A novel path planning approach for smart cargo ships based on anisotropic fast marching

Yan, Xinping; Wang, Shu-wu; Ma, Feng; Liu, Yuan-chang; Wang, Jin

doi:10.1016/j.eswa.2020.113558

Cited by 35 publications

(13 citation statements)

References 37 publications

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“…In addition to our own work previously mentioned, other researchers have also applied this method to the path planning problem [ 50 , 51 , 52 , 53 , 54 , 55 ].…”

Section: Description Of the Approachmentioning

confidence: 99%

Path Planning and Collision Risk Management Strategy for Multi-UAV Systems in 3D Environments

López

Muñoz

Quevedo

et al. 2021

Sensors

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Multi-UAV systems are attracting, especially in the last decade, the attention of researchers and companies of very different fields due to the great interest in developing systems capable of operating in a coordinated manner in complex scenarios and to cover and speed up applications that can be dangerous or tedious for people: search and rescue tasks, inspection of facilities, delivery of goods, surveillance, etc. Inspired by these needs, this work aims to design, implement and analyze a trajectory planning and collision avoidance strategy for multi-UAV systems in 3D environments. For this purpose, a study of the existing techniques for both problems is carried out and an innovative strategy based on Fast Marching Square—for the planning phase—and a simple priority-based speed control—as the method for conflict resolution—is proposed, together with prevention measures designed to try to limit and reduce the greatest number of conflicting situations that may occur between vehicles while they carry out their missions in a simulated 3D urban environment. The performance of the algorithm is evaluated successfully on the basis of certain conveniently chosen statistical measures that are collected throughout the simulation runs.

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“…In addition to our own work previously mentioned, other researchers have also applied this method to the path planning problem [ 50 , 51 , 52 , 53 , 54 , 55 ].…”

Section: Description Of the Approachmentioning

confidence: 99%

Path Planning and Collision Risk Management Strategy for Multi-UAV Systems in 3D Environments

López

Muñoz

Quevedo

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Among these technologies, the applications based on reinforcement learning (RL) are the most promising, their application expandability and function portability have great research space (Brandon et al , 2020). Technology based on RL can fuse multi-party data information, and after comprehensive processing, it gives a more reasonable resource utilization and emergency treatment plan, so it is favored by research scholars (Luo et al , 2020; Yan et al , 2020). According to relevant survey data, the RL-based technology can increase the success rate of traffic rescue emergency handling events from 28% to 87% (Jackson et al , 2020; Gao and Ding, 2020).…”

Section: Introductionmentioning

confidence: 99%

A new path plan method based on hybrid algorithm of reinforcement learning and particle swarm optimization

Liu

Zhang

et al. 2021

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PurposeTo solve the path planning problem of the intelligent driving vehicular, this paper designs a hybrid path planning algorithm based on optimized reinforcement learning (RL) and improved particle swarm optimization (PSO).Design/methodology/approachFirst, the authors optimized the hyper-parameters of RL to make it converge quickly and learn more efficiently. Then the authors designed a pre-set operation for PSO to reduce the calculation of invalid particles. Finally, the authors proposed a correction variable that can be obtained from the cumulative reward of RL; this revises the fitness of the individual optimal particle and global optimal position of PSO to achieve an efficient path planning result. The authors also designed a selection parameter system to help to select the optimal path.FindingsSimulation analysis and experimental test results proved that the proposed algorithm has advantages in terms of practicability and efficiency. This research also foreshadows the research prospects of RL in path planning, which is also the authors’ next research direction.Originality/valueThe authors designed a pre-set operation to reduce the participation of invalid particles in the calculation in PSO. And then, the authors designed a method to optimize hyper-parameters to improve learning efficiency of RL. And then they used RL trained PSO to plan path. The authors also proposed an optimal path evaluation system. This research also foreshadows the research prospects of RL in path planning, which is also the authors’ next research direction.

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“…At present, most of the theories of collision-avoidance navigation are however focused on collision avoidance decision-making or planning. For collision avoidance decision-making or planning theories, there is mainly a divide into four kinds of methods, such as obstacle avoidance path planning based on an intelligent search algorithm [ 2 , 3 ], local path planning based on optimization theory [ 4 ], collision avoidance decision-making based on adaptive algorithm [ 5 ], and obstacle avoidance control method [ 6 ]. However, these methods only study the problem of collision avoidance decision-making or obstacle avoidance control individually, without fully considering the coupling of decision and control.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Collision Avoidance Framework of MASS Based on B-Spline and Optimal Decoupling Control

Zhang

Wang

Chui

et al. 2021

Sensors

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Real-time collision-avoidance navigation of autonomous ships is required by many application scenarios, such as carriage of goods by sea, search, and rescue. The collision avoidance algorithm is the core of autonomous navigation for Maritime autonomous surface ships (MASS). In order to realize real-time and free-collision under the condition of multi-ship encounter in an uncertain environment, a real-time collision avoidance framework is proposed using B-spline and optimal decoupling control. This framework takes advantage to handle the uncertain environment with limited sensing MASS which plans dynamically feasible, highly reliable, and safe feasible collision avoidance. First, owing to the collision risk assessment, a B-spline-based collision avoidance trajectory search (BCATS) algorithm is proposed to generate free-collision trajectories effectively. Second, a waypoint-based collision avoidance trajectory optimization is proposed with the path-speed decoupling control. Two benefits, a reduction of control cost and an improvement in the smoothness of the collision avoidance trajectory, are delivered. Finally, we conducted an experiment using the Electronic Chart System (ECS). The results reveal the robustness and real-time collision avoidance trajectory planned by the proposed collision avoidance system.

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A novel path planning approach for smart cargo ships based on anisotropic fast marching

Cited by 35 publications

References 37 publications

Path Planning and Collision Risk Management Strategy for Multi-UAV Systems in 3D Environments

Path Planning and Collision Risk Management Strategy for Multi-UAV Systems in 3D Environments

A new path plan method based on hybrid algorithm of reinforcement learning and particle swarm optimization

A Real-Time Collision Avoidance Framework of MASS Based on B-Spline and Optimal Decoupling Control

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