Risk-based path planning for autonomous underwater vehicles in an oil spill environment

Chen, Xi; Bose, Neil; Brito, Mario; Khan, Faisal; Millar, Gina; Bulger, Craig; Zou, Ting

doi:10.1016/j.oceaneng.2022.113077

Cited by 9 publications

(3 citation statements)

References 33 publications

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“…However, the above method only ensures the safety of waypoints, and cannot guarantee the safety of trajectories generated according to waypoints. The method based on the risk index has been widely used on unmanned aerial vehicles [22], underwater vehicles [23], and ground mobile robots [24], but they still have some safety hazards.…”

Section: Related Workmentioning

confidence: 99%

A Global Trajectory Planning Framework Based on Minimizing the Risk Index

et al. 2023

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At present, autonomous mobile robots are widely used in industrial and commercial fields. However, although the global path searched by existing mobile robot path planning methods has no collision with obstacles, there is a problem in that the path is close to obstacles and is not smooth, and there is a collision safety risk when the robot is actually moving. To solve the above problems, this paper proposes a global path planning method based on minimizing the risk index. Firstly, the distance calculation method of the heuristic function of the traditional graph search algorithm is improved to reduce the number of nodes in the search space. Additionally, by selecting the appropriate search neighborhood, the search efficiency and path smoothness of the algorithm are improved. Thirdly, to increase the distance between the original search path and obstacles, the risk index path search strategy is proposed. Finally, the minimized snap trajectory smoothing method with a safe corridor is used to smooth the original waypoint. Both simulation and real robot experimental results show that the minimum distance between waypoints and obstacles is increased by 43.72% on average, and the number of trajectory inflection points are reduced by 75.12% on average after optimization. As such, the proposed method can fully guarantee safety and generate smooth mobile robot paths in global trajectory planning tasks.

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Section: Related Workmentioning

confidence: 99%

A Global Trajectory Planning Framework Based on Minimizing the Risk Index

et al. 2023

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“…Currently, the most commonly used global path-planning algorithms include Dijkstra [11][12][13], A* [14][15], genetic [16][17][18], particle swarm optimization [19][20][21], and ant colony algorithms [22]. Dijkstra and A* algorithms are used to solve the optimal path.…”

Section: Introductionmentioning

confidence: 99%

Ship Formation Algorithm Based on the Leader–Follower Method

Meng

Chen³

et al. 2023

IEEE Access

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In recent years, marine engineers have increasingly relied on multiship collaborative work to complete complex tasks at sea and improve maritime transport efficiency. Ship formation significantly contributes to the maintenance of stability during multiship-coordinated operations, but establishing the formation structure while ensuring the safety of each ship remains challenging. Therefore, this study proposes an improved leader-follower-based formation algorithm as a solution to this issue. First, the fast marching square method was used for global static path planning. The leader-follower formation control method then enabled the path tracking of the follower ship to the leader ship. Finally, an improved artificial potential field method was used for local collision avoidance, and virtual obstacles were added to improve the situation in which the ship probably entered a local minimum point. The experimental results revealed that the proposed algorithm could plan a route with time, smoothness, and safety advantages for the formation leader and quickly form a stable formation. Notably, the ship can quickly initiate collision avoidance operations when it encounters an obstacle posing a collision risk.INDEX TERMS Artificial potential field method, fast marching square method, formation of ships, leaderfollower method, local collision, path planning.

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“…Anderlini et al (2021) developed a remote fault detection system for Slocum gliders, which can diagnose a range of anomalies, such as wing loss, in near real time to improve safety performance. (Chen et al, 2022) proposed an offboard risk-based path planning approach for AUGs operating in an oil spill environment. A BN-based risk model was developed for probabilistic prediction of risk states given various environmental observations.…”

Section: Introductionmentioning

confidence: 99%

A copula‐based method of risk prediction for autonomous underwater gliders in dynamic environments

et al. 2023

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Autonomous underwater gliders (AUGs) are effective platforms for oceanic research and environmental monitoring. However, complex underwater environments with uncertainties could pose the risk of vehicle loss during their missions. It is therefore essential to conduct risk prediction to assist decision making for safer operations. The main limitation of current studies for AUGs is the lack of a tailored method for risk analysis considering both dynamic environments and potential functional failures of the vehicle. Hence, this study proposed a copula‐based approach for evaluating the risk of AUG loss in dynamic underwater environments. The developed copula Bayesian network (CBN) integrated copula functions into a traditional Bayesian belief network (BBN), aiming to handle nonlinear dependencies among environmental variables and inherent technical failures. Specifically, potential risk factors with causal effects were captured using the BBN. A Gaussian copula was then employed to measure correlated dependencies among identified risk factors. Furthermore, the dependence analysis and CBN inference were performed to assess the risk level of vehicle loss given various environmental observations. The effectiveness of the proposed method was demonstrated in a case study, which considered deploying a Slocum G1 Glider in a real water region. Risk mitigation measures were provided based on key findings. This study potentially contributes a tailored tool of risk prediction for AUGs in dynamic environments, which can enhance the safety performance of AUGs and assist in risk mitigation for decision makers.

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Risk-based path planning for autonomous underwater vehicles in an oil spill environment

Cited by 9 publications

References 33 publications

A Global Trajectory Planning Framework Based on Minimizing the Risk Index

A Global Trajectory Planning Framework Based on Minimizing the Risk Index

Ship Formation Algorithm Based on the Leader–Follower Method

A copula‐based method of risk prediction for autonomous underwater gliders in dynamic environments

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