An insect-inspired model facilitating autonomous navigation by incorporating goal approaching and collision avoidance Article Swipe
Xuelong Sun
,
Qinbing Fu
,
Jigen Peng
,
Shigang Yue
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.neunet.2023.05.033
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.neunet.2023.05.033
Being one of the most fundamental and crucial capacity of robots and animals, autonomous navigation that consists of goal approaching and collision avoidance enables completion of various tasks while traversing different environments. In light of the impressive navigational abilities of insects despite their tiny brains compared to mammals, the idea of seeking solutions from insects for the two key problems of navigation, i.e., goal approaching and collision avoidance, has fascinated researchers and engineers for many years. However, previous bio-inspired studies have focused on merely one of these two problems at one time. Insect-inspired navigation algorithms that synthetically incorporate both goal approaching and collision avoidance, and studies that investigate the interactions of these two mechanisms in the context of sensory-motor closed-loop autonomous navigation are lacking. To fill this gap, we propose an insect-inspired autonomous navigation algorithm to integrate the goal approaching mechanism as the global working memory inspired by the sweat bee's path integration (PI) mechanism, and the collision avoidance model as the local immediate cue built upon the locust's lobula giant movement detector (LGMD) model. The presented algorithm is utilized to drive agents to complete navigation task in a sensory-motor closed-loop manner within a bounded static or dynamic environment. Simulation results demonstrate that the synthetic algorithm is capable of guiding the agent to complete challenging navigation tasks in a robust and efficient way. This study takes the first tentative step to integrate the insect-like navigation mechanisms with different functionalities (i.e., global goal and local interrupt) into a coordinated control system that future research avenues could build upon.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.neunet.2023.05.033
- OA Status
- hybrid
- Cited By
- 16
- References
- 70
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4378070761
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4378070761Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.neunet.2023.05.033Digital Object Identifier
- Title
-
An insect-inspired model facilitating autonomous navigation by incorporating goal approaching and collision avoidanceWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-05-24Full publication date if available
- Authors
-
Xuelong Sun, Qinbing Fu, Jigen Peng, Shigang YueList of authors in order
- Landing page
-
https://doi.org/10.1016/j.neunet.2023.05.033Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.neunet.2023.05.033Direct OA link when available
- Concepts
-
Collision avoidance, Computer science, Artificial intelligence, Collision, Autonomous agent, Simulation, Computer securityTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
16Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 7, 2024: 8, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
70Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4378070761 |
|---|---|
| doi | https://doi.org/10.1016/j.neunet.2023.05.033 |
| ids.doi | https://doi.org/10.1016/j.neunet.2023.05.033 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/37285728 |
| ids.openalex | https://openalex.org/W4378070761 |
| fwci | 2.91149344 |
| mesh[0].qualifier_ui | |
| mesh[0].descriptor_ui | D000818 |
| mesh[0].is_major_topic | False |
| mesh[0].qualifier_name | |
| mesh[0].descriptor_name | Animals |
| mesh[1].qualifier_ui | |
| mesh[1].descriptor_ui | D006040 |
| mesh[1].is_major_topic | True |
| mesh[1].qualifier_name | |
| mesh[1].descriptor_name | Goals |
| mesh[2].qualifier_ui | |
| mesh[2].descriptor_ui | D007313 |
| mesh[2].is_major_topic | True |
| mesh[2].qualifier_name | |
| mesh[2].descriptor_name | Insecta |
| mesh[3].qualifier_ui | |
| mesh[3].descriptor_ui | D003198 |
| mesh[3].is_major_topic | False |
| mesh[3].qualifier_name | |
| mesh[3].descriptor_name | Computer Simulation |
| mesh[4].qualifier_ui | |
| mesh[4].descriptor_ui | D000465 |
| mesh[4].is_major_topic | False |
| mesh[4].qualifier_name | |
| mesh[4].descriptor_name | Algorithms |
