The branching‐course model predictive control algorithm for maritime collision avoidance

Eriksen, Bjørn-Olav Holtung; Breivik, Morten; Wilthil, Erik F.; Flåten, Andreas L.; Brekke, Edmund

doi:10.1002/rob.21900

Cited by 58 publications

(39 citation statements)

References 37 publications

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“…The experimental setup was similar to the setup reported in Eriksen et al (2019b), using the Telemetron ASV from Maritime Robotics as the ownship and the Ocean Space Drone 1 (OSD1) from Kongsberg Seatex as the moving obstacle. In addition, virtual static obstacles, expanded with a padding radius, were used to emulate static obstacles.…”

Section: Methodsmentioning

confidence: 99%

“…In order to improve the robustness to noise on obstacle estimates, transitional cost is included in the objective function, which penalizes changing the planned trajectory from iteration to iteration. In Eriksen et al (2019b), a single transitional cost term is used, which introduces a cost if one selects a different speed and/or course than the one closest to the one selected in the previous iteration. Note that the trajectory prediction is based on sampling the possible acceleration of the vessel in the current iteration, which implies that the exact trajectory selected in the previous iteration may not exist in the current search space.…”

Section: Speed and Course Transitional Costsmentioning

confidence: 99%

“…The authors have performed a significant amount of work on the hybrid architecture in Figure 1, concerning e.g. model-based vessel controllers Breivik, 2017a, 2018), short-term COLAV (Eriksen et al, , 2019b, mid-level COLAV (Eriksen and Breivik, 2017b) and a high-level planner interfaced to the mid-level algorithm (Bitar et al, 2019). In an upcoming article (Eriksen et al, 2019a), we populate the hybrid architecture with algorithms including the BC-MPC algorithm discussed in this article, and demonstrate COLAV compliant with COLREGs rules 8 and 13-17 in simulations.…”

Section: Introductionmentioning

confidence: 99%

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Short-term ASV Collision Avoidance with Static and Moving Obstacles

Eriksen¹,

Breivik²

2019

MIC

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This article considers collision avoidance (COLAV) for both static and moving obstacles using the branchingcourse model predictive control (BC-MPC) algorithm, which is designed for use by autonomous surface vehicles (ASVs). The BC-MPC algorithm originally only considered COLAV of moving obstacles, so in order to make the algorithm also be able to avoid static obstacles, we introduce an extra term in the objective function based on an occupancy grid. In addition, other improvements are made to the algorithm resulting in trajectories with less wobbling. The modified algorithm is verified through fullscale experiments in the Trondheimsfjord in Norway with both virtual static obstacles and a physical moving obstacle. A radar-based tracking system is used to detect and track the moving obstacle, which enables the algorithm to avoid obstacles without depending on vessel-to-vessel communication. The experiments show that the algorithm is able to simultaneously avoid both static and moving obstacles, while providing clear and readily observable maneuvers. The BC-MPC algorithm is compliant with rules 8, 13 and 17 of the the International Regulations for Preventing Collisions at Sea (COLREGs), and favors maneuvers following rules 14 and 15.

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Section: Methodsmentioning

confidence: 99%

Section: Speed and Course Transitional Costsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Short-term ASV Collision Avoidance with Static and Moving Obstacles

Eriksen¹,

Breivik²

2019

MIC

Self Cite

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“…Raw penalty (26) where x is the distance to the obstacle, θ is the vessel-relative angle (azimuth angle), and v y is the velocity component in the direction towards the vessel. The scaling factor ζ v is given as a function of the angle θ and the velocity v y , such that the reward efficiently guides the agent towards COLREGscompliant behavior.…”

Section: )mentioning

confidence: 99%

COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle Using Deep Reinforcement Learning

et al. 2020

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Path Following and Collision Avoidance, be it for unmanned surface vessels or other autonomous vehicles, are two fundamental guidance problems in robotics. For many decades, they have been subject to academic study, leading to a vast number of proposed approaches. However, they have mostly been treated as separate problems, and have typically relied on non-linear first-principles models with parameters that can only be determined experimentally. The rise of deep reinforcement learning in recent years suggests an alternative approach: end-to-end learning of the optimal guidance policy from scratch by means of a trial-and-error based approach. In this article, we explore the potential of Proximal Policy Optimization, a deep reinforcement learning algorithm with demonstrated state-of-the-art performance on Continuous Control tasks, when applied to the dual-objective problem of controlling an autonomous surface vehicle in a COLREGs compliant manner such that it follows an a priori known desired path while avoiding collisions with other vessels along the way. Based on high-fidelity elevation and AIS tracking data from the Trondheim Fjord, an inlet of the Norwegian sea, we evaluate the trained agent's performance in challenging, dynamic real-world scenarios where the ultimate success of the agent rests upon its ability to navigate nonuniform marine terrain while handling challenging, but realistic vessel encounters.

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“…One issue to be considered is ship dynamics, which reflect real conditions. An interesting approach regarding ship dynamics is presented in [ 49 ], where the trajectory generation problem is divided into separate stages and ship dynamics are solved in the last stage. Our future research will concentrate on a method for incorporating ship dynamics in MBSA.…”

Section: Future Workmentioning

confidence: 99%

Beam Search Algorithm for Anti-Collision Trajectory Planning for Many-to-Many Encounter Situations with Autonomous Surface Vehicles

Koszelew

Karbowska-Chilinska

Ostrowski

et al. 2020

Sensors

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A single anti-collision trajectory generation problem for an “own” vessel only is significantly different from the challenge of generating a whole set of safe trajectories for multi-surface vehicle encounter situations in the open sea. Effective solutions for such problems are needed these days, as we are entering the era of autonomous ships. The article specifies the problem of anti-collision trajectory planning in many-to-many encounter situations. The proposed original multi-surface vehicle beam search algorithm (MBSA), based on the beam search strategy, solves the problem. The general idea of the MBSA involves the application of a solution for one-to-many encounter situations (using the beam search algorithm, BSA), which was tested on real automated radar plotting aid (ARPA) and automatic identification system (AIS) data. The test results for the MBSA were from simulated data, which are discussed in the final part. The article specifies the problem of anti-collision trajectory planning in many-to-many encounter situations involving moving autonomous surface vehicles, excluding Collision Regulations (COLREGs) and vehicle dynamics.

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The branching‐course model predictive control algorithm for maritime collision avoidance

Cited by 58 publications

References 37 publications

Short-term ASV Collision Avoidance with Static and Moving Obstacles

Short-term ASV Collision Avoidance with Static and Moving Obstacles

COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle Using Deep Reinforcement Learning

Beam Search Algorithm for Anti-Collision Trajectory Planning for Many-to-Many Encounter Situations with Autonomous Surface Vehicles

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