Drivable Path Planning Using Hybrid Search Algorithm Based on E and Bernstein-Bézier Motion Primitives
This article proposes a hybrid path-planning algorithm, the HE* algorithm, which combines the discrete grid-based E* search and continuous Bernstein-Bézier (BB) motion primitives. Several researchers have addressed the smooth path planning problem and the sample-based integrated path planning techni...
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creator | Klancar, Gregor Seder, Marija Blazic, Saso Skrjanc, Igor Petrovic, Ivan |
description | This article proposes a hybrid path-planning algorithm, the HE* algorithm, which combines the discrete grid-based E* search and continuous Bernstein-Bézier (BB) motion primitives. Several researchers have addressed the smooth path planning problem and the sample-based integrated path planning techniques. We believe that the main benefits of the proposed approach are: directly drivable path, no additional post-optimization tasks, reduced search branching, low computational complexity, and completeness guarantee. Several examples and comparisons with the state-of-the-art planners are provided to illustrate and evaluate the main advantages of the HE* algorithm. HE* yields a collision-safe and smooth path that is close to spatially optimal (the Euclidean shortest path) with a guaranteed continuity of curvature. Therefore, the path is easily drivable for a wheeled robot without any additional post-optimization and smoothing required. HE* is a two-stage algorithm which uses a direction-guiding heuristics computed by the E* search in the first stage, which improves the quality and reduces the complexity of the hybrid search in the second stage. In each iteration, the search is expanded by a set of BBs, the parameters of which adapt continuously according to the guiding heuristics. Completeness is guaranteed by relying on a complete node mechanism, which also provides an upper bound for the calculated path cost. A remarkable feature of HE* is that it produces good results even at coarse resolutions. |
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Several researchers have addressed the smooth path planning problem and the sample-based integrated path planning techniques. We believe that the main benefits of the proposed approach are: directly drivable path, no additional post-optimization tasks, reduced search branching, low computational complexity, and completeness guarantee. Several examples and comparisons with the state-of-the-art planners are provided to illustrate and evaluate the main advantages of the HE* algorithm. HE* yields a collision-safe and smooth path that is close to spatially optimal (the Euclidean shortest path) with a guaranteed continuity of curvature. Therefore, the path is easily drivable for a wheeled robot without any additional post-optimization and smoothing required. HE* is a two-stage algorithm which uses a direction-guiding heuristics computed by the E* search in the first stage, which improves the quality and reduces the complexity of the hybrid search in the second stage. In each iteration, the search is expanded by a set of BBs, the parameters of which adapt continuously according to the guiding heuristics. Completeness is guaranteed by relying on a complete node mechanism, which also provides an upper bound for the calculated path cost. A remarkable feature of HE* is that it produces good results even at coarse resolutions.</description><identifier>ISSN: 2168-2216</identifier><identifier>EISSN: 2168-2232</identifier><identifier>DOI: 10.1109/TSMC.2019.2945110</identifier><identifier>CODEN: ITSMFE</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Algorithms ; Angular velocity ; Bernstein–Bézier (BB) curve ; Collision avoidance ; Completeness ; graph search algorithm ; hybrid planner ; Iterative methods ; Mathematical analysis ; Mobile robots ; motion primitives ; Optimization ; Path planning ; Planning ; Search algorithms ; Shortest-path problems ; Smoothing methods ; Splines (mathematics) ; Task complexity ; Upper bounds</subject><ispartof>IEEE transactions on systems, man, and cybernetics. Systems, 2021-08, Vol.51 (8), p.4868-4882</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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Systems</title><addtitle>TSMC</addtitle><description>This article proposes a hybrid path-planning algorithm, the HE* algorithm, which combines the discrete grid-based E* search and continuous Bernstein-Bézier (BB) motion primitives. Several researchers have addressed the smooth path planning problem and the sample-based integrated path planning techniques. We believe that the main benefits of the proposed approach are: directly drivable path, no additional post-optimization tasks, reduced search branching, low computational complexity, and completeness guarantee. Several examples and comparisons with the state-of-the-art planners are provided to illustrate and evaluate the main advantages of the HE* algorithm. HE* yields a collision-safe and smooth path that is close to spatially optimal (the Euclidean shortest path) with a guaranteed continuity of curvature. Therefore, the path is easily drivable for a wheeled robot without any additional post-optimization and smoothing required. HE* is a two-stage algorithm which uses a direction-guiding heuristics computed by the E* search in the first stage, which improves the quality and reduces the complexity of the hybrid search in the second stage. In each iteration, the search is expanded by a set of BBs, the parameters of which adapt continuously according to the guiding heuristics. Completeness is guaranteed by relying on a complete node mechanism, which also provides an upper bound for the calculated path cost. A remarkable feature of HE* is that it produces good results even at coarse resolutions.