending QGroundControl for Automated Mission Planning of UAVs

Ramírez-Atencia

Camacho

2018

Self Cite

Unmanned Aerial Vehicle (UAVs) have become very popular in the last decade due to some advantages such as strong terrain adaptation, low cost, zero casualties, and so on. One of the most interesting advances in this field is the automation of mission planning (task allocation) and real-time replanning, which are highly useful to increase the autonomy of the vehicle and reduce the operator workload. These automated mission planning and replanning systems require a Human Computer Interface (HCI) that facilitates the visualization and selection of plans that will be executed by the vehicles. In addition, most missions should be assessed before their real-life execution. This paper extends QGroundControl, an open-source simulation environment for flight control of multiple vehicles, by adding a mission designer that permits the operator to build complex missions with tasks and other scenario items; an interface for automated mission planning and replanning, which works as a test bed for different algorithms, and a Decision Support System (DSS) that helps the operator in the selection of the plan. In this work, a complete guide of these systems and some practical use cases are provided.

“…This corresponds to military applications and usually assumes a cooperative swarm of UAVs collaborating for the mission achievement. The decision model might be Markov Decision Process oriented [11] or multi-objective evolutionary algorithms based [12,13]. UAV health oriented approaches will tend to avoid physical damage or loss of the drone.…”

Section: Related Workmentioning

confidence: 99%

Context/Resource-Aware Mission Planning Based on BNs and Concurrent MDPs for Autonomous UAVs

Hireche

Dezan

Mocanu

et al. 2018

This paper presents a scalable approach to model uncertainties within a UAV (Unmanned Aerial Vehicle) embedded mission manager. It proposes a concurrent version of BFM models, which are Bayesian Networks built from FMEA (Failure Mode and Effects Analysis) and used by MDPs (Markov Decision Processes). The models can separately handle different applications during the mission; they consider the context of the mission including external constraints (luminosity, climate, etc.), the health of the UAV (Energy, Sensor) as well as the computing resource availability including CPU (Central Processing Unit) load, FPGA (Field Programmable Gate Array) use and timing performances. The proposed solution integrates the constraints into a mission specification by means of FMEA tables in order to facilitate their specifications by non-experts. Decision-making processes are elaborated following a “just enough” quality management by automatically providing adequate implementation of the embedded applications in order to achieve the mission goals, in the context given by the sensors and the on-board monitors. We illustrate the concurrent BFM approach with a case study of a typical tracking UAV mission. This case also considers a FPGA-SoC (FPGA-System on Chip) platform into consideration and demonstrates the benefits to tune the quality of the embedded applications according to the environmental context.