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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 basic motion planning problem is to compute a continuous path that connects a start configuration S and a goal configuration G, while avoiding collision with known obstacles. The robot and obstacle geometry is described in a 2D or 3D workspace , while the motion is represented as a path in (possibly higher-dimensional) configuration space .
A self-driving Uber car accident in 2018 is an example of autonomous vehicle accidents that are also listed among self-driving car fatalities. A report made by the National Transportation Safety Board (NTSB) showed that the self-driving Uber car was unable to identify the victim in a sufficient amount of time for the vehicle to slow down and ...
These techniques for kinodynamic planning have been shown to work well in practice. However, none of these heuristic techniques can guarantee the optimality of the computed solution (i.e., they have no performance guarantees), and none can be mathematically proven to be faster than the original PTAS algorithms (i.e., none have a provably lower ...
The space shuttle crew leverages a hand held device to feed information into rendezvous planning. The primary limitation on this device is that it requires a line of sight between the target and the designator. Terrain contour matching . Uses a ground scanning radar to "match" topography against digital map data to fix current position.
Part of a series on Self-driving cars & self-driving vehicles Enablers Assured clear distance ahead Autonomous racing Datasets Lane centering Pedestrian crash avoidance mitigation Vehicle infrastructure integration Topics Automatic parking History Impact Platoon Regulation Liability Robotaxi Self-driving truck Tunnel problem Related topics Automatic train operation Unmanned surface vehicle ...
Automated planning and scheduling, sometimes denoted as simply AI planning, [1] is a branch of artificial intelligence that concerns the realization of strategies or action sequences, typically for execution by intelligent agents, autonomous robots and unmanned vehicles.
ADAS use automated technology, such as sensors and cameras, to detect nearby obstacles or driver errors, and respond accordingly. ADAS can enable various levels of autonomous driving. As most road crashes occur due to human error, [2] ADAS are developed to automate, adapt, and enhance vehicle technology for safety and better driving.