Roboat III: An autonomous surface vessel for urban transportation
In this paper, we present our novel autonomous surface vessel platform, a full‐scale Roboat for urban transportation. This 4‐m‐long Roboat is designed with six seats and can carry a payload of up to 1000 kg. Roboat has two main thrusters for cruising and two tunnel thrusters to accommodate docking a...
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| Published in | Journal of field robotics Vol. 40; no. 8; pp. 1996 - 2009 |
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| Main Authors | , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken
Wiley Subscription Services, Inc
01.12.2023
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| Online Access | Get full text |
| ISSN | 1556-4959 1556-4967 |
| DOI | 10.1002/rob.22237 |
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| Abstract | In this paper, we present our novel autonomous surface vessel platform, a full‐scale Roboat for urban transportation. This 4‐m‐long Roboat is designed with six seats and can carry a payload of up to 1000 kg. Roboat has two main thrusters for cruising and two tunnel thrusters to accommodate docking and interconnectivity between Roboats. We build an adaptive nonlinear model predictive controller for trajectory tracking to account for payload changes while transporting passengers. We use a sparse directed graph to represent the canal topological map and then find the most time‐efficient global path in a city‐scale environment using the algorithm. We then employ a multiobjective algorithm's lexicographic search to generate an obstacle‐free path using a point‐cloud projected two‐dimensional occupancy grid map. We also develop a docking mechanism to allow Roboat to “grasp” the docking station. Extensive experiments in Amsterdam waterways demonstrate that Roboat can (1) successfully track the optimal trajectories generated by the planner with varying numbers of passengers on board; (2) autonomously dock to the station without human intervention; (3) execute an autonomous water taxi task where it docks to pick up passengers, drive passengers to the destination while planning its path to avoid obstacles, and finally dock to drop off passengers. |
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| AbstractList | In this paper, we present our novel autonomous surface vessel platform, a full‐scale Roboat for urban transportation. This 4‐m‐long Roboat is designed with six seats and can carry a payload of up to 1000 kg. Roboat has two main thrusters for cruising and two tunnel thrusters to accommodate docking and interconnectivity between Roboats. We build an adaptive nonlinear model predictive controller for trajectory tracking to account for payload changes while transporting passengers. We use a sparse directed graph to represent the canal topological map and then find the most time‐efficient global path in a city‐scale environment using the algorithm. We then employ a multiobjective algorithm's lexicographic search to generate an obstacle‐free path using a point‐cloud projected two‐dimensional occupancy grid map. We also develop a docking mechanism to allow Roboat to “grasp” the docking station. Extensive experiments in Amsterdam waterways demonstrate that Roboat can (1) successfully track the optimal trajectories generated by the planner with varying numbers of passengers on board; (2) autonomously dock to the station without human intervention; (3) execute an autonomous water taxi task where it docks to pick up passengers, drive passengers to the destination while planning its path to avoid obstacles, and finally dock to drop off passengers. In this paper, we present our novel autonomous surface vessel platform, a full‐scale Roboat for urban transportation. This 4‐m‐long Roboat is designed with six seats and can carry a payload of up to 1000 kg. Roboat has two main thrusters for cruising and two tunnel thrusters to accommodate docking and interconnectivity between Roboats. We build an adaptive nonlinear model predictive controller for trajectory tracking to account for payload changes while transporting passengers. We use a sparse directed graph to represent the canal topological map and then find the most time‐efficient global path in a city‐scale environment using the A* ${A}^{* }$ algorithm. We then employ a multiobjective algorithm's lexicographic search to generate an obstacle‐free path using a point‐cloud projected two‐dimensional occupancy grid map. We also develop a docking mechanism to allow Roboat to “grasp” the docking station. Extensive experiments in Amsterdam waterways demonstrate that Roboat can (1) successfully track the optimal trajectories generated by the planner with varying numbers of passengers on board; (2) autonomously dock to the station without human intervention; (3) execute an autonomous water taxi task where it docks to pick up passengers, drive passengers to the destination while planning its path to avoid obstacles, and finally dock to drop off passengers. |
| Author | Doornbusch, Rens Leoni, Pietro Jordan, Joshua Wang, Wei Fernández‐Gutiérrez, David Shan, Tixiao Ratti, Carlo Schiphorst, Jonathan Klein Hagemann, Niklas Duarte, Fabio Rus, Daniela |
| Author_xml | – sequence: 1 givenname: Wei surname: Wang fullname: Wang, Wei organization: Computer Science and Artificial Intelligence Laboratory (CSAIL) Massachusetts Institute of Technology Cambridge Massachusetts USA, SENSEable City Laboratory Massachusetts Institute of Technology Cambridge Massachusetts USA – sequence: 2 givenname: David surname: Fernández‐Gutiérrez fullname: Fernández‐Gutiérrez, David organization: SENSEable City Laboratory Massachusetts Institute of Technology Cambridge Massachusetts USA – sequence: 3 givenname: Rens surname: Doornbusch fullname: Doornbusch, Rens organization: Amsterdam Institute for Advanced Metropolitan Solutions (AMS) Amsterdam The Netherlands – sequence: 4 givenname: Joshua surname: Jordan fullname: Jordan, Joshua organization: Amsterdam Institute for Advanced Metropolitan Solutions (AMS) Amsterdam The Netherlands – sequence: 5 givenname: Tixiao surname: Shan fullname: Shan, Tixiao organization: SENSEable City Laboratory Massachusetts Institute of Technology Cambridge Massachusetts USA – sequence: 6 givenname: Pietro surname: Leoni fullname: Leoni, Pietro organization: SENSEable City Laboratory Massachusetts Institute of Technology Cambridge Massachusetts USA – sequence: 7 givenname: Niklas surname: Hagemann fullname: Hagemann, Niklas organization: SENSEable City Laboratory Massachusetts Institute of Technology Cambridge Massachusetts USA – sequence: 8 givenname: Jonathan Klein surname: Schiphorst fullname: Schiphorst, Jonathan Klein organization: Amsterdam Institute for Advanced Metropolitan Solutions (AMS) Amsterdam The Netherlands – sequence: 9 givenname: Fabio surname: Duarte fullname: Duarte, Fabio organization: SENSEable City Laboratory Massachusetts Institute of Technology Cambridge Massachusetts USA – sequence: 10 givenname: Carlo surname: Ratti fullname: Ratti, Carlo organization: SENSEable City Laboratory Massachusetts Institute of Technology Cambridge Massachusetts USA – sequence: 11 givenname: Daniela surname: Rus fullname: Rus, Daniela organization: Computer Science and Artificial Intelligence Laboratory (CSAIL) Massachusetts Institute of Technology Cambridge Massachusetts USA |
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| Cites_doi | 10.1016/j.arcontrol.2016.04.018 10.1109/ICRA.2018.8460632 10.1109/ICRA.2019.8793525 10.1109/ROBOT.2007.364181 10.1109/OCEANS.2008.5289429 10.1109/ICRA48506.2021.9561331 10.1109/IROS45743.2020.9340743 10.1109/CDC42340.2020.9304298 10.1109/IROS.2007.4399056 10.1109/IROS45743.2020.9340712 10.1002/rob.21767 10.1109/JOE.2016.2571158 10.1109/ICRA.2014.6907011 10.1109/IROS45743.2020.9341176 10.1109/ROBOT.2010.5509538 10.1109/ICRA.2011.5980509 10.1109/TCST.2014.2303805 10.1109/ICRA.2011.5979561 10.1109/ROBOT.2007.364249 10.1109/JOE.2021.3059210 10.1109/TIE.2008.2005933 10.1109/IROS40897.2019.8968131 10.1109/MRS.2019.8901054 10.1109/IROS.2016.7759617 10.1109/CDC.2015.7402330 10.1115/1.3662552 10.1109/JOE.2013.2278891 10.4173/mic.2004.1.1 10.1007/978-3-319-68496-3_16 |
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| SubjectTerms | Algorithms Docking Graph theory Nonlinear control Obstacle avoidance Predictive control Seats Tracking control Trajectory control Trajectory optimization Tunnel thrusters Urban transportation Vessels Waterways |
| Title | Roboat III: An autonomous surface vessel for urban transportation |
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