A Cross-Entropy Motion Planning Framework for Hybrid Continuum Robots
The sampling-based motion planners, including the Rapidly-exploring Random Trees (RRT) algorithms, are widely utilized in continuum robots, enabling efficient search for feasible motion plans in constrained environments. In surgical robotics, complex mapping among the high-dimensional kinematics of...
Gespeichert in:
| Veröffentlicht in: | IEEE robotics and automation letters 2023-12, Vol.8 (12), p.8200-8207 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 8207 |
|---|---|
| container_issue | 12 |
| container_start_page | 8200 |
| container_title | IEEE robotics and automation letters |
| container_volume | 8 |
| creator | Chen, Jibiao Yan, Junyan Qiu, Yufu Fang, Haiyang Chen, Jianghua Cheng, Shing Shin |
| description | The sampling-based motion planners, including the Rapidly-exploring Random Trees (RRT) algorithms, are widely utilized in continuum robots, enabling efficient search for feasible motion plans in constrained environments. In surgical robotics, complex mapping among the high-dimensional kinematics of continuum robots, trajectory parameterization, and path redundancy may lead to non-optimal motion path, which in turn affects their efficiency and surgical task performance (e.g. path following), and ultimately the patient outcome. In this letter, a cross-entropy (CE) motion planning framework is proposed for continuum robots, wherein the RRT * planner is equipped with a CE estimation method serving as a probabilistic model to sample elite trajectories with optimal computation costs. It can asymptotically optimize the sampling distributions among individuals in terms of either robot states or parameterized trajectories. The presented CE motion planners were implemented on a hybrid continuum robot to enable obstacle avoidance, approximate follow-the-leader (FTL) motion, and navigation in a clinical scenario. They are shown to offer lower sampling cost and higher computational efficiency compared to existing approaches. |
| doi_str_mv | 10.1109/LRA.2023.3325777 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_proquest_journals_2884893277</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>10287401</ieee_id><sourcerecordid>2884893277</sourcerecordid><originalsourceid>FETCH-LOGICAL-c292t-7c6ab6bb32112c0ccd4dc4acbb37d88f4ee4fc24c152bafe32a4a34e676a0f8a3</originalsourceid><addsrcrecordid>eNpNkEFLAzEQhYMoWGrvHjwEPO-aTNJN9liW1goVpeg5ZLOJbG2Tmuwi_ffd0h56mmF4b-bNh9AjJTmlpHxZrWc5EGA5YzAVQtygETAhMiaK4vaqv0eTlDaEEDoFwcrpCM1nuIohpWzuuxj2B_weujZ4_LnV3rf-By-i3tn_EH-xCxEvD3VsG1wF37W-73d4HerQpQd05_Q22cmljtH3Yv5VLbPVx-tbNVtlBkroMmEKXRd1zYBSMMSYhjeGazNMRCOl49ZyZ4CbIV6tnWWguWbcFqLQxEnNxuj5vHcfw19vU6c2oY9-OKlASi5LBkIMKnJWmdNn0Tq1j-1Ox4OiRJ14qYGXOvFSF16D5elsaa21V3KQghPKjvklZtQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2884893277</pqid></control><display><type>article</type><title>A Cross-Entropy Motion Planning Framework for Hybrid Continuum Robots</title><source>IEEE Electronic Library (IEL)</source><creator>Chen, Jibiao ; Yan, Junyan ; Qiu, Yufu ; Fang, Haiyang ; Chen, Jianghua ; Cheng, Shing Shin</creator><creatorcontrib>Chen, Jibiao ; Yan, Junyan ; Qiu, Yufu ; Fang, Haiyang ; Chen, Jianghua ; Cheng, Shing Shin</creatorcontrib><description>The sampling-based motion planners, including the Rapidly-exploring Random Trees (RRT) algorithms, are widely utilized in continuum robots, enabling efficient search for feasible motion plans in constrained environments. In surgical robotics, complex mapping among the high-dimensional kinematics of continuum robots, trajectory parameterization, and path redundancy may lead to non-optimal motion path, which in turn affects their efficiency and surgical task performance (e.g. path following), and ultimately the patient outcome. In this letter, a cross-entropy (CE) motion planning framework is proposed for continuum robots, wherein the RRT * planner is equipped with a CE estimation method serving as a probabilistic model to sample elite trajectories with optimal computation costs. It can asymptotically optimize the sampling distributions among individuals in terms of either robot states or parameterized trajectories. The presented CE motion planners were implemented on a hybrid continuum robot to enable obstacle avoidance, approximate follow-the-leader (FTL) motion, and navigation in a clinical scenario. They are shown to offer lower sampling cost and higher computational efficiency compared to existing approaches.