</description><subject>Algorithms</subject><subject>Angular velocity</subject><subject>Bernstein–Bézier (BB) curve</subject><subject>Collision avoidance</subject><subject>Completeness</subject><subject>graph search algorithm</subject><subject>hybrid planner</subject><subject>Iterative methods</subject><subject>Mathematical analysis</subject><subject>Mobile robots</subject><subject>motion primitives</subject><subject>Optimization</subject><subject>Path planning</subject><subject>Planning</subject><subject>Search algorithms</subject><subject>Shortest-path problems</subject><subject>Smoothing methods</subject><subject>Splines (mathematics)</subject><subject>Task complexity</subject><subject>Upper bounds</subject><issn>2168-2216</issn><issn>2168-2232</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kM9uwjAMxqNpk4YYDzDtEmnnsvxp0vQIjI1JoCEB5yi0LgSVliUFib3RnmMvtlRMXGzL_vzZ-iH0SEmfUpK-LBezUZ8RmvZZGovQukEdRqWKGOPs9lpTeY963u8IIZQpyYnsoM2rsyezLgHPTbPF89JUla02eOXbODmvnc3xAozLtnhQbmpnm-0eD42HHNcVHmNT5XgIrvIN2Coa_v58W3B4Vjc2jOfO7m1jT-Af0F1hSg-9_9xFq7fxcjSJpp_vH6PBNMpYypsolkyojNBMZVmeFEJSHgspDU9YiDlfU5AiLmgBjHBBwagUeCIMV5LIggreRc8X34Orv47gG72rj64KJzUTIiBgKpFBRS-qzNXeOyj0IXxq3FlTolukukWqW6T6H2nYebrsWAC46lWwUyrmf695cWg</recordid><startdate>20210801</startdate><enddate>20210801</enddate><creator>Klancar, Gregor</creator><creator>Seder, Marija</creator><creator>Blazic, Saso</creator><creator>Skrjanc, Igor</creator><creator>Petrovic, Ivan</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Klancar, Gregor</au><au>Seder, Marija</au><au>Blazic, Saso</au><au>Skrjanc, Igor</au><au>Petrovic, Ivan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Drivable Path Planning Using Hybrid Search Algorithm Based on E and Bernstein-Bézier Motion Primitives</atitle><jtitle>IEEE transactions on systems, man, and cybernetics. Systems</jtitle><stitle>TSMC</stitle><date>2021-08-01</date><risdate>2021</risdate><volume>51</volume><issue>8</issue><spage>4868</spage><epage>4882</epage><pages>4868-4882</pages><issn>2168-2216</issn><eissn>2168-2232</eissn><coden>ITSMFE</coden><abstract>This article proposes a hybrid path-planning algorithm, the HE* algorithm, which combines the discrete grid-based E* search and continuous Bernstein-Bézier (BB) motion primitives. Several researchers have addressed the smooth path planning problem and the sample-based integrated path planning techniques. We believe that the main benefits of the proposed approach are: directly drivable path, no additional post-optimization tasks, reduced search branching, low computational complexity, and completeness guarantee. Several examples and comparisons with the state-of-the-art planners are provided to illustrate and evaluate the main advantages of the HE* algorithm. HE* yields a collision-safe and smooth path that is close to spatially optimal (the Euclidean shortest path) with a guaranteed continuity of curvature. Therefore, the path is easily drivable for a wheeled robot without any additional post-optimization and smoothing required. HE* is a two-stage algorithm which uses a direction-guiding heuristics computed by the E* search in the first stage, which improves the quality and reduces the complexity of the hybrid search in the second stage. In each iteration, the search is expanded by a set of BBs, the parameters of which adapt continuously according to the guiding heuristics. Completeness is guaranteed by relying on a complete node mechanism, which also provides an upper bound for the calculated path cost. A remarkable feature of HE* is that it produces good results even at coarse resolutions.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TSMC.2019.2945110</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0002-1461-3321</orcidid><orcidid>https://orcid.org/0000-0002-9347-8534</orcidid><orcidid>https://orcid.org/0000-0001-9961-5627</orcidid><orcidid>https://orcid.org/0000-0002-1418-255X</orcidid><orcidid>https://orcid.org/0000-0002-0502-5376</orcidid></addata></record> |
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subjects | Algorithms Angular velocity Bernstein–Bézier (BB) curve Collision avoidance Completeness graph search algorithm hybrid planner Iterative methods Mathematical analysis Mobile robots motion primitives Optimization Path planning Planning Search algorithms Shortest-path problems Smoothing methods Splines (mathematics) Task complexity Upper bounds |
title | Drivable Path Planning Using Hybrid Search Algorithm Based on E and Bernstein-Bézier Motion Primitives |
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