| mesh[5].qualifier_ui | |
| mesh[5].descriptor_ui | D001921 |
| mesh[5].is_major_topic | False |
| mesh[5].qualifier_name | |
| mesh[5].descriptor_name | Brain |
| mesh[6].qualifier_ui | |
| mesh[6].descriptor_ui | D008322 |
| mesh[6].is_major_topic | False |
| mesh[6].qualifier_name | |
| mesh[6].descriptor_name | Mammals |
| mesh[7].qualifier_ui | |
| mesh[7].descriptor_ui | D000818 |
| mesh[7].is_major_topic | False |
| mesh[7].qualifier_name | |
| mesh[7].descriptor_name | Animals |
| mesh[8].qualifier_ui | |
| mesh[8].descriptor_ui | D006040 |
| mesh[8].is_major_topic | True |
| mesh[8].qualifier_name | |
| mesh[8].descriptor_name | Goals |
| mesh[9].qualifier_ui | |
| mesh[9].descriptor_ui | D007313 |
| mesh[9].is_major_topic | True |
| mesh[9].qualifier_name | |
| mesh[9].descriptor_name | Insecta |
| mesh[10].qualifier_ui | |
| mesh[10].descriptor_ui | D003198 |
| mesh[10].is_major_topic | False |
| mesh[10].qualifier_name | |
| mesh[10].descriptor_name | Computer Simulation |
| mesh[11].qualifier_ui | |
| mesh[11].descriptor_ui | D000465 |
| mesh[11].is_major_topic | False |
| mesh[11].qualifier_name | |
| mesh[11].descriptor_name | Algorithms |
| mesh[12].qualifier_ui | |
| mesh[12].descriptor_ui | D001921 |
| mesh[12].is_major_topic | False |
| mesh[12].qualifier_name | |
| mesh[12].descriptor_name | Brain |
| mesh[13].qualifier_ui | |
| mesh[13].descriptor_ui | D008322 |
| mesh[13].is_major_topic | False |
| mesh[13].qualifier_name | |
| mesh[13].descriptor_name | Mammals |
| type | article |
| title | An insect-inspired model facilitating autonomous navigation by incorporating goal approaching and collision avoidance |
| biblio.issue | |
| biblio.volume | 165 |
| biblio.last_page | 118 |
| biblio.first_page | 106 |
| grants[0].funder | https://openalex.org/F4320321001 |
| grants[0].award_id | 12031003 |
| grants[0].funder_display_name | National Natural Science Foundation of China |
| grants[1].funder | https://openalex.org/F4320321001 |
| grants[1].award_id | 62206066 |
| grants[1].funder_display_name | National Natural Science Foundation of China |
| grants[2].funder | https://openalex.org/F4320321106 |
| grants[2].award_id | |
| grants[2].funder_display_name | Ministry of Education of the People's Republic of China |
| grants[3].funder | https://openalex.org/F4320321543 |
| grants[3].award_id | 2022M710864 |
| grants[3].funder_display_name | China Postdoctoral Science Foundation |
| grants[4].funder | https://openalex.org/F4320332999 |
| grants[4].award_id | |
| grants[4].funder_display_name | Horizon 2020 Framework Programme |
| grants[5].funder | https://openalex.org/F4320335254 |
| grants[5].award_id | |
| grants[5].funder_display_name | Horizon 2020 |
| grants[6].funder | https://openalex.org/F4320335982 |
| grants[6].award_id | 22YJCZH032 |
| grants[6].funder_display_name | Humanities and Social Science Fund of Ministry of Education of China |
| grants[7].funder | https://openalex.org/F4320338337 |
| grants[7].award_id | 778062 |
| grants[7].funder_display_name | H2020 Marie Skłodowska-Curie Actions |
| topics[0].id | https://openalex.org/T10586 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9943000078201294 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1707 |
| topics[0].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[0].display_name | Robotic Path Planning Algorithms |
| topics[1].id | https://openalex.org/T10462 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9779999852180481 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Reinforcement Learning in Robotics |
| topics[2].id | https://openalex.org/T11099 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9682000279426575 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2203 |
| topics[2].subfield.display_name | Automotive Engineering |
| topics[2].display_name | Autonomous Vehicle Technology and Safety |
| funders[0].id | https://openalex.org/F4320321001 |
| funders[0].ror | https://ror.org/01h0zpd94 |
| funders[0].display_name | National Natural Science Foundation of China |
| funders[1].id | https://openalex.org/F4320321106 |
| funders[1].ror | https://ror.org/01mv9t934 |
| funders[1].display_name | Ministry of Education of the People's Republic of China |
| funders[2].id | https://openalex.org/F4320321543 |
| funders[2].ror | https://ror.org/0426zh255 |
| funders[2].display_name | China Postdoctoral Science Foundation |
| funders[3].id | https://openalex.org/F4320332999 |
| funders[3].ror | https://ror.org/00k4n6c32 |
| funders[3].display_name | Horizon 2020 Framework Programme |
| funders[4].id | https://openalex.org/F4320335254 |
| funders[4].ror | |
| funders[4].display_name | Horizon 2020 |
| funders[5].id | https://openalex.org/F4320335982 |
| funders[5].ror | |
| funders[5].display_name | Humanities and Social Science Fund of Ministry of Education of China |
| funders[6].id | https://openalex.org/F4320338337 |
| funders[6].ror | |
| funders[6].display_name | H2020 Marie Skłodowska-Curie Actions |
| is_xpac | False |
| apc_list.value | 3350 |
| apc_list.currency | USD |
| apc_list.value_usd | 3350 |
| apc_paid.value | 3350 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 3350 |
| concepts[0].id | https://openalex.org/C2780864053 |
| concepts[0].level | 3 |
| concepts[0].score | 0.8656519651412964 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q5147495 |
| concepts[0].display_name | Collision avoidance |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.6106609106063843 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C154945302 |
| concepts[2].level | 1 |
| concepts[2].score | 0.5300031900405884 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[2].display_name | Artificial intelligence |
| concepts[3].id | https://openalex.org/C121704057 |
| concepts[3].level | 2 |
| concepts[3].score | 0.46005508303642273 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q352070 |
| concepts[3].display_name | Collision |
| concepts[4].id | https://openalex.org/C13687954 |
| concepts[4].level | 2 |
| concepts[4].score | 0.41099289059638977 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q4826847 |
| concepts[4].display_name | Autonomous agent |
| concepts[5].id | https://openalex.org/C44154836 |
| concepts[5].level | 1 |
| concepts[5].score | 0.37077969312667847 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q45045 |
| concepts[5].display_name | Simulation |
| concepts[6].id | https://openalex.org/C38652104 |
| concepts[6].level | 1 |
| concepts[6].score | 0.1443067193031311 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q3510521 |
| concepts[6].display_name | Computer security |
| keywords[0].id | https://openalex.org/keywords/collision-avoidance |
| keywords[0].score | 0.8656519651412964 |
| keywords[0].display_name | Collision avoidance |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.6106609106063843 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[2].score | 0.5300031900405884 |
| keywords[2].display_name | Artificial intelligence |
| keywords[3].id | https://openalex.org/keywords/collision |
| keywords[3].score | 0.46005508303642273 |
| keywords[3].display_name | Collision |
| keywords[4].id | https://openalex.org/keywords/autonomous-agent |
| keywords[4].score | 0.41099289059638977 |
| keywords[4].display_name | Autonomous agent |
| keywords[5].id | https://openalex.org/keywords/simulation |
| keywords[5].score | 0.37077969312667847 |
| keywords[5].display_name | Simulation |
| keywords[6].id | https://openalex.org/keywords/computer-security |
| keywords[6].score | 0.1443067193031311 |
| keywords[6].display_name | Computer security |
| language | en |
| locations[0].id | doi:10.1016/j.neunet.2023.05.033 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S123019304 |
| locations[0].source.issn | 0893-6080, 1879-2782 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 0893-6080 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Neural Networks |
| locations[0].source.host_organization | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_name | Elsevier BV |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320990 |
| locations[0].license | cc-by-nc-nd |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by-nc-nd |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Neural Networks |
| locations[0].landing_page_url | https://doi.org/10.1016/j.neunet.2023.05.033 |
| locations[1].id | pmid:37285728 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | Neural networks : the official journal of the International Neural Network Society |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/37285728 |
| indexed_in | crossref, pubmed |
| authorships[0].author.id | https://openalex.org/A5036080638 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-9035-5523 |
| authorships[0].author.display_name | Xuelong Sun |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I37987034 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Mathematics and Information Science, Guangzhou University, Guangzhou, 510006, China |
| authorships[0].affiliations[1].institution_ids | https://openalex.org/I37987034 |
| authorships[0].affiliations[1].raw_affiliation_string | Machine Life and Intelligence Research Centre, Guangzhou University, Guangzhou, 510006, China |
| authorships[0].institutions[0].id | https://openalex.org/I37987034 |
| authorships[0].institutions[0].ror | https://ror.org/05ar8rn06 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I37987034 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Guangzhou University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Xuelong Sun |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Machine Life and Intelligence Research Centre, Guangzhou University, Guangzhou, 510006, China, School of Mathematics and Information Science, Guangzhou University, Guangzhou, 510006, China |
| authorships[1].author.id | https://openalex.org/A5007505981 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-5726-6956 |
| authorships[1].author.display_name | Qinbing Fu |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I37987034 |
| authorships[1].affiliations[0].raw_affiliation_string | Machine Life and Intelligence Research Centre, Guangzhou University, Guangzhou, 510006, China |
| authorships[1].affiliations[1].institution_ids | https://openalex.org/I37987034 |
| authorships[1].affiliations[1].raw_affiliation_string | School of Mathematics and Information Science, Guangzhou University, Guangzhou, 510006, China |
| authorships[1].institutions[0].id | https://openalex.org/I37987034 |
| authorships[1].institutions[0].ror | https://ror.org/05ar8rn06 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I37987034 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Guangzhou University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Qinbing Fu |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Machine Life and Intelligence Research Centre, Guangzhou University, Guangzhou, 510006, China, School of Mathematics and Information Science, Guangzhou University, Guangzhou, 510006, China |
| authorships[2].author.id | https://openalex.org/A5003653964 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-7207-3183 |
| authorships[2].author.display_name | Jigen Peng |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I37987034 |
| authorships[2].affiliations[0].raw_affiliation_string | School of Mathematics and Information Science, Guangzhou University, Guangzhou, 510006, China |
| authorships[2].affiliations[1].institution_ids | https://openalex.org/I37987034 |
| authorships[2].affiliations[1].raw_affiliation_string | Machine Life and Intelligence Research Centre, Guangzhou University, Guangzhou, 510006, China |
| authorships[2].institutions[0].id | https://openalex.org/I37987034 |
| authorships[2].institutions[0].ror | https://ror.org/05ar8rn06 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I37987034 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Guangzhou University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Jigen Peng |
| authorships[2].is_corresponding | True |
| authorships[2].raw_affiliation_strings | Machine Life and Intelligence Research Centre, Guangzhou University, Guangzhou, 510006, China, School of Mathematics and Information Science, Guangzhou University, Guangzhou, 510006, China |
| authorships[3].author.id | https://openalex.org/A5059786971 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-1899-6307 |
| authorships[3].author.display_name | Shigang Yue |
| authorships[3].countries | GB |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I51532219 |
| authorships[3].affiliations[0].raw_affiliation_string | Computational Intelligence Lab (CIL)/School of Computer Science, University of Lincoln, Lincoln, LN6 7TS, United Kingdom |
| authorships[3].affiliations[1].institution_ids | https://openalex.org/I153648349 |
| authorships[3].affiliations[1].raw_affiliation_string | School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, United Kingdom |
| authorships[3].institutions[0].id | https://openalex.org/I153648349 |
| authorships[3].institutions[0].ror | https://ror.org/04h699437 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I153648349 |
| authorships[3].institutions[0].country_code | GB |
| authorships[3].institutions[0].display_name | University of Leicester |
| authorships[3].institutions[1].id | https://openalex.org/I51532219 |
| authorships[3].institutions[1].ror | https://ror.org/03yeq9x20 |
| authorships[3].institutions[1].type | education |
| authorships[3].institutions[1].lineage | https://openalex.org/I51532219 |
| authorships[3].institutions[1].country_code | GB |
| authorships[3].institutions[1].display_name | University of Lincoln |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Shigang Yue |
| authorships[3].is_corresponding | True |
| authorships[3].raw_affiliation_strings | Computational Intelligence Lab (CIL)/School of Computer Science, University of Lincoln, Lincoln, LN6 7TS, United Kingdom, School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, United Kingdom |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1016/j.neunet.2023.05.033 |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | An insect-inspired model facilitating autonomous navigation by incorporating goal approaching and collision avoidance |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10586 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9943000078201294 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1707 |
| primary_topic.subfield.display_name | Computer Vision and Pattern Recognition |
| primary_topic.display_name | Robotic Path Planning Algorithms |
| related_works | https://openalex.org/W4317634134, https://openalex.org/W2981729160, https://openalex.org/W4310743282, https://openalex.org/W2743212448, https://openalex.org/W2889566344, https://openalex.org/W3113932901, https://openalex.org/W1819938260, https://openalex.org/W2340892746, https://openalex.org/W3163022373, https://openalex.org/W3005999311 |
| cited_by_count | 16 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 7 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 8 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1016/j.neunet.2023.05.033 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S123019304 |
| best_oa_location.source.issn | 0893-6080, 1879-2782 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 0893-6080 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Neural Networks |
| best_oa_location.source.host_organization | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_name | Elsevier BV |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| best_oa_location.license | cc-by-nc-nd |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Neural Networks |
| best_oa_location.landing_page_url | https://doi.org/10.1016/j.neunet.2023.05.033 |
| primary_location.id | doi:10.1016/j.neunet.2023.05.033 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S123019304 |
| primary_location.source.issn | 0893-6080, 1879-2782 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 0893-6080 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Neural Networks |
| primary_location.source.host_organization | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_name | Elsevier BV |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| primary_location.license | cc-by-nc-nd |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Neural Networks |
| primary_location.landing_page_url | https://doi.org/10.1016/j.neunet.2023.05.033 |
| publication_date | 2023-05-24 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2146345399, https://openalex.org/W2952786843, https://openalex.org/W2272552872, https://openalex.org/W2047374839, https://openalex.org/W2176852228, https://openalex.org/W2097856935, https://openalex.org/W3017918822, https://openalex.org/W2938262524, https://openalex.org/W2947447667, https://openalex.org/W2025197196, https://openalex.org/W2158061932, https://openalex.org/W2943528199, https://openalex.org/W2095849074, https://openalex.org/W1969472392, https://openalex.org/W2914693969, https://openalex.org/W2129564881, https://openalex.org/W2030283526, https://openalex.org/W2332578720, https://openalex.org/W6752198896, https://openalex.org/W2990230838, https://openalex.org/W2800226686, https://openalex.org/W3190041142, https://openalex.org/W2968094342, https://openalex.org/W2020134397, https://openalex.org/W2606680161, https://openalex.org/W3200508686, https://openalex.org/W2955399703, https://openalex.org/W1981031568, https://openalex.org/W2754385040, https://openalex.org/W2890691917, https://openalex.org/W2912908978, https://openalex.org/W2523902637, https://openalex.org/W3208266771, https://openalex.org/W2185428930, https://openalex.org/W2610861664, https://openalex.org/W2117364466, https://openalex.org/W2929003410, https://openalex.org/W6803345733, https://openalex.org/W4200478016, https://openalex.org/W2133636714, https://openalex.org/W1969970265, https://openalex.org/W2027654885, https://openalex.org/W2088173505, https://openalex.org/W6804890775, https://openalex.org/W2197222329, https://openalex.org/W2043369736, https://openalex.org/W2513016052, https://openalex.org/W2124058244, https://openalex.org/W2533977883, https://openalex.org/W2980991344, https://openalex.org/W3199336337, https://openalex.org/W2890672587, https://openalex.org/W2094544836, https://openalex.org/W2153860256, https://openalex.org/W2764135343, https://openalex.org/W3037205721, https://openalex.org/W3084308099, https://openalex.org/W2011199901, https://openalex.org/W3017151499, https://openalex.org/W2289037009, https://openalex.org/W2106990484, https://openalex.org/W2330020890, https://openalex.org/W2065628087, https://openalex.org/W2050679095, https://openalex.org/W6674446112, https://openalex.org/W2111880621, https://openalex.org/W2024221649, https://openalex.org/W1566405224, https://openalex.org/W4200168644, https://openalex.org/W3142707146 |
| referenced_works_count | 70 |
| abstract_inverted_index.a | 188, 193, 218, 246 |
| abstract_inverted_index.In | 32 |
| abstract_inverted_index.To | 124 |
| abstract_inverted_index.an | 130 |
| abstract_inverted_index.as | 141, 160 |
| abstract_inverted_index.at | 89 |
| abstract_inverted_index.by | 147 |
| abstract_inverted_index.in | 114, 187, 217 |
| abstract_inverted_index.is | 178, 206 |
| abstract_inverted_index.of | 2, 9, 17, 25, 34, 39, 50, 60, 85, 110, 117, 208 |
| abstract_inverted_index.on | 82 |
| abstract_inverted_index.or | 196 |
| abstract_inverted_index.to | 46, 135, 180, 183, 212, 230 |
| abstract_inverted_index.we | 128 |
| abstract_inverted_index.The | 175 |
| abstract_inverted_index.and | 6, 11, 20, 65, 71, 101, 104, 155, 220, 242 |
| abstract_inverted_index.are | 122 |
| abstract_inverted_index.cue | 164 |
| abstract_inverted_index.for | 55, 73 |
| abstract_inverted_index.has | 68 |
| abstract_inverted_index.key | 58 |
| abstract_inverted_index.one | 1, 84, 90 |
| abstract_inverted_index.the | 3, 35, 48, 56, 108, 115, 137, 142, 148, 156, 161, 167, 203, 210, 226, 232 |
| abstract_inverted_index.two | 57, 87, 112 |
| abstract_inverted_index.(PI) | 153 |
| abstract_inverted_index.This | 223 |
| abstract_inverted_index.both | 98 |
| abstract_inverted_index.fill | 125 |
| abstract_inverted_index.from | 53 |
| abstract_inverted_index.gap, | 127 |
| abstract_inverted_index.goal | 18, 63, 99, 138, 241 |
| abstract_inverted_index.have | 80 |
| abstract_inverted_index.idea | 49 |
| abstract_inverted_index.into | 245 |
| abstract_inverted_index.many | 74 |
| abstract_inverted_index.most | 4 |
| abstract_inverted_index.path | 151 |
| abstract_inverted_index.step | 229 |
| abstract_inverted_index.task | 186 |
| abstract_inverted_index.that | 15, 95, 106, 202, 250 |
| abstract_inverted_index.this | 126 |
| abstract_inverted_index.tiny | 43 |
| abstract_inverted_index.upon | 166 |
| abstract_inverted_index.way. | 222 |
| abstract_inverted_index.with | 236 |
| abstract_inverted_index.Being | 0 |
| abstract_inverted_index.agent | 211 |
| abstract_inverted_index.bee's | 150 |
| abstract_inverted_index.build | 255 |
| abstract_inverted_index.built | 165 |
| abstract_inverted_index.could | 254 |
| abstract_inverted_index.drive | 181 |
| abstract_inverted_index.first | 227 |
| abstract_inverted_index.giant | 170 |
| abstract_inverted_index.i.e., | 62 |
| abstract_inverted_index.light | 33 |
| abstract_inverted_index.local | 162, 243 |
| abstract_inverted_index.model | 159 |
| abstract_inverted_index.study | 224 |
| abstract_inverted_index.sweat | 149 |
| abstract_inverted_index.takes | 225 |
| abstract_inverted_index.tasks | 27, 216 |
| abstract_inverted_index.their | 42 |
| abstract_inverted_index.these | 86, 111 |
| abstract_inverted_index.time. | 91 |
| abstract_inverted_index.upon. | 256 |
| abstract_inverted_index.while | 28 |
| abstract_inverted_index.(LGMD) | 173 |
| abstract_inverted_index.(i.e., | 239 |
| abstract_inverted_index.agents | 182 |
| abstract_inverted_index.brains | 44 |
| abstract_inverted_index.future | 251 |
| abstract_inverted_index.global | 143, 240 |
| abstract_inverted_index.lobula | 169 |
| abstract_inverted_index.manner | 191 |
| abstract_inverted_index.memory | 145 |
| abstract_inverted_index.merely | 83 |
| abstract_inverted_index.model. | 174 |
| abstract_inverted_index.robots | 10 |
| abstract_inverted_index.robust | 219 |
| abstract_inverted_index.static | 195 |
| abstract_inverted_index.system | 249 |
| abstract_inverted_index.within | 192 |
| abstract_inverted_index.years. | 75 |
| abstract_inverted_index.avenues | 253 |
| abstract_inverted_index.bounded | 194 |
| abstract_inverted_index.capable | 207 |
| abstract_inverted_index.context | 116 |
| abstract_inverted_index.control | 248 |
| abstract_inverted_index.crucial | 7 |
| abstract_inverted_index.despite | 41 |
| abstract_inverted_index.dynamic | 197 |
| abstract_inverted_index.enables | 23 |
| abstract_inverted_index.focused | 81 |
| abstract_inverted_index.guiding | 209 |
| abstract_inverted_index.insects | 40, 54 |
| abstract_inverted_index.propose | 129 |
| abstract_inverted_index.results | 200 |
| abstract_inverted_index.seeking | 51 |
| abstract_inverted_index.studies | 79, 105 |
| abstract_inverted_index.various | 26 |
| abstract_inverted_index.working | 144 |
| abstract_inverted_index.However, | 76 |
| abstract_inverted_index.animals, | 12 |
| abstract_inverted_index.capacity | 8 |
| abstract_inverted_index.compared | 45 |
| abstract_inverted_index.complete | 184, 213 |
| abstract_inverted_index.consists | 16 |
| abstract_inverted_index.detector | 172 |
| abstract_inverted_index.inspired | 146 |
| abstract_inverted_index.lacking. | 123 |
| abstract_inverted_index.locust's | 168 |
| abstract_inverted_index.mammals, | 47 |
| abstract_inverted_index.movement | 171 |
| abstract_inverted_index.previous | 77 |
| abstract_inverted_index.problems | 59, 88 |
| abstract_inverted_index.research | 252 |
| abstract_inverted_index.utilized | 179 |
| abstract_inverted_index.abilities | 38 |
| abstract_inverted_index.algorithm | 134, 177, 205 |
| abstract_inverted_index.avoidance | 22, 158 |
| abstract_inverted_index.collision | 21, 66, 102, 157 |
| abstract_inverted_index.different | 30, 237 |
| abstract_inverted_index.efficient | 221 |
| abstract_inverted_index.engineers | 72 |
| abstract_inverted_index.immediate | 163 |
| abstract_inverted_index.integrate | 136, 231 |
| abstract_inverted_index.mechanism | 140 |
| abstract_inverted_index.presented | 176 |
| abstract_inverted_index.solutions | 52 |
| abstract_inverted_index.synthetic | 204 |
| abstract_inverted_index.tentative | 228 |
| abstract_inverted_index.Simulation | 199 |
| abstract_inverted_index.algorithms | 94 |
| abstract_inverted_index.autonomous | 13, 120, 132 |
| abstract_inverted_index.avoidance, | 67, 103 |
| abstract_inverted_index.completion | 24 |
| abstract_inverted_index.fascinated | 69 |
| abstract_inverted_index.impressive | 36 |
| abstract_inverted_index.interrupt) | 244 |
| abstract_inverted_index.mechanism, | 154 |
| abstract_inverted_index.mechanisms | 113, 235 |
| abstract_inverted_index.navigation | 14, 93, 121, 133, 185, 215, 234 |
| abstract_inverted_index.traversing | 29 |
| abstract_inverted_index.approaching | 19, 64, 100, 139 |
| abstract_inverted_index.challenging | 214 |
| abstract_inverted_index.closed-loop | 119, 190 |
| abstract_inverted_index.coordinated | 247 |
| abstract_inverted_index.demonstrate | 201 |
| abstract_inverted_index.fundamental | 5 |
| abstract_inverted_index.incorporate | 97 |
| abstract_inverted_index.insect-like | 233 |
| abstract_inverted_index.integration | 152 |
| abstract_inverted_index.investigate | 107 |
| abstract_inverted_index.navigation, | 61 |
| abstract_inverted_index.researchers | 70 |
| abstract_inverted_index.bio-inspired | 78 |
| abstract_inverted_index.environment. | 198 |
| abstract_inverted_index.interactions | 109 |
| abstract_inverted_index.navigational | 37 |
| abstract_inverted_index.environments. | 31 |
| abstract_inverted_index.sensory-motor | 118, 189 |
| abstract_inverted_index.synthetically | 96 |
| abstract_inverted_index.Insect-inspired | 92 |
| abstract_inverted_index.functionalities | 238 |
| abstract_inverted_index.insect-inspired | 131 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5059786971, https://openalex.org/A5003653964 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I153648349, https://openalex.org/I37987034, https://openalex.org/I51532219 |
| citation_normalized_percentile.value | 0.90473553 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |