Complexity is a dominant, multi-dimensional attribute of the battlespace, and is evident in the geography, manmade
infrastructure, force asymmetry and organizational processes. The Unmanned Aerial Vehicle represents a strategic
enabler for military operations in complex environments by providing a flexible means of acquiring real-time
information and deriving actionable knowledge. Limitations arising from remotely piloted UAV operation together with
the desired operational flexibility in complex environments both dictate the need for increasingly autonomous UAV
operation within a rigorous airspace integration framework. UAV autonomy relies primarily on access to missioncritical
information from on-board sensors and networked datalink, together with comprehensive, efficient and robust
algorithms for decisions on course of action. Global battlefield networking extends the notion of individual vehicle
operation to a coordinated team, whose members carry out complementary and/or redundant tasks. DRDC research on
cooperative teaming of UAVs covers in particular the development and implementation of cooperative control based on
model predictive control. In the context of operations in complex environments, the present paper discusses the selected
approach to cooperative control, and presents applications to formation flight, collision avoidance, real-time
implementation and multi-processing, and fault-detection, isolation and recovery.
Proc. SPIE. 5422, Unmanned Ground Vehicle Technology VI
KEYWORDS: Defense and security, Unmanned aerial vehicles, Sensors, Robotics, Control systems, Artificial intelligence, Intelligence systems, Algorithm development, Systems modeling, Decision support systems
The Defence Research and Development Canada's (DRDC has been given strategic direction to pursue research to increase the independence and effectiveness of military vehicles and systems. This has led to the creation of the Autonomous Intelligent Systems (AIS) prgram and is notionally divide into air, land and marine vehicle systems as well as command, control and decision support systems. This paper presents an overarching description of AIS research issues, challenges and directions as well as a nominal path that vehicle intelligence will take. The AIS program requires a very close coordination between research and implementation on real vehicles. This paper briefly discusses the symbiotic relationship between intelligence algorithms and implementation mechanisms. Also presented are representative work from two vehicle specific research program programs. Work from the Autonomous Air Systems program discusses the development of effective cooperate control for multiple air vehicle. The Autonomous Land Systems program discusses its developments in platform and ground vehicle intelligence.