Highly Autonomous UAV Mission Planning and Piloting for Civilian Airspace Operations

The last decade has seen a rapid increase in the development and deployment of Unmanned Airborne Vehicles (UAVs). Previous UAVs have been capable of useful missions with a limited degree of on-board intelligence. However, more on-board intelligence is required to fully exploit the potential of UAVs. The objective of this research is to increase the on-board intelligence in two areas: mission planning; and mission piloting. Thereby improving the integration of a UAV into civilian airspace and reducing operator workload. This paper presents the research towards the development of the Intelligent Mission Planner and Pilot. The IMPP enables a UAV to autonomously plan and to perform missions within civilian airspace. The IMPP employs a novel multidisciplinary approach, exploiting robotics, 3D graphics and computer science techniques. Results are presented based upon testing using real world data from south-east Queensland. These results demonstrate the performance achieved by the mission planning and piloting algorithms.

[1]  Yoshifumi Kitamura,et al.  3-D path planning in a dynamic environment using an octree and an artificial potential field , 1995, Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots.

[2]  Jozef Vörös,et al.  Low-cost implementation of distance maps for path planning using matrix quadtrees and octrees , 2001 .

[3]  Emily M. Craparo,et al.  Natural Language Processing in the Control of Unmanned Aerial Vehicles , 2004 .

[4]  Ramesh C. Jain,et al.  Building an environment model using depth information , 1989, Computer.

[5]  Scott A. Bortoff,et al.  Path planning for UAVs , 2000, Proceedings of the 2000 American Control Conference. ACC (IEEE Cat. No.00CH36334).

[6]  Rafael Jesús Segura,et al.  Algorithms to Test Ray-Triangle Intersection. Comparative Study , 2001, WSCG.

[7]  Ji-Hwei Horng,et al.  Vehicle path planning by using adaptive constrained distance transformation , 2002, Pattern Recognit..

[8]  Jonathan P. How,et al.  Implementation of a Manned Vehicle - UAV Mission System , 2004 .

[9]  Tulika Mitra,et al.  An FPGA implementation of triangle mesh decompression , 2002, Proceedings. 10th Annual IEEE Symposium on Field-Programmable Custom Computing Machines.

[10]  Daniel Thalmann,et al.  PII: S0925-7721(99)00046-2 , 2000 .

[11]  Timothy W. McLain,et al.  Coordinated target assignment and intercept for unmanned air vehicles , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[12]  D. Evans,et al.  The evolution of the Global Hawk and MALD avionics systems , 1999, Gateway to the New Millennium. 18th Digital Avionics Systems Conference. Proceedings (Cat. No.99CH37033).