Special issue on “Bio-inspired computing for autonomous vehicles”

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International Journal of Intelligent Computing and Cybernetics

ISSN: 1756-378X

Article publication date: 17 August 2012

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Citation

Gao, Y., Peters, J. and Tsourdos, A. (2012), "Special issue on “Bio-inspired computing for autonomous vehicles”", International Journal of Intelligent Computing and Cybernetics, Vol. 5 No. 3. https://doi.org/10.1108/ijicc.2012.39805caa.001

Publisher

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Emerald Group Publishing Limited

Copyright © 2012, Emerald Group Publishing Limited


Special issue on “Bio-inspired computing for autonomous vehicles”

Article Type: Guest editorial From: International Journal of Intelligent Computing and Cybernetics, Volume 5, Issue 3

Autonomous aerial and ground vehicles are intelligent robotic systems that can assist or replace humans in hostile and uncertain environments. They also offer a wide range of possible applications in defense, civil search and rescue, environmental modeling and space exploration. Autonomy of such a vehicle requires a form of basic intelligence. Bio-inspired computing represents a modern approach for generating intelligent systems that focuses on improving control robustness, adaptability, and the emergent organization of the machine. This special issue aims at exhibiting the latest research achievement, findings and ideas in autonomous vehicles that benefit from bio-inspired algorithms and methods.

The special issue aims to provide the readers a diverse collection of methods addressing various aspects of autonomous vehicles, rather than focus on one particular approach or problem area. The eight papers selected for this special issue provide original designs, ideas and some involve in-depth analytical findings. Contributions include work on the following techniques:

  • 2D splinegon for collective terrain mapping for co-operative UAVs;

  • inverse reinforcement learning;

  • emerging swarm traffic;

  • discrete-time based sliding-mode control for robotic manipulators;

  • bio-inspired control of vehicles;

  • robust and adaptive approach to controlling spacecraft re-entry;

  • EMMAE failure detection system; and

  • sliding-mode control for miniature helicopters.

In addition to the diversity in the techniques, the articles in this special issue also represent different autonomous vehicle applications ranging from ground robots and aircrafts to Earth re-entry vehicles. We expect the multi-disciplinary research techniques presented in this special issue would allow the readers to enhance the quality of their own research. Some details of each article are described as follows.

The first paper “Terrain based co-operative UAV mapping of complex obstacles using 2-D splinegon” authored by Lazarus et al. presents a recently proposed algorithm in terrain based co-operative UAV mapping of the unknown complex obstacle in a stationary environment where the complex obstacles are represented as curved in nature. The second paper “A survey of inverse reinforcement learning techniques” by Zhifei and Joo presents an overview over existing techniques that accomplish imitation learning by recovering the teachers cost function. The third paper “Emerging robot swarm traffic” (Penders and Alboul) examines emerging behaviors of the ant swarm traffic and applies the techniques to mobile robots. The fourth paper “Discrete-time based sliding-mode control of robot manipulators” by Majidabad and Shandiz proposes new features in the sliding-mode control technique which work in discrete time domain. The fifth paper “Real-time, decentralized and bio-inspired topology control for holonomic autonomous vehicles” authored by S¸ahin and Uyar presents a new approach to tackle control problems that are over-constrained. The sixth paper “DSC-backstepping based robust adaptive LS-SVM control for near space vehicle’s reentry attitude” by Zhang et al. provides a very involved concerted approaches possibly enabling better re-entry attitude control. The seventh paper “EMMAE failure detection system and failure evaluation over flight performance” by Qiu et al. describes research in the fault detection and isolation (FDI) and evaluation the reduction to performance after failures occurred in the flight control system (FCS) during its mission operation. The final (eighth) paper “Chattering-free sliding mode control with unidirectional auxiliary surfaces for miniature helicopters” (Fu et al.) proposes a chattering-free sliding-mode control scheme with unidirectional auxiliary surfaces (UAS-SMC) for small miniature autonomous helicopters (Trex 250).

The call for papers for this special issue received 15 submissions, and each submission was peer-reviewed by at least two experts in the related field. After the revisions were made according to the feedback, eight manuscripts were accepted to appear in the issue. The Guest Editors would like to thank the authors and the reviewers for their contributions to this special issue. Moreover, we are grateful for the International Journal of Intelligent Computing and Cybernetics for the opportunity to publish and the journal editors for their insightful feedback to this special issue, their support and guidance in the publication.

About the Guest Editors

Dr Yang Gao, B.Eng (1st Hon), PhD, SMIEEE, FHEA, is the Senior Lecturer in Spacecraft Autonomy and heads the AI and Autonomy Group within Surrey Space Centre at University of Surrey in the UK. Dr Gao and her research team specialize in computer vision, machine learning and biomimetics with applications to space robotics and autonomous systems. She is also actively involved in space mission design and promoting the Surrey small-sat approach within missions like ExoMars, MoonLITE, Moonraker, LunarEX/NET, and Marco Polo-R, etc. She is a co-author of one text book on fuzzy neural network techniques, five book chapters, over 70 technical papers in internationally refereed journals and conference proceedings, and an invited session chair, speaker and lecturer at various international meetings and summer schools.

Professor Dr Jan Peters, Dipl.-Ing., Dipl.-Inform., MSc CS, MSc AME, PhD is a Full Professor (W3) at Technische Universität Darmstadt heading the Intelligent Autonomous Systems group at the same time as the Robot Learning Lab at the Max Planck Institute for Intelligent Systems. Between 2007-2011, Jan Peters was a senior research scientist and group leader at the Max Planck Institute for Biological Cybernetics. Jan Peters is a computer scientist (holding a German MSc from FernUni Hagen, an MSc and PhD from University of Southern California), an electrical engineer (receiving a German MSc in EE from TU München), and a mechanical engineer (with a MSc in Mechanical Engineering from USC). Jan has held visiting research positions at ATR, Japan and at National University of Singapore during his graduate studies. Jan Peters’ PhD thesis received the 2007 Dick Volz Best US Robotics PhD Runner-Up Award. Jan Peters’ research interests span a large variety of topics in robotics, machine learning and biomimetic systems with a strong focus on learning of motor skills. Jan Peters has co-founded the IEEE RAS Technical Committee on Robot Learning.

Professor Antonios Tsourdos obtained a BEng on Electronic, Control and Systems Engineering from the University of Sheffield (1995), an MSc on Systems Engineering from Cardiff University (1996) and a PhD on Nonlinear Robust Missile Autopilot Design and Analysis from Cranfield University (1999). He was appointed Head of the Autonomous Systems Group in 2007. Professor Tsourdos was a member of Team Stellar, the winning team for the UK MoD Grand Challenge (2008) and the IET Innovation Award (Category Team, 2009). Antonios is an editorial board member of the Proceedings of the IMechE Part G Journal of Aerospace Engineering, the International Journal of Systems Science and the IEEE Transactions on Instrumentation and Measurement. Professor Tsourdos is a member of the AAD KTN National Technical Committee on Autonomous Systems. Professor Tsourdos is also a member of the IFAC Technical Committee on Aerospace Control, the IFAC Technical Committee on Intelligent Autonomous Vehicles, the AIAA Unmanned Systems Program Committee, and the IEEE Control System Society Technical Committee on Aerospace Control (TCAC). Professor Tsourdos is also a member of IET Robotics & Mechatronics Executive Team. His research interests include guidance, control and navigation of autonomous vehicles, multi-vehicle systems, data and information fusion.

Yang Gao, Jan Peters, Antonios TsourdosGuest Editors

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