COLREGs-aware Trajectory Optimization for Autonomous Surface Vessels

Tsolakis, Anastasios; Benders, D.; Groot, Oscar de; Negenborn, Rudy R.; Reppa, Vasso; Ferranti, Laura

doi:10.1016/j.ifacol.2022.10.441

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…For example, one line of single-agent motion planning research employs searchbased algorithms based on motion primitives, e.g., rapidlyexploring random trees [29], [38], [39]. Other studies employ model predictive control (MPC) [26], [28], [40] to obtain an optimal control signal. In contrast to using search algorithms based on a finite amount of motion primitives, MPC directly optimizes the controller in the continuous state and input space.…”

Section: B) Motion Planning For Vesselsmentioning

confidence: 99%

Provable Traffic Rule Compliance in Safe Reinforcement Learning on the Open Sea

Krasowski,

Althoff

2024

IEEE Trans. Intell. Veh.

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For safe operation, autonomous vehicles have to obey traffic rules that are set forth in legal documents formulated in natural language. Temporal logic is a suitable concept to formalize such traffic rules. Still, temporal logic rules often result in constraints that are hard to solve using optimization-based motion planners. Reinforcement learning (RL) is a promising method to find motion plans for autonomous vehicles. However, vanilla RL algorithms are based on random exploration and do not automatically comply with traffic rules. Our approach accomplishes guaranteed rule-compliance by integrating temporal logic specifications into RL. Specifically, we consider the application of vessels on the open sea, which must adhere to the Convention on the International Regulations for Preventing Collisions at Sea (COLREGS). To efficiently synthesize rule-compliant actions, we combine predicates based on set-based prediction with a statechart representing our formalized rules and their priorities. Action masking then restricts the RL agent to this set of verified rule-compliant actions. In numerical evaluations on critical maritime traffic situations, our agent always complies with the formalized legal rules and never collides while achieving a high goal-reaching rate during training and deployment. In contrast, vanilla and traffic rule-informed RL agents frequently violate traffic rules and collide even after training.

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Section: B) Motion Planning For Vesselsmentioning

confidence: 99%

Provable Traffic Rule Compliance in Safe Reinforcement Learning on the Open Sea

Krasowski,

Althoff

2024

IEEE Trans. Intell. Veh.

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“…For example, one line of research employs search-based algorithms based on motion primitives, e.g., rapidly-exploring random trees [28], [31], [32]. Other studies employ model predictive control (MPC) [25], [27], [33] to obtain an optimal control signal. In contrast to using search algorithms based on a finite amount of motion primitives, MPC directly optimizes the controller in the continuous state and input space.…”

Section: Related Workmentioning

confidence: 99%

Safe Reinforcement Learning for Urban Driving using Invariably Safe Braking Sets

Krasowski

Zhang

Althoff

2022

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

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Autonomous vehicles have to obey traffic rules. These rules are often formalized using temporal logic, resulting in constraints that are hard to solve using optimization-based motion planners. Reinforcement Learning (RL) is a promising method to find motion plans adhering to temporal logic specifications. However, vanilla RL algorithms are based on random exploration, which is inherently unsafe. To address this issue, we propose a provably safe RL approach that always complies with traffic rules. As a specific application area, we consider vessels on the open sea, which must adhere to the Convention on the International Regulations for Preventing Collisions at Sea (COLREGS). We introduce an efficient verification approach that determines the compliance of actions with respect to the COLREGS formalized using temporal logic. Our action verification is integrated into the RL process so that the agent only selects verified actions. In contrast to agents that only integrate the traffic rule information in the reward function, our provably safe agent always complies with the formalized rules in critical maritime traffic situations and, thus, never causes a collision.

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