TY - JOUR
T1 - Model Predictive Trajectory Optimization and Control for Autonomous Surface Vessels Considering Traffic Rules
AU - Tsolakis, Anastasios
AU - Negenborn, Rudy R.
AU - Reppa, Vasso
AU - Ferranti, Laura
PY - 2024/2/13
Y1 - 2024/2/13
N2 - This paper presents a rule-compliant trajectory optimization method for the guidance and control of Autonomous Surface Vessels. The method builds on Model Predictive Contouring Control and incorporates the International Regulations for Preventing Collisions at Sea relevant to motion planning. We use these rules for traffic situation assessment and to derive traffic-related constraints that are inserted in the optimization problem. Our optimization-based approach enables the formalization of abstract verbal expressions, such as traffic rules, and their incorporation in the trajectory optimization algorithm along with the dynamics and other constraints that dictate the system’s evolution over a sufficiently long planning horizon. The ability to plan considering different types of constraints and the system’s dynamics, over a long horizon in a unified manner, leads to a proactive motion planner that mimics rule-compliant maneuvering behavior, suitable for navigation in mixed-traffic environments. The efficacy and scalability of the derived algorithm are validated in different simulation scenarios, including complex traffic situations with multiple Obstacle Vessels.
AB - This paper presents a rule-compliant trajectory optimization method for the guidance and control of Autonomous Surface Vessels. The method builds on Model Predictive Contouring Control and incorporates the International Regulations for Preventing Collisions at Sea relevant to motion planning. We use these rules for traffic situation assessment and to derive traffic-related constraints that are inserted in the optimization problem. Our optimization-based approach enables the formalization of abstract verbal expressions, such as traffic rules, and their incorporation in the trajectory optimization algorithm along with the dynamics and other constraints that dictate the system’s evolution over a sufficiently long planning horizon. The ability to plan considering different types of constraints and the system’s dynamics, over a long horizon in a unified manner, leads to a proactive motion planner that mimics rule-compliant maneuvering behavior, suitable for navigation in mixed-traffic environments. The efficacy and scalability of the derived algorithm are validated in different simulation scenarios, including complex traffic situations with multiple Obstacle Vessels.
KW - Autonomous surface vessels
KW - Costs
KW - Heuristic algorithms
KW - model predictive control
KW - Navigation
KW - Optimization
KW - Regulation
KW - Task analysis
KW - traffic regulations
KW - Trajectory optimization
UR - http://www.scopus.com/inward/record.url?scp=85187252442&partnerID=8YFLogxK
U2 - 10.1109/TITS.2024.3357284
DO - 10.1109/TITS.2024.3357284
M3 - Article
AN - SCOPUS:85187252442
SN - 1524-9050
SP - 1
EP - 14
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
ER -