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            "note": "<h1>RRT - Rapidly-Exploring Random Trees</h1>\n<p>(pp. 233-)</p>\n<p>Let <em>T</em> be on of the trees rooted at <em>q<sub>init</sub></em> and <em>q<sub>goal</sub></em>.</p>\n<p>At each iteration, a random configuration q_rand is sampled uniformly in Q_free. The nearest configuration to q_rand in T, q_near, is found and an attempt is made to progress from q_near towards q_rand. Usually, this entails moving q_near a distance step_size in the straight line define by q_near and q_free. The newly generated config, q_new, if it is collision-free, is added to T, as well as the edge (q_near, q_new).</p>",
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            "note": "<h1>Quasi-Random sampling</h1>\n<p>(pg 219 e seguintes)</p>\n<p>QRS aim to optimize various properties of the distribution of samples.</p>\n<p>Let <em>P</em> be a set of point samples on some space <em>X</em>, and <em>N</em>=#<em>P</em>.</p>\n<h4>Discrepancy:</h4>\n<p>how uniformly the points in <em>P</em> cover <em>X</em></p>\n<h4>Dispersion:</h4>\n<p>how uniformly points are distributed over the space <em>X</em>; provides a measure of the largest portion of X that contains no points of <em>P</em>.</p>\n<p><em>Van de Corput sequence</em>: can only be used to sample the real line.</p>\n<p><em>Halton sequence</em>: generalizes the Van der Corput sequence to <em>d</em> dimensions.</p>\n<p>When <em>N</em> is specified, a <em>Hammersley </em>sequence achieves the best possible asymptotic discrepancy.</p>",
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            "note": "<h1>PRM - Probabilistic Road Maps</h1>\n<p>(pp 202--...)</p>\n<p>PRM divides planning in two phases: learning phase, during which a roadmap in <em>Q<sub>free</sub></em> is built; and the query phase, during which user-defined query configurations are connected with the precomputed roadmap. The nodes of the roadmap are configurations in <em>Q<sub>free</sub></em> and the edges of the roadmap correspond to free paths computed by a local planner.</p>\n<p>The basic PRM algorithm first constructs a roadmap in a probabilistic way for a given workspace.</p>\n<p>Initially, the graph <em>G = ( V , E ) </em>is empty. The, repeatedly, a configuration is sampled from <em>Q</em>. if the configuration is collision-free, it is added to the roadmap. The process is repeated until <em>N</em> collision-free configurations have been sampled. For every node <em>q ∈ V </em>a set <em>N<sub>q</sub></em> of <em>k</em> closest neighbours to the configuration <em>q</em> is chosen from <em>V</em>. The local planner is called to connect <em>q </em>to each node <em>q' ∈ N<sub>q</sub></em>. Whenever successful in computing a feasible path between <em>q</em> and <em>q'</em>, the edge <em>( q,q' )</em> is added to the roadmap. <em><br /></em></p>",
            "tags": [
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            "title": "Principles of robot motion : theory, algorithms, and implementation",
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                    "firstName": "Howie",
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                    "creatorType": "author",
                    "firstName": "Lydia E.",
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                    "firstName": "Sebastian",
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            "shortTitle": "Principles of robot motion",
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            "tags": [
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            "note": "<h1>Single-Query vs Multiple-Query Planners</h1>\n<p>PRM was originally presented as a multiple-query planner: the goal was to create a roadmap that captures the connectivity of Q<sub>free</sub> and then answer multiple user-defined queries very fast. In manyu planning instances, the answer to a single query is of interest and these instances are best served by single-query planners. Single-query planners attempt to solve a query as fast as possible and do not focus on the exploration of the entire Q<sub>free</sub>. (pp 227)</p>",
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            "note": "<h1>Kinodynamic motion planning</h1>\n<p>Cap 10, 11, 12</p>",
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            "note": "<h1>EST - Expansive-Spaces Trees</h1>\n<p>ESTs were initially developed as an efficient single-query planner that covers the space between <em>q<sub>init</sub></em> and <em>q<sub>goal</sub></em> rapidly. EST was initially geared toward kinodynamic problems.&nbsp;</p>\n<p>Let <em>T</em> be on of the trees rooted at <em>q<sub>init</sub></em> and <em>q<sub>goal</sub></em>. The planner first selects a configuration <em>q</em> in <em>T</em> from which to grow <em>T</em> and then samples a random configuration, <em>q_rand</em> from a uniform distribution in the neighbourhood of <em>q</em>. Configuration <em>q</em> is selected at random with probability <em>π(q)</em>.</p>\n<p>The local planner attemps a connection between <em>q</em> and <em>q_rand</em>. If sucessful, <em>q<sub>rand</sub></em> is added to the vertices of <em>T</em> and <em>(q,q<sub>rand</sub>)</em> is added to the edges of <em>T</em>. A new configuration is added only if the planner succeeds in connecting it to an existing configuration in <em>T</em>.</p>\n<p><em>π(q)</em> is biased towards configurations of T that are not dense.</p>\n<p>(pp- 230-)</p>",
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