Path planning and controls are fundamental building blocks of autonomy. Together, the two concepts enable a robotic system to formulate a path from point A to point B and then generate motor commands to execute travel along that path.
Head of Autonomy Peter Lommel explains how state estimation systems have changed over the last few decades and what makes state estimation different in GPS-denied environments.
Shield AI’s Head of Autonomy Peter Lommel discusses state estimation’s inherent challenges. Specifically, the challenge of fusing together a number of imperfect, incomplete and indirect sources of information to determine a best estimate.
Reliable state estimation is a fundamental building block of autonomy. Exploration, path planning and controls all depend on the robot’s ability to reliably establish where it is, how it is moving, and how it is oriented.
Shield AI is deeply invested in simulation and emulation. Shield AI’s Director of User Services & Experience Ali Momeni on the important role both play in autonomous robotics.
Shield AI’s co-founder Andrew Reiter discusses some of the key challenges of autonomy and what makes autonomy in GPS-denied environments different.
Shield AI’s Co-Founder and Technical Fellow on why he become interested in robotics and what’s exciting about building an autonomous system from the ground up.
State estimation and mapping are among the cornerstones of mobile autonomy. Knowing where the robot is and understanding its surroundings are fundamental requirements.
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