Application of Improved Particle Swarm Optimization for Navigation of Unmanned Surface Vehicles

Abstract

Multi-sensor fusion for unmanned surface vehicles (USVs) is an important issue for autonomous navigation of USVs. In this paper, an improved particle swarm optimization (PSO) is proposed for real-time autonomous navigation of a USV in real maritime environment. To overcome the conventional PSO’s inherent shortcomings, such as easy occurrence of premature convergence and human experience-determined parameters, and to enhance the precision and algorithm robustness of the solution, this work proposes three optimization strategies: linearly descending inertia weight, adaptively controlled acceleration coefficients, and random grouping inversion. Their respective or combinational effects on the effectiveness of path planning are investigated by Monte Carlo simulations for five TSPLIB instances and application tests for the navigation of a self-developed unmanned surface vehicle on the basis of multi-sensor data. Comparative results show that the adaptively controlled acceleration coefficients play a substantial role in reducing the path length and the linearly descending inertia weight help improve the algorithm robustness. Meanwhile, the random grouping inversion optimizes the capacity of local search and maintains the population diversity by stochastically dividing the single swarm into several subgroups. Moreover, the PSO combined with all three strategies shows the best performance with the shortest trajectory and the superior robustness, although retaining solution precision and avoiding being trapped in local optima require more time consumption. The experimental results of our USV demonstrate the effectiveness and efficiency of the proposed method for real-time navigation based on multi-sensor fusion.

Multi-Subdomain Grouping-Based Particle Swarm Optimization for the Traveling Salesman Problem

“…First, three of our PSO or GA-based algorithms are selected for comparison with the MSGPSO: a multi-domain inversionbased GA (MDIGA) [42], an improved PSO with adaptively adjusted acceleration coefficients and inertia weight (AWIPSO) [41], and a greedy strategy-based PSO (GSPSO) (abbreviated as IPSO in [32]). It is worth noting that this study has certain connections and essential differences with the three recently published works.…”

“…Other parameters, e.g., acceleration coefficients, inertia weight and mutation probability, were investigated in our previous work. The choice that yield good performance was also presented [41,42]. Firstly, the values of acceleration coefficients and inertia weight are set same according to our previous study.…”

Autonomous Navigation of a Team of Unmanned Surface Vehicles for Intercepting Intruders on a Region Boundary

“…Intercepting an intruder or several intruders is the objective of the team of mobile robots, in a way that at least one robot must be close to the intrusion point when the intruder is about to cross the boundary of the protected region. Moreover, in this paper we concentrate on navigation of a team of autonomous unmanned surface vehicles (USVs); see, for example [ 12 , 13 ]. Real life examples of the investigated problem are various asset guarding problems.…”

Construction cost minimisation of the stepped spillway using improved particle swarm optimisation and artificial bee colony algorithms

“…As was discussed, only a few researches have been carried out in the second category of this research using effective methods such as meta‐heuristic methods. Therefore, ABC, PSO and their improved versions (IPSO and IABC) are used here to minimize the construction cost of stepped spillway due to the unique characteristics of these algorithms in which they were presented in different papers to solve various large‐scale engineering problems (Garg, 2016; Patwal et al , 2018; Cao et al , 2019; Latchoumi et al, 2019; Fang and Popole, 2019; Latif and Saka, 2019; Mann and Singh, 2019; Moeini and Soghrati, 2019, Xin et al , 2019).…”