Artificial Neural Network Based Mission Planning Mechanism for Spacecraft

The ability to plan and react fast in dynamic space environments is central to intelligent behavior of spacecraft. For space and robotic applications, many planners have been used. But it is difficult to encode the domain knowledge and directly use existing techniques such as heuristic to improve the performance of the application systems. Therefore, regarding planning as an advanced control problem, this paper first proposes an autonomous mission planning and action selection mechanism through a multiple layer perceptron neural network approach to select actions in planning process and improve efficiency. To prove the availability and effectiveness, we use autonomous mission planning problems of the spacecraft, which is a sophisticated system with complex subsystems and constraints as an example. Simulation results have shown that artificial neural networks (ANNs) are usable for planning problems. Compared with the existing planning method in EUROPA, the mechanism using ANNs is more efficient and can guarantee stable performance. Therefore, the mechanism proposed in this paper is more suitable for planning problems of spacecraft that require real time and stability.

[1]  Ari K. Jonsson,et al.  MAPGEN Planner: Mixed-Initiative Activity Planning for the Mars Exploration Rover Mission , 2003 .

[2]  Akira Abe.  Trajectory planning for flexible Cartesian robot manipulator by using artificial neural network: numerical simulation and experimental verification , 2011, Robotica.

[3]  Youssef Bassil.  Neural Network Model for Path-Planning of Robotic Rover Systems , 2012, ArXiv.

[4]  Simon X. Yang,et al.  Neural-Network-Based Path Planning for a Multirobot System With Moving Obstacles , 2009, IEEE Trans. Syst. Man Cybern. Part C.

[5]  Cui Ping-yua.  State of the Art and Development Trends of On-board Autonomy Technology for Deep Space Explorer , 2014 .

[6]  Guangcheng Ma,et al.  Robust tracking control of space robot via neural network , 2006, 2006 1st International Symposium on Systems and Control in Aerospace and Astronautics.

[7]  Tania Bedrax-Weiss,et al.  EUROPA2: Plan Database Services for Planning and Scheduling Applications , 2004 .

[8]  Marco Roveri,et al.  Timelines with Temporal Uncertainty , 2013, AAAI.

[9]  Guangcheng Ma,et al.  Robust tracking control of space robots using fuzzy neural network , 2005, 2005 International Symposium on Computational Intelligence in Robotics and Automation.

[10]  Paolo Traverso,et al.  Automated planning - theory and practice , 2004 .

[11]  Kevin N. Gurney,et al.  An introduction to neural networks , 2018 .

[12]  Malik Ghallab,et al.  IxTeT: an integrated approach for plan generation and scheduling , 1995, Proceedings 1995 INRIA/IEEE Symposium on Emerging Technologies and Factory Automation. ETFA'95.

[13]  Blai Bonet,et al.  A Robust and Fast Action Selection Mechanism for Planning , 1997, AAAI/IAAI.

[14]  Alex Fukunaga,et al.  ASPEN: A Framework for Automated Planning and Scheduling of Spacecraft Control and Operations , 1997 .

[15]  Syed Usman Ahmed,et al.  Guided Autowave Pulse Coupled Neural Network (GAPCNN) based real time path planning and an obstacle avoidance scheme for mobile robots , 2014, Robotics Auton. Syst..

[16]  B. Yegnanarayana,et al.  Artificial Neural Networks , 2004 .

[17]  Hans Corsten,et al.  Artificial neural networks for supporting production planning and control , 1996 .

[18]  R. Sreerama Kumar,et al.  Intelligent task planning and action selection of a mobile robot in a multi-agent system through a fuzzy neural network approach , 2007, Eng. Appl. Artif. Intell..

[19]  B. V. K. Vijaya Kumar,et al.  The wave expansion neural network , 1997, Neurocomputing.

[20]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[21]  Micah Clark,et al.  Validation and verification of the remote agent for spacecraft autonomy , 1999, 1999 IEEE Aerospace Conference. Proceedings (Cat. No.99TH8403).

[22]  Xie Jianhong.  Parallel Bionic Algorithm Used to Optimize Sensor Placement for Self-diagnostic Smart Structures , 2008, 2008 International Conference on Computer Science and Information Technology.

[23]  Richard Stottler,et al.  Incorporating High-Speed, Optimizing Scheduling into NASA's EUROPA Planning Architecture , 2012, Infotech@Aerospace.

[24]  Oskar von Stryk,et al.  Design Concepts for a new Temporal Planning Paradigm , 2012 .

[25]  Ari K. Jónsson,et al.  MAPGEN: Mixed-Initiative Planning and Scheduling for the Mars Exploration Rover Mission , 2004, IEEE Intell. Syst..

[26]  Xu Rui,et al.  Timeline based autonomous mission planning system for deep space exploration , 2004 .

[27]  Tao Mei,et al.  Motion Planning for Autonomous Vehicle Based on Radial Basis Function Neural Network in Unstructured Environment , 2014, Sensors.

[28]  Stan C. A. M. Gielen,et al.  Neural Network Dynamics for Path Planning and Obstacle Avoidance , 1995, Neural Networks.

[29]  Ching-Chin Chern,et al.  A heuristic relief transportation planning algorithm for emergency supply chain management , 2010, Int. J. Comput. Math..

[30]  Max Q.-H. Meng,et al.  An efficient neural network approach to dynamic robot motion planning , 2000, Neural Networks.

[31]  J. Dayhoff,et al.  Artificial neural networks , 2001, Cancer.