</description><identifier>ISSN: 2377-3766</identifier><identifier>EISSN: 2377-3766</identifier><identifier>DOI: 10.1109/LRA.2023.3325777</identifier><identifier>CODEN: IRALC6</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Algorithms ; Continuum robots ; cross-entropy (CE) motion planning ; Electron tubes ; Entropy (Information theory) ; Kinematics ; Motion planning ; Motion segmentation ; Obstacle avoidance ; Parameterization ; Planning ; Probabilistic models ; Rapidly-exploring Random Trees (RRT) approach ; Redundancy ; Robot dynamics ; Robot kinematics ; Robotics ; Robots ; Sampling ; sampling-based motion planning ; Trajectory ; Trajectory optimization ; Trajectory planning</subject><ispartof>IEEE robotics and automation letters, 2023-12, Vol.8 (12), p.8200-8207</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c292t-7c6ab6bb32112c0ccd4dc4acbb37d88f4ee4fc24c152bafe32a4a34e676a0f8a3</citedby><cites>FETCH-LOGICAL-c292t-7c6ab6bb32112c0ccd4dc4acbb37d88f4ee4fc24c152bafe32a4a34e676a0f8a3</cites><orcidid>0000-0002-9873-4138 ; 0000-0002-6587-2737 ; 0000-0002-8177-7465 ; 0000-0001-6271-5819 ; 0000-0002-8031-1356 ; 0000-0002-9386-5497</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10287401$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10287401$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Chen, Jibiao</creatorcontrib><creatorcontrib>Yan, Junyan</creatorcontrib><creatorcontrib>Qiu, Yufu</creatorcontrib><creatorcontrib>Fang, Haiyang</creatorcontrib><creatorcontrib>Chen, Jianghua</creatorcontrib><creatorcontrib>Cheng, Shing Shin</creatorcontrib><title>A Cross-Entropy Motion Planning Framework for Hybrid Continuum Robots</title><title>IEEE robotics and automation letters</title><addtitle>LRA</addtitle><description>The sampling-based motion planners, including the Rapidly-exploring Random Trees (RRT) algorithms, are widely utilized in continuum robots, enabling efficient search for feasible motion plans in constrained environments. In surgical robotics, complex mapping among the high-dimensional kinematics of continuum robots, trajectory parameterization, and path redundancy may lead to non-optimal motion path, which in turn affects their efficiency and surgical task performance (e.g. path following), and ultimately the patient outcome. In this letter, a cross-entropy (CE) motion planning framework is proposed for continuum robots, wherein the RRT * planner is equipped with a CE estimation method serving as a probabilistic model to sample elite trajectories with optimal computation costs. It can asymptotically optimize the sampling distributions among individuals in terms of either robot states or parameterized trajectories. The presented CE motion planners were implemented on a hybrid continuum robot to enable obstacle avoidance, approximate follow-the-leader (FTL) motion, and navigation in a clinical scenario. They are shown to offer lower sampling cost and higher computational efficiency compared to existing approaches.</description><subject>Algorithms</subject><subject>Continuum robots</subject><subject>cross-entropy (CE) motion planning</subject><subject>Electron tubes</subject><subject>Entropy (Information theory)</subject><subject>Kinematics</subject><subject>Motion planning</subject><subject>Motion segmentation</subject><subject>Obstacle avoidance</subject><subject>Parameterization</subject><subject>Planning</subject><subject>Probabilistic models</subject><subject>Rapidly-exploring Random Trees (RRT) approach</subject><subject>Redundancy</subject><subject>Robot dynamics</subject><subject>Robot kinematics</subject><subject>Robotics</subject><subject>Robots</subject><subject>Sampling</subject><subject>sampling-based motion planning</subject><subject>Trajectory</subject><subject>Trajectory optimization</subject><subject>Trajectory planning</subject><issn>2377-3766</issn><issn>2377-3766</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkEFLAzEQhYMoWGrvHjwEPO-aTNJN9liW1goVpeg5ZLOJbG2Tmuwi_ffd0h56mmF4b-bNh9AjJTmlpHxZrWc5EGA5YzAVQtygETAhMiaK4vaqv0eTlDaEEDoFwcrpCM1nuIohpWzuuxj2B_weujZ4_LnV3rf-By-i3tn_EH-xCxEvD3VsG1wF37W-73d4HerQpQd05_Q22cmljtH3Yv5VLbPVx-tbNVtlBkroMmEKXRd1zYBSMMSYhjeGazNMRCOl49ZyZ4CbIV6tnWWguWbcFqLQxEnNxuj5vHcfw19vU6c2oY9-OKlASi5LBkIMKnJWmdNn0Tq1j-1Ox4OiRJ14qYGXOvFSF16D5elsaa21V3KQghPKjvklZtQ</recordid><startdate>20231201</startdate><enddate>20231201</enddate><creator>Chen, Jibiao</creator><creator>Yan, Junyan</creator><creator>Qiu, Yufu</creator><creator>Fang, Haiyang</creator><creator>Chen, Jianghua</creator><creator>Cheng, Shing Shin</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-9873-4138</orcidid><orcidid>https://orcid.org/0000-0002-6587-2737</orcidid><orcidid>https://orcid.org/0000-0002-8177-7465</orcidid><orcidid>https://orcid.org/0000-0001-6271-5819</orcidid><orcidid>https://orcid.org/0000-0002-8031-1356</orcidid><orcidid>https://orcid.org/0000-0002-9386-5497</orcidid></search><sort><creationdate>20231201</creationdate><title>A Cross-Entropy Motion Planning Framework for Hybrid Continuum Robots</title><author>Chen, Jibiao ; Yan, Junyan ; Qiu, Yufu ; Fang, Haiyang ; Chen, Jianghua ; Cheng, Shing