Phase Angle-Encoded and Quantum-Behaved Particle Swarm Optimization Applied to Three-Dimensional Route Planning for UAV

Fu, Yangguang; Ding, Mingyue; Zhou, Chen

doi:10.1109/tsmca.2011.2159586

Cited by 216 publications

(82 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Particle Swarm Optimization (Zheng et al 2005) and Genetic Algorithm (Nikolos et al2003;Zheng et al 2005;Kumar and Kumar 2010) are two popular types of optimization algorithms applied successfully in path planning application. Fu et al (2012) employed Quantum-based PSO (QPSO) for unmanned aerial vehicle path planning, but implemented only off-line path planning in a static and known environment, which is far from reality. Subsequently, this algorithm was employed by Zeng et al, (2014-a;2014-b) for on-line AUV path planning in a dynamic marine environment.…”

Section: Path/trajectory Planning Approachesmentioning

confidence: 99%

A novel versatile architecture for autonomous underwater vehicle’s motion planning and task assignment

Zadeh¹,

Powers²,

Sammut³

et al. 2016

Soft Comput

View full text Add to dashboard Cite

Expansion of today's underwater scenarios and missions necessitates the requestion for robust decision making of the Autonomous Underwater Vehicle (AUV); hence, design an efficient decision making framework is essential for maximizing the mission productivity in a restricted time. This paper focuses on developing a deliberative conflict-free-task assignment architecture encompassing a Global Route Planner (GRP) and a Local Path Planner (LPP) to provide consistent motion planning encountering both environmental dynamic changes and a priori knowledge of the terrain, so that the AUV is reactively guided to the target of interest in the context of an uncertain underwater environment. The architecture involves three main modules: The GRP module at the top level deals with the task priority assignment, mission time management, and determination of a feasible route between start and destination point in a large scale environment. The LPP module at the lower level deals with safety considerations and generates collision-free optimal trajectory between each specific pair of waypoints listed in obtained global route. Re-planning module tends to promote robustness and reactive ability of the AUV with respect to the environmental changes. The experimental results for different simulated missions, demonstrate the inherent robustness and drastic efficiency of the proposed scheme in enhancement of the vehicles autonomy in terms of mission productivity, mission time management, and vehicle safety.

show abstract

Section: Path/trajectory Planning Approachesmentioning

confidence: 99%

A novel versatile architecture for autonomous underwater vehicle’s motion planning and task assignment

Zadeh¹,

Powers²,

Sammut³

et al. 2016

Soft Comput

View full text Add to dashboard Cite

show abstract

“…The local version of OLPSO (OLPSO-L) outperforms the global version. Furthermore, Fu et al (2012) introduced another novel PSO variant, namely, phase angleencoded QPSO (PAE-QPSO). This variant expresses the particle position as a phase angle vector.…”

Section: Pso Variants and Improvementsmentioning

confidence: 99%

Particle swarm optimization with dual-level task allocation

Lim

Isa

2015

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

“…Quantumbehaved Particle Swarm Optimization (QPSO) is recognized as an improved version of the original PSO, however, it differs in that QPSO assumes that every particle in the swarm has quantum behaviour instead of using the conventional position and velocity update rules employed in PSO. Fu [28] applied the QPSO for path planning and showed that it has superior performance compared to the standard PSO and GA algorithms. However, Fu's work, which is focussed on unmanned aerial vehicle applications, does not consider ocean current information and its effects on an AUV's ability to successfully complete its mission.…”

Section: Optimization Algorithms For Planningmentioning

confidence: 99%

Efficient Path Re-planning for AUVs Operating in Spatiotemporal Currents

Zeng

Sammut

Lammas

et al. 2014

J Intell Robot Syst

View full text Add to dashboard Cite

This paper presents an on-line dynamic path re-planning system for an autonomous underwater vehicle (AUV) to enable it to operate efficiently in a spatiotemporal, cluttered, and uncertain environment. The proposed strategy combines path re-planning with an evolutionary algorithm to adapt and regenerate the trajectory during the course of the mission using continuously updated current profiles from on-board sensors, such as a Horizontal Acoustic Doppler Velocity Logger. A quantumbehaved particle swarm optimization (QPSO) algorithm is used with a cost function which is based on the total time required to travel along the path segments accounting for the effect of space-time variable currents. The proposed path planner is designed to generate an optimal trajectory for an AUV navigating through a spatiotemporal ocean environment in the presence of irregularly shaped terrains as well as obstacles whose position coordinates are uncertain. Simulation results show that using the same on-board computation resources, the proposed path re-planning methodology with reuse of information gained from the previous planning history is able to obtain a more optimized trajectory than one relying on reactive path planning. Subsets of representative Monte Carlo simulations were run to analyse the performance of these dynamic planning systems. The results demonstrate the inherent robustness and superiority of the proposed planner based on path re-planning scheme when compared with the reactive path planning scheme.

show abstract

Phase Angle-Encoded and Quantum-Behaved Particle Swarm Optimization Applied to Three-Dimensional Route Planning for UAV

Cited by 216 publications

References 53 publications

A novel versatile architecture for autonomous underwater vehicle’s motion planning and task assignment

A novel versatile architecture for autonomous underwater vehicle’s motion planning and task assignment

Particle swarm optimization with dual-level task allocation

Efficient Path Re-planning for AUVs Operating in Spatiotemporal Currents

Contact Info

Product

Resources

About