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  2. Motion planning - Wikipedia

    en.wikipedia.org/wiki/Motion_planning

    The performance of a probabilistically complete planner is measured by the rate of convergence. For practical applications, one usually uses this property, since it allows setting up the time-out for the watchdog based on an average convergence time. Incomplete planners do not always produce a feasible path when one exists (see first paragraph ...

  3. TensorFlow - Wikipedia

    en.wikipedia.org/wiki/TensorFlow

    TensorFlow includes an “eager execution” mode, which means that operations are evaluated immediately as opposed to being added to a computational graph which is executed later. [35] Code executed eagerly can be examined step-by step-through a debugger, since data is augmented at each line of code rather than later in a computational graph. [35]

  4. Pathfinding - Wikipedia

    en.wikipedia.org/wiki/Pathfinding

    Quadtrees can be used for hierarchical path finding The idea was first described by the video game industry , which had a need for planning in large maps with a low amount of CPU time . The concept of using abstraction and heuristics is older and was first mentioned under the name ABSTRIPS (Abstraction-Based STRIPS ) [ 7 ] which was used to ...

  5. Wavefront expansion algorithm - Wikipedia

    en.wikipedia.org/wiki/Wavefront_expansion_algorithm

    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]

  6. Real-time path planning - Wikipedia

    en.wikipedia.org/wiki/Real-time_path_planning

    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.

  7. A* search algorithm - Wikipedia

    en.wikipedia.org/wiki/A*_search_algorithm

    Keys: current:= cameFrom [current] total_path. prepend (current) return total_path // A* finds a path from start to goal. // h is the heuristic function. h(n) estimates the cost to reach goal from node n. function A_Star (start, goal, h) // The set of discovered nodes that may need to be (re-)expanded. // Initially, only the start node is known.

  8. 8 Certifications That Can Boost Your Tech-Based Side Gig - AOL

    www.aol.com/finance/8-certifications-boost-tech...

    The gig economy runs so strong, some people make livable incomes working part time. The average side gig brings in about $688 per month, according to Self Financial, but the potential for more ...

  9. Simultaneous perturbation stochastic approximation - Wikipedia

    en.wikipedia.org/wiki/Simultaneous_perturbation...

    SPSA can also be used to efficiently estimate the Hessian matrix of the loss function based on either noisy loss measurements or noisy gradient measurements (stochastic gradients). As with the basic SPSA method, only a small fixed number of loss measurements or gradient measurements are needed at each iteration, regardless of the problem ...