Publication
Dynamic AMR Navigation: Simulation with Trajectory Prediction of Moving Obstacles
| dc.contributor.author | Cadete, Tomás | |
| dc.contributor.author | Pinto, Vítor H. | |
| dc.contributor.author | Lima, José | |
| dc.contributor.author | Gonçalves, Gil | |
| dc.contributor.author | Costa, Paulo Gomes da | |
| dc.date.accessioned | 2025-02-10T11:15:01Z | |
| dc.date.available | 2025-02-10T11:15:01Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Autonomous Mobile Robots (AMRs) have significantly transformed task management in factories, warehouses, and urban environments. These robots enhance operational efficiency, reduce labor costs, and automate various tasks. However, navigating dynamic environments with moving obstacles, such as human workers, vehicles, and machinery, remains challenging. Traditional navigation systems, which rely on static maps and predefined routes, struggle to adapt to these dynamic settings. This research addresses these limitations by developing a dynamic navigation system that improves AMR performance in industrial and urban scenarios. The system enhances the A* algorithm to account for the current positions and predicted trajectories of moving obstacles, allowing the AMR to navigate safely and efficiently. Advanced sensor technologies, such as LiDAR and stereo cameras, are utilized for real-time environmental perception. The system integrates trajectory prediction and an Artificial Potential Field (APF) method for emergency collision avoidance. The solution is implemented using the Gazebo simulator and the Robot Operating System (ROS2), ensuring real-time operation and adaptive path planning. This research aims to significantly improve AMR safety, efficiency, and adaptability in dynamic environments. | pt_PT |
| dc.description.sponsorship | This work was financially supported by PPS 18: 3D navigation system for mobile robotic equipment from Agenda GreenAuto: Green Innovation for the Automotive Industry, no. C644867037-00000013, investment project no. 54, from the Incentive System to Mobilising Agendas for Business Innovation, funded by the Recovery and Resilience Plan and by European Funds NextGeneration EU. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Cadete, Tomás; Pinto, Vítor H.; Lima, José; Gonçalves, Gil; Costa, Paulo (2024). Dynamic AMR Navigation: Simulation with Trajectory Prediction of Moving Obstacles. In 7th Iberian Robotics Conference (ROBOT 2024). Madrid: IEEE, p. 1-7. ISBN 979-8-3503-7636-4. | pt_PT |
| dc.identifier.doi | 10.1109/ROBOT61475.2024.10797420 | pt_PT |
| dc.identifier.isbn | 979-8-3503-7636-4 | |
| dc.identifier.uri | http://hdl.handle.net/10198/31143 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | IEEE | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Autonomous mobile robot | pt_PT |
| dc.subject | Dynamic navigation | pt_PT |
| dc.subject | Trajectory prediction | pt_PT |
| dc.subject | Moving obstacles | pt_PT |
| dc.subject | Simulation | pt_PT |
| dc.subject | Artificial potential field | pt_PT |
| dc.title | Dynamic AMR Navigation: Simulation with Trajectory Prediction of Moving Obstacles | pt_PT |
| dc.type | conference paper | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 7 | pt_PT |
| oaire.citation.startPage | 1 | pt_PT |
| oaire.citation.title | 7th Iberian Robotics Conference (ROBOT 2024) | pt_PT |
| person.familyName | Lima | |
| person.givenName | José | |
| person.identifier | R-000-8GD | |
| person.identifier.ciencia-id | 6016-C902-86A9 | |
| person.identifier.orcid | 0000-0001-7902-1207 | |
| person.identifier.rid | L-3370-2014 | |
| person.identifier.scopus-author-id | 55851941311 | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | conferenceObject | pt_PT |
| relation.isAuthorOfPublication | d88c2b2a-efc2-48ef-b1fd-1145475e0055 | |
| relation.isAuthorOfPublication.latestForDiscovery | d88c2b2a-efc2-48ef-b1fd-1145475e0055 |