MPC-Based mid-level collision avoidance for asvs using nonlinear programming

Eriksen, Bjørn-Olav Holtung; Breivik, Morten

doi:10.1109/ccta.2017.8062554

Cited by 35 publications

(24 citation statements)

References 13 publications

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“…The first term penalizes energy usage and describes work done by the actuators, while the second term is a disproportionate penalization on turn-rate r, and prefers readily Step 1 Step 2 Step 3 observable turns performed with high turn-rate. The idea for the turn-rate penalization is obtained from (Eriksen and Breivik, 2017). The same cost-to-go function is used in (Bitar et al, 2019).…”

Section: Optimal Control Problemmentioning

confidence: 99%

Warm-Started Optimized Trajectory Planning for ASVs

et al. 2019

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We present improvements to a recently developed method for trajectory planning for autonomous surface vehicles (ASVs) in terms of run time. The original method combines two types of planners: An A implementation that quickly finds the global shortest piecewise linear path on a uniformly discretized map, and an optimal control-based trajectory planner which takes into account ASV dynamics. Firstly, we propose an improvement to the discretization of the map by switching to a Voronoi diagram rather than the uniform discretization, which offers a far more sparse search tree for the A implementation. Secondly, modifications to the path refinement are made, as suggested in a paper by Bhattacharya and Gavrilova. The changes result in a reduction to the run time of the first part of the method of 85 % for an example scenario while maintaining the same level of optimality.

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Section: Optimal Control Problemmentioning

confidence: 99%

Warm-Started Optimized Trajectory Planning for ASVs

et al. 2019

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“…In this paper, we demonstrate the three-layered hybrid COLAV shown in Figure 1 by combining and extending the COLAV algorithms developed in (Bitar et al, 2019a;Eriksen and Breivik, 2017b;Bitar et al, 2019b;. The high-level planner has a long temporal horizon, and finds an energy-optimized nominal trajectory from an initial to a goal position considering static obstacles.…”

Section: Introductionmentioning

confidence: 99%

“…The authors have previously worked extensively on different components of this architecture. Examples include high-level COLAV algorithms (Bitar et al, 2018 , 2019b ), a mid-level algorithm (Eriksen and Breivik, 2017b ; Bitar et al, 2019a ), short-term algorithms (Eriksen et al, 2018 , 2019 ; Eriksen and Breivik, 2019 ) and the development of high-performance vessel controllers (Eriksen and Breivik, 2017a , 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Hybrid Collision Avoidance for ASVs Compliant With COLREGs Rules 8 and 13–17

et al. 2020

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This paper presents a three-layered hybrid collision avoidance (COLAV) system for autonomous surface vehicles, compliant with rules 8 and 13-17 of the International Regulations for Preventing Collisions at Sea (COLREGs). The COLAV system consists of a high-level planner producing an energy-optimized trajectory, a model predictive control based mid-level COLAV algorithm considering moving obstacles and the COLREGs, and the branching-course model predictive control algorithm for short-term COLAV handling emergency situations in accordance with the COLREGs. Previously developed algorithms by the authors are used for the high-level planner and short-term COLAV, while we in this paper further develop the mid-level algorithm to make it comply with COLREGs rules 13-17. This includes developing a state machine for classifying obstacle vessels using a combination of the geometrical situation, the distance and time to the closest point of approach (CPA) and a new CPA-like measure. The performance of the hybrid COLAV system is tested through numerical simulations for three scenarios representing a range of different challenges, including multi-obstacle situations with multiple simultaneously active COLREGs rules, and also obstacles ignoring the COLREGs. The COLAV system avoids collision in all the scenarios, and follows the energy-optimized trajectory when the obstacles do not interfere with it. Keywords: Hybrid collision avoidance, Autonomous surface vehicle (ASV), COLREGs, COLREGs compliant, Model predictive control (MPC), Energy-optimized control arXiv:1907.00198v2 [eess.SY] 14 Jul 20192018, Falco navigated autonomously between two ports in Finland 2 . Reports state that in excess of 75 % of maritime accidents are due to human errors (Chauvin, 2011;Levander, 2017), indicating that there is also a potential for increased safety in addition to the economical and environmental benefits.An obvious prerequisite for autonomous ship operations is the development of robust and wellfunctioning collision avoidance (COLAV) systems. In addition to generating collision-free maneuvers, a COLAV system must adhere to the "rules of the road" of the oceans, i.e. the International Regulations for Preventing Collisions at Sea (COLREGs) (Cockcroft and Lameijer, 2004). These rules are written for human ship operators and include qualitative requirements on how to perform safe and readily observable maneuvers. Part B of the COLREGs concern steering and sailing, and includes the following rules that are the most relevant to a motion control system: Rule 8Requires maneuvers to be readily observable and to be done in ample time. Rules 13-15 Describe the maneuvers to perform in cases of overtaking, head-on and crossing situations. Participants in crossing situations are defined by the terms give-way and stand-on vessels. Rule 16Requires that a give-way vessel must take early and substantial action to keep clear of the stand-on vessel. Rule 17Consists of two main parts. The first part requires a stand-on vessel to maintain its course and speed, whi...

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“…This layer performs short‐time COLAV making sure to avoid obstacles performing sudden maneuvers or which are detected too late to be handled by the mid‐level algorithm, while also ensuring that the maneuvers are feasible with respect to the dynamic constraints of the vessel. The short‐term layer can also act as a backup solution to avoid collisions in cases where the mid‐level algorithm fails to produce feasible trajectories, for instance, due to time constraints or numerical issues (Eriksen & Breivik, ). Furthermore, the short‐term layer should be able to avoid collision in emergency situations, for example, when obstacles do not maneuver in accordance with COLREGs.…”

Section: Introductionmentioning

confidence: 99%

“…Model predictive control (MPC) has for a long time been a well‐known and proven tool for motion planning and COLAV for, for example, ground and automotive robots (Gray, Ali, Gao, Hedrick, & Borrelli, ; Keller, Haß, Seewald, & Bertram, ; Ögren & Leonard, ), aerospace applications (Kuwata & How, ), and underwater vehicles (Caldwell, Dunlap, & Collins, ). In the later years, MPC has also been applied for COLAV in the maritime domain, both using sample‐based approaches where one considers a finite space of control inputs (Hagen, Kufoalor, Brekke, & Johansen, ; Johansen, Perez, & Cristofaro, ; Švec et al, ) and conventional gradient‐based search algorithms (Abdelaal & Hahn, ; Eriksen & Breivik, ). None of these algorithms does, however, consider the amounts of noise which we expect to encounter using a radar‐based tracking system.…”

Section: Introductionmentioning

confidence: 99%

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

Eriksen

Breivik

Wilthil

et al. 2019

Journal of Field Robotics

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This article presents a new algorithm for short-term maritime collision avoidance (COLAV) named the branching-course model predictive control (BC-MPC) algorithm. The algorithm is designed to be robust with respect to noise on obstacle estimates, which is a significant source of disturbance when using exteroceptive sensors such as, for example, radars for obstacle detection and tracking. Exteroceptive sensors do not require vesselto-vessel communication, which enables COLAV toward vessels not equipped with, for example, automatic identification system transponders, in addition to increasing the robustness with respect to faulty information which may be provided by other vessels. The BC-MPC algorithm is compliant with Rules 8, 13, and 17 of the International Regulations for Preventing Collisions at Sea (COLREGs), and favors maneuvers following Rules 14 and 15. Specifically, the algorithm can ignore the specific maneuvering regulations of Rules 14 and 15, which may be required in situations where Rule 17 revokes a stand-on obligation. The algorithm is experimentally validated in several fullscale experiments in the Trondheimsfjord in 2017 using a radar-based system for obstacle detection and tracking. To complement the experimental results, we present simulations where the BC-MPC algorithm is tested in more complex scenarios involving multiple obstacles and several simultaneously active COLREGs rules. The COLAV experiments and simulations show good performance. K E Y W O R D S control, marine robotics, planning

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MPC-Based mid-level collision avoidance for asvs using nonlinear programming

Cited by 35 publications

References 13 publications

Warm-Started Optimized Trajectory Planning for ASVs

Warm-Started Optimized Trajectory Planning for ASVs

Hybrid Collision Avoidance for ASVs Compliant With COLREGs Rules 8 and 13–17

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

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