Shin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c292t-7c6ab6bb32112c0ccd4dc4acbb37d88f4ee4fc24c152bafe32a4a34e676a0f8a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Continuum robots</topic><topic>cross-entropy (CE) motion planning</topic><topic>Electron tubes</topic><topic>Entropy (Information theory)</topic><topic>Kinematics</topic><topic>Motion planning</topic><topic>Motion segmentation</topic><topic>Obstacle avoidance</topic><topic>Parameterization</topic><topic>Planning</topic><topic>Probabilistic models</topic><topic>Rapidly-exploring Random Trees (RRT) approach</topic><topic>Redundancy</topic><topic>Robot dynamics</topic><topic>Robot kinematics</topic><topic>Robotics</topic><topic>Robots</topic><topic>Sampling</topic><topic>sampling-based motion planning</topic><topic>Trajectory</topic><topic>Trajectory optimization</topic><topic>Trajectory planning</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Jibiao</creatorcontrib><creatorcontrib>Yan, Junyan</creatorcontrib><creatorcontrib>Qiu, Yufu</creatorcontrib><creatorcontrib>Fang, Haiyang</creatorcontrib><creatorcontrib>Chen, Jianghua</creatorcontrib><creatorcontrib>Cheng, Shing Shin</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE robotics and automation letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Chen, Jibiao</au><au>Yan, Junyan</au><au>Qiu, Yufu</au><au>Fang, Haiyang</au><au>Chen, Jianghua</au><au>Cheng, Shing Shin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Cross-Entropy Motion Planning Framework for Hybrid Continuum Robots</atitle><jtitle>IEEE robotics and automation letters</jtitle><stitle>LRA</stitle><date>2023-12-01</date><risdate>2023</risdate><volume>8</volume><issue>12</issue><spage>8200</spage><epage>8207</epage><pages>8200-8207</pages><issn>2377-3766</issn><eissn>2377-3766</eissn><coden>IRALC6</coden><abstract>The sampling-based motion planners, including the Rapidly-exploring Random Trees (RRT) algorithms, are widely utilized in continuum robots, enabling efficient search for feasible motion plans in constrained environments. In surgical robotics, complex mapping among the high-dimensional kinematics of continuum robots, trajectory parameterization, and path redundancy may lead to non-optimal motion path, which in turn affects their efficiency and surgical task performance (e.g. path following), and ultimately the patient outcome. In this letter, a cross-entropy (CE) motion planning framework is proposed for continuum robots, wherein the RRT * planner is equipped with a CE estimation method serving as a probabilistic model to sample elite trajectories with optimal computation costs. It can asymptotically optimize the sampling distributions among individuals in terms of either robot states or parameterized trajectories. The presented CE motion planners were implemented on a hybrid continuum robot to enable obstacle avoidance, approximate follow-the-leader (FTL) motion, and navigation in a clinical scenario. They are shown to offer lower sampling cost and higher computational efficiency compared to existing approaches.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/LRA.2023.3325777</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0002-9873-4138</orcidid><orcidid>https://orcid.org/0000-0002-6587-2737</orcidid><orcidid>https://orcid.org/0000-0002-8177-7465</orcidid><orcidid>https://orcid.org/0000-0001-6271-5819</orcidid><orcidid>https://orcid.org/0000-0002-8031-1356</orcidid><orcidid>https://orcid.org/0000-0002-9386-5497</orcidid></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 2377-3766 |
| ispartof | IEEE robotics and automation letters, 2023-12, Vol.8 (12), p.8200-8207 |
| issn | 2377-3766 2377-3766 |
| language | eng |
| recordid | cdi_proquest_journals_2884893277 |
| source | IEEE Electronic Library (IEL) |
| subjects | Algorithms Continuum robots cross-entropy (CE) motion planning Electron tubes Entropy (Information theory) Kinematics Motion planning Motion segmentation Obstacle avoidance Parameterization Planning Probabilistic models Rapidly-exploring Random Trees (RRT) approach Redundancy Robot dynamics Robot kinematics Robotics Robots Sampling sampling-based motion planning Trajectory Trajectory optimization Trajectory planning |
| title | A Cross-Entropy Motion Planning Framework for Hybrid Continuum Robots |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-29T20%3A21%3A10IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Cross-Entropy%20Motion%20Planning%20Framework%20for%20Hybrid%20Continuum%20Robots&rft.jtitle=IEEE%20robotics%20and%20automation%20letters&rft.au=Chen,%20Jibiao&rft.date=2023-12-01&rft.volume=8&rft.issue=12&rft.spage=8200&rft.epage=8207&rft.pages=8200-8207&rft.issn=2377-3766&rft.eissn=2377-3766&rft.coden=IRALC6&rft_id=info:doi/10.1109/LRA.2023.3325777&rft_dat=%3Cproquest_RIE%3E2884893277%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2884893277&rft_id=info:pmid/&rft_ieee_id=10287401&rfr_iscdi=true |