Autonomous Guidance Navigation and Control Michael Gillham University of Kent SYSIASS Meeting...
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Transcript of Autonomous Guidance Navigation and Control Michael Gillham University of Kent SYSIASS Meeting...
![Page 1: Autonomous Guidance Navigation and Control Michael Gillham University of Kent SYSIASS Meeting University of Essex 21.04.11.](https://reader035.fdocuments.us/reader035/viewer/2022062719/56649eec5503460f94bfd7f4/html5/thumbnails/1.jpg)
Autonomous Guidance Navigation and Control
Michael GillhamUniversity of Kent
SYSIASS Meeting University of Essex 21.04.11
![Page 2: Autonomous Guidance Navigation and Control Michael Gillham University of Kent SYSIASS Meeting University of Essex 21.04.11.](https://reader035.fdocuments.us/reader035/viewer/2022062719/56649eec5503460f94bfd7f4/html5/thumbnails/2.jpg)
Problems• Localisation and global goals.
• Local Minima and obstacle avoidance.• Trajectory following smoothness.• Sensor uncertainties or failure.• Control robustness and safety
criticality.
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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.
![Page 4: Autonomous Guidance Navigation and Control Michael Gillham University of Kent SYSIASS Meeting University of Essex 21.04.11.](https://reader035.fdocuments.us/reader035/viewer/2022062719/56649eec5503460f94bfd7f4/html5/thumbnails/4.jpg)
Sensing for localisation and control
LIDAR: Accurate ranging to obstacles and targetsStereo 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.
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Path Planning
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Dynamic obstacle avoidance
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Higher level and lower level control
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Looking ahead
• Feedback and feed-forward• Virtual vehicle method• Look ahead point
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Control• Weightless neural networks.
• Modularisation.• Sliding mode control.
• Alternate trajectories and bifurcation points.• Real-time dynamic and static collision
avoidance.• look-ahead prediction.
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Hybrid Adaptive Intelligent Control
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PlatformProcessor: 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