Motion planning for multi-HUG formation in an environment with obstacles

Yang, Yan; Wang, Shuxin; Wu, Zhilin; Wang, Yanhui

doi:10.1016/j.oceaneng.2011.10.008

Cited by 46 publications

(18 citation statements)

References 20 publications

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“…A distributed control method [21] based on artificial potential fields (APFs) and virtual leaders was introduced for a group of underwater vehicles to coordinate motion and construct geometry. Combