Navigation of unmanned marine vehicles in accordance with the rules of the road

Benjamin, Michael R.; Curcio, J. A.; Leonard, John J.; Newman, Paul

doi:10.1109/robot.2006.1642249

Cited by 100 publications

(47 citation statements)

References 22 publications

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“…Previous research in AUV behavior development has primarily focused on implementing behaviors for a single vehicle [10][11][12], such as a single AUV taking temperature readings during ocean trials. With access to larger numbers of autonomous craft, research is now needed to explore possible ways for these vehicles to interact [6,34], especially if the vehicles have different capabilities (such as an autonomous surface vehicle and an autonomous underwater vehicle, see [47]).…”

Section: Limitations Of Current Search Methodsmentioning

confidence: 99%

Autonomous cooperation of heterogeneous platforms for sea-based search tasks

et al. 2008

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Many current methods of search using autonomous marine vehicles do not adapt to changes in mission objectives or the environment. A cellular-decomposition-based framework for cooperative, adaptive search is proposed that allows multiple search platforms to adapt to changes in both mission objectives and environmental parameters. Software modules for the autonomy framework MOOS-IvP are described that implement this framework. Simulated and experimental results show that it is feasible to combine both pre-planned and adaptive behaviors to effectively search a target area.

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Section: Limitations Of Current Search Methodsmentioning

confidence: 99%

Autonomous cooperation of heterogeneous platforms for sea-based search tasks

et al. 2008

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“…Benjamin et al [7,8] evaluated single vehicle pairs while testing several COLREGS rules with a solution based on multi-objective optimization with interval programming. In-water validation was conducted, though the scope was limited to "canonical collision risk situations."…”

Section: Literature Review and Recent Researchmentioning

confidence: 99%

COLREGS-Compliant Autonomous Collision Avoidance Using Multi-Objective Optimization with Interval Programming

Woerner¹

2014

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High contact density environments are becoming ubiquitous in autonomous marine vehicle (AMV) operations. Safely managing these environments and their mission greatly taxes platforms. AMV collisions will likely increase as contact density increases. In situations where AMVs are not performing a collaborative mission but are using shared physical space such as multiple vehicles in the same harbor, a high demand exists for safe and e cient operation to minimize mission track deviations while preserving the safety and integrity of mission platforms. With no existing protocol for collision avoidance of AMVs, much e↵ort to date has focused on individual ad hoc collision avoidance approaches that are self-serving, lack the uniformity of fleet-distributed protocols, and disregard the overall fleet e ciency when scaled to being in a contact-dense environment. This research shows that by applying interval programming and a collision avoidance protocol such as the International Regulations for Prevention of Collisions at Sea (COLREGS) to a fleet of AMVs operating in the same geographic area, the fleet achieves nearly identical e ciency concurrent with significant reductions in the collisions observed. A basic collision avoidance protocol was analyzed against a COLREGS-based algorithm while parameters key to collision avoidance were studied using Monte Carlo methods and regression analysis of both real-world and simulated statistical data. A testing metric was proposed for declaring AMVs as "COLREGS-compliant" for at-sea operations. This work tested five AMVs simultaneously with COLREGS collision avoidance-the largest test known to date.

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“…They used a simple kinematic model with no environmental disturbances. Benjamin et al developed a technique for collision avoidance and navigation of the marine vehicles respecting the rules of the roads [63]. Soltan et al developed nonlinear sliding mode control based trajectory planner for a 3 DOF dynamics model [64].…”

Section: Physics-aware Trajectory Planningmentioning

confidence: 99%

GPU based generation of state transition models using simulations for unmanned surface vehicle trajectory planning

Thakur

Švec

Gupta

2012

Robotics and Autonomous Systems

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This paper describes GPU based algorithms to compute state transition model for unmanned surface vehicles (USVs) using 6 degree of freedom (DOF) dynamics simulations of vehicle-wave interaction. State transition model is a key component of Markov Decision Process (MDP), which is a natural framework to formulate the problem of trajectory planning under motion uncertainty. USV trajectory planning problem is characterized by the presence of large and somewhat stochastic forces due to ocean waves, which can cause significant deviations in their motion. Feedback controllers are often employed to reject disturbances and get back on the desired trajectory. However, the motion uncertainty can be significant and must be considered in the trajectory planning to avoid collisions with the surrounding obstacles. In case of USV missions, state transition probabilities need to be generated on-board, to compute trajectory plans that can handle dynamically changing USV parameters and environment (e.g., changing boat inertia tensor due to fuel consumption, variations in damping due to changes in water density, variations in sea-state, etc.). The 6 DOF dynamics simulations reported in this paper are based on potential flow theory. We also present a model simplification algorithm based on temporal coherence and its GPU implementation to accelerate simulation computation performance. Using the techniques discussed in this paper we were able to compute state transition probabilities in less than 10 minutes. Computed transition probabilities are subsequently used in a stochastic dynamic programming based approach to solve the MDP to obtain trajectory plan. Using this approach, we are able to generate dynamically feasible trajectories for USVs that exhibit safe behaviors in high sea-states in the vicinity of static obstacles.

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Navigation of unmanned marine vehicles in accordance with the rules of the road

Cited by 100 publications

References 22 publications

Autonomous cooperation of heterogeneous platforms for sea-based search tasks

Autonomous cooperation of heterogeneous platforms for sea-based search tasks

COLREGS-Compliant Autonomous Collision Avoidance Using Multi-Objective Optimization with Interval Programming

GPU based generation of state transition models using simulations for unmanned surface vehicle trajectory planning

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