Hybrid qualitative state plan and mission planning problem with UAVs

• Grant number: 14/11331-0
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: August 01, 2014
• End date: January 31, 2017
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computational Mathematics
• Agreement: Coordination of Improvement of Higher Education Personnel (CAPES)
• Principal Investigator: Claudio Fabiano Motta Toledo
• Grantee: Márcio da Silva Arantes
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Associated research grant: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry, AP.CEPID

Abstract

This paper aims to present a proposal for a thesis to be developed in the Doctoral Program in Computer Science and Computational Mathematics at ICMC / USP. The subject of the thesis seeks to advance the state of the art to solve the problem of mission planning for unmanned aerial vehicles (UAVs), through its representation as a Hybrid Qualitative State Plan (HQSP) Problem. This representation aims to address the uncertainties in a real environment with stochastic models as defined in recent studies. The outlines of this project were developed in cooperation with Professor Brian Charles Williams at the Massachusetts Institute of Technology (MIT), where the HQSP problem was established and whose unresolved aspects like representation and scalability will be addressed in this thesis. Therefore, techniques based on evolutionary computation will be established aiming to achieve greater scalability and better representation of reality in these systems. The solutions will be validated in flight simulators and possible integration into real environment using two fixed wing UAVs. The experiments seek to solve problems in real situations employing UAVs, involving agriculture and the environment that are already conducted by the Embedded and Evolutionary Systems (ESS) group of the ICMC/USP. Some preliminary results will be presented using evolutionary computation, where it has been possible to obtain improvements in the modeling and scalability for the considered problem. (AU)

Scientific publications

(The scientific publications listed on this page originate from the Web of Science or SciELO databases. Their authors have cited FAPESP grant or fellowship project numbers awarded to Principal Investigators or Fellowship Recipients, whether or not they are among the authors. This information is collected automatically and retrieved directly from those bibliometric databases.)

ARANTES, JESIMAR DA SILVA; ARANTES, MARCIO DA SILVA; MOTTA TOLEDO, CLAUDIO FABIAN; TRINDADE JUNIOR, ONOFRE; WILLIAMS, BRIAN C.; ACM. An Embedded System Architecture based on Genetic Algorithms for Mission and Safety Planning with UAV. PROCEEDINGS OF THE 2017 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'17), v. N/A, p. 8-pg., 2017-01-01. (13/26091-2, 14/11331-0, 15/23182-2)

ARANTES, JESIMAR DA SILVA; ARANTES, MARCIO DA SILVA; MISSAGLIA, ANDRE BADAWI; SIMOES, EDUARDO DO VALLE; MOTTA TOLEDO, CLAUDIO FABIAO; IEEE. Evaluating Hardware Platforms and Path Re-Planning Strategies for the UAV Emergency Landing Problem. 2017 IEEE 29TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2017), v. N/A, p. 8-pg., 2017-01-01. (13/07375-0, 14/11331-0, 15/23182-2)

ARANTES, MARCIO DA SILVA; MOTTA TOLEDO, CLAUDIO FABIANO; WILLIAMS, BRIAN CHARLES; ONO, MASAHIRO. Collision-Free Encoding for Chance-Constrained Nonconvex Path Planning. IEEE Transactions on Robotics, v. 35, n. 2, p. 433-448, APR 2019. (13/07375-0, 14/11331-0, 14/12297-0)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

ARANTES, Márcio da Silva. Hybrid qualitative state plan problem and mission planning with UAVs. 2017. Doctoral Thesis - Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) São Carlos.