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Real-Time Path Planning is a term used in robotics that consists of motion planning methods that can adapt to real time changes in the environment. This includes everything from primitive algorithms that stop a robot when it approaches an obstacle to more complex algorithms that continuously takes in information from the surroundings and creates a plan to avoid obstacles.
A reference model architecture is a canonical form, not a system design specification. The RCS reference model architecture combines real-time motion planning and control with high level task planning, problem solving, world modeling, recursive state estimation, tactile and visual image processing, and acoustic signature analysis.
Motion planning algorithms might address robots with a larger number of joints (e.g., industrial manipulators), more complex tasks (e.g. manipulation of objects), different constraints (e.g., a car that can only drive forward), and uncertainty (e.g. imperfect models of the environment or robot).
Nvidia Drive is a computer platform by Nvidia, aimed at providing autonomous car and driver assistance functionality powered by deep learning. [1] [2] The platform was introduced at the Consumer Electronics Show (CES) in Las Vegas in January 2015. [3]
AirSim (Aerial Informatics and Robotics Simulation) is an open-source, cross platform simulator for drones, ground vehicles such as cars and various other objects, built on Epic Games’ proprietary Unreal Engine 4 as a platform for AI research. [2]
Momenta does not build cars itself, but sells their car software to automakers. The company works on deep learning capacities, the so-called "brains", of cars. Momenta's software is fed with large amounts of data which are needed in particular for the development of self-driving cars, so that they can achieve end-to-end intelligent driving.
Path planning is realized with propagating wavefronts. The wavefront expansion algorithm is a specialized potential field path planner with breadth-first search to avoid local minima. [1] [2] It uses a growing circle around the robot. The nearest neighbors are analyzed first and then the radius of the circle is extended to distant regions. [3]
Navlab is a series of autonomous and semi-autonomous vehicles developed by teams from The Robotics Institute at the School of Computer Science, Carnegie Mellon University. Later models were produced under a new department created specifically for the research called "The Carnegie Mellon University Navigation Laboratory". [ 1 ]