Autonomous Guidance Navigation and Control Michael Gillham University of Kent SYSIASS Meeting University of Essex

Problems Localisation and global goals. Local Minima and obstacle avoidance. Trajectory following smoothness. Sensor uncertainties or failure. Control robustness and safety criticality.

Research Integration of higher level control with lower level incorporating learning and deliberation. Local minima avoidance and goal seeking solutions. Real-time dynamic and static collision avoidance incorporated into the trajectory manifold. Local path planning smoothness from look-ahead prediction. Removal of chatter and instabilities from sliding mode. Weightless neural network real-time feedback dynamic controller.

Sensing for localisation and control LIDAR: Accurate ranging to obstacles and targets Stereo vision: Angle, depth, motion. Sonar: Immediate vicinity obstacles, motion. Magnetic: Simple inertial/body frame of reference. GPS: Localisation and map planning. Gyroscope: MEMS, attitude feedback. Accelerometers: Good feedback for smoothness. Wheel rotation sensor: Traction control.

Path Planning

Dynamic obstacle avoidance

Higher level and lower level control

Looking ahead Feedback and feed-forward Virtual vehicle method Look ahead point

Control Weightless neural networks. Modularisation. Sliding mode control. Alternate trajectories and bifurcation points. Real-time dynamic and static collision avoidance. look-ahead prediction.

Hybrid Adaptive Intelligent Control

Platform Processor: 1000mips 500MHz Analog Devices Blackfin BF537, 32MB SDRAM, 4MB Flash, JTAG SRV-1 Blackfin Camera with 500MHz Analog Devices Blackfin BF537 processor, 32MB SDRAM, 4MB Flash, and OV7725 VGA low-light camera (up to 60fps) with 3.6mm f2.0 lens (90-deg field-of-view) laser pointers for ranging, support for up to 4 Maxbotics ultrasonic ranging modules and various I2C sensors