Autonomous Surface Vehicle energy-efficient and reward-based path planning using Particle Swarm Optimization and Visibility Graphs

Krell, Evan; King, Scott A.; Carrillo, Luis Rodolfo García

doi:10.1016/j.apor.2022.103125

Cited by 39 publications

(17 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Step: the maximum range AF can move (5) Initialize the artifcial fsh population: N (6) Initialize the state of each AF: X i (7) while the path is not found do (8) for each AF do (9) fag � stateEvaluation() (10) case fag � prey: (11) X i (t + 1)←X i (t) + step towards best potential position (12) case fag � follow: (13) X i (t + 1)←X i (t) + step towards neighbor fish with better position (14) case fag � Swarm: (15) X i (t + 1)←X i (t) + step towards center of the fish swarm (16) case fag � random: (17) X i (t + 1)←X i (t) + step towards random direction (18) Mathematical Problems in Engineering and τ r are the control input of surge and yaw. Sat * represents the saturation function of the control signals, and it is described as follows [24]:…”

Section: Usv Model Designmentioning

confidence: 99%

“…Tis system was developed based on the integration of a novel B-spline data frame and a particle swarm optimization (PSO)-based solver engine. Krell et al [13] introduced the concept of opportunistic reward-based planning and applied PSO to the path planning problem. Te PSO is used to optimize routes that balance path efciency and reward, and simulations have been carried out based on real ocean current data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Path Planning For Unmanned Surface Vehicles Based On Modified Artificial Fish Swarm Algorithm With Local Optimizer

Wang

Zhao

Bai

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The appeal for safe navigation of autonomous surface vessels (ASVs) has deemed the path planning problem as an attractive research interest. However, most of the previous works to solve the path planning problem focus on finding the shortest and collision-free path, but the solutions are scarcely satisfied by the safety requirements and constraints related to the USVs’ mechanical systems. To address this challenge, we present a novel path planning method based on a modified artificial fish swarm algorithm in combination with a path optimizer. The modifications are made from two perspectives: (1) Four customized operators and an adaptive factor are applied to improve the convergence performance of the algorithm. (2) A local path optimizer is proposed to enhance its feasibility of cooperating with the USV control system. Path safety, path smoothness, and nonholonomic constraints are considered. The path planning benchmark experiments have demonstrated its superior performance in terms of efficiency and path quality compared to other state-of-the-art algorithms. Moreover, the proposed method is also integrated into the USV’s control system in a practical environment with satisfactory feasibility. The simulation results provide strong evidence that the proposed method can be regarded as a practical approach for USV path planning problems.

show abstract

Section: Usv Model Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Path Planning For Unmanned Surface Vehicles Based On Modified Artificial Fish Swarm Algorithm With Local Optimizer

Wang

Zhao

Bai

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…These algorithms offer a set of high-level strategies to search for solutions, allowing them to optimize paths while considering multiple objectives with a comparatively low computational burden. Considering the effects of currents, Krell et al [20] devised an improved PSO method implemented in visibility graphs. For the safe navigation of ships, a quasi-reflection-based PSO was proposed by Xue [21].…”

Section: Introductionmentioning

confidence: 99%

Physical Consistent Path Planning for Unmanned Surface Vehicles under Complex Marine Environment

Wang

Bai

Zhao

2023

JMSE

View full text Add to dashboard Cite

The increasing demand for safe and efficient maritime transportation has underscored the necessity of developing effective path-planning algorithms for Unmanned Surface Vehicles (USVs). However, the inherent complexities of the ocean environment and the non-holonomic properties of the physical system have posed significant challenges to designing feasible paths for USVs. To address these issues, a novel path planning framework is elaborately designed, which consists of an optimization model, a meta-heuristic solver, and a Clothoid-based path connector. First, by encapsulating the intricate nature of the ocean environment and ship dynamics, a multi-objective path planning problem is designed, providing a comprehensive and in-depth portrayal of the underlying mechanism. By integrating the principles of the candidate set random testing initialization and adaptive probability set, an enhanced genetic algorithm is devised to fully exploit the underlying optimization problem in constrained space, contributing to the global searching ability. Accounting for the non-holonomic constraints, the fast-discrete Clothoid curve is capable of maintaining and improving the continuity of the path curve, thereby promoting strong coordination between the planning and control modules. A thorough series of simulations and comparisons conducted in diverse ocean scenarios has conclusively demonstrated the effectiveness and superiority of the proposed path planning framework.

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

Autonomous Surface Vehicle energy-efficient and reward-based path planning using Particle Swarm Optimization and Visibility Graphs

Cited by 39 publications

References 25 publications

Path Planning For Unmanned Surface Vehicles Based On Modified Artificial Fish Swarm Algorithm With Local Optimizer

Path Planning For Unmanned Surface Vehicles Based On Modified Artificial Fish Swarm Algorithm With Local Optimizer

Physical Consistent Path Planning for Unmanned Surface Vehicles under Complex Marine Environment

Ship Formation Algorithm Based on the Leader–Follower Method

Contact Info

Product

Resources

About