Scope and Motivations
In a world with limited resources and increasing complexity, optimisation and computational intelligence are becoming necessary tools for design and analysis of complex systems. With increases in computational efficiency, optimisation can now solve many previously unsolvable problems, while computational intelligence can offer new solutions to effectively make complexity manageable. This is particularly true in the fields of aerospace where complex systems need to operate often in harsh, inhospitable environments with high level of reliability, or where large amount of data need to be processed in real-time for monitoring operations. Additionally, these systems have a high degree of uncertainty on the operating conditions and environment requiring robust system designs.
In space and aerospace sciences, many applications require the solution of global single or multi-objective optimisation problems, including mixed variables, multi-modal and non-differentiable quantities. In astronomy, telescopes and detectors are advancing generating large volumes of data that need to be analysed. From global trajectory optimisation to multidisciplinary aircraft and spacecraft design, from planning and scheduling for autonomous vehicles to the synthesis of robust controllers for airplanes, unmanned aerial vehicles and satellites, from fault detection in space system to image processing of science or Earth observation missions, computational intelligence techniques have become an important – and in many cases an essential – tool for tackling these kinds of problems, providing useful and often non-intuitive solutions. Not only has work in aerospace applications paved the way for the ubiquitous application of computational intelligence, but moreover, they have also led to the development of new and refined approaches and methods.
In the last two decades, evolutionary computing, fuzzy logic, bio-inspired computing, artificial neural networks, swarm intelligence, learning algorithms and other computational intelligence techniques have been used to find optimal trajectories, design optimal constellations or formations, evolve hardware, design robust and optimal aerospace systems (e.g. reusable launch vehicles, re-entry vehicles), evolve scheduled plans for unmanned aerial vehicles, improve aerodynamic design (e.g. airfoil and vehicle shape), optimise structural design, improve the control of aerospace vehicles, regulate air traffic, mine massive amounts of astronomical data, optimise scheduling between different resources (e.g., ground stations, ground- and space-based telescopes), classify constellations, etc.
This special session intends to collect many diverse efforts made in the application of computational intelligence techniques, and related methods, to aerospace problems. The session seeks to bring together researchers from around the globe for presentations and discussions on recent advances in computational intelligence techniques and their application and success in the solution of space and aerospace problems. The session focuses on CI techniques applied to systems operating in air and/or space, collecting experts from astronomy, space sciences, mechanical, aerospace and electrical engineering, as well as computer science, mathematics and more diverse disciplines.
In particular, the list of topics address methods specifically devised, adapted or tailored to address problems in space and aerospace, methods that have been demonstrated to be particularly effective at solving aerospace related problems and application-focused results stemming from the successful and innovate application of CI techniques.
Authors are invited to submit papers topics relating to CI methods or applications in aerospace, including but not limited to:
- Global trajectory optimisation
- Multidisciplinary design for space missions
- Formation and constellation design and control
- Optimal control of aircraft, UAVs, spacecraft or rovers
- Planning and scheduling for autonomous systems in space
- Multi-, many-objective optimisation for space applications
- Resource allocation and programmatics
- Evolutionary computation for concurrent engineering
- Knowledge-based system engineering
- Distributed global optimisation
- Mission planning and control
- Robust mission design under uncertainties
- Decision making strategies for large scale sequential decision problems
- Intelligent search and optimisation methods in aerospace applications
- Guidance, navigation and control for aerospace vehicles
- Autonomous exploration of interplanetary and planetary environments
- Implications of emerging AI fields such as Artificial Life or Swarm Intelligence on aerospace research
- Intelligent algorithms for fault identification, diagnosis and repair of aerospace systems
- Multi-agent systems approach and bio-inspired solutions for system design and control
- Advances in machine learning for aerospace applications
- Intelligent interfaces for human-machine interaction
- Knowledge discovery, data mining and presentation of large data sets
- Data mining and machine learning in astronomy and earth observation
Manuscripts should be prepared according to the standard format of regular papers specified in the conference website and submitted online on the Easychair system. Papers submitted for these session will be peer-reviewed with the same criteria used for other contributed papers. All accepted papers in the special sessions will be included in the published conference proceedings.
Conference submission guidelines
CI-Aero SSCI-2016 Call for Papers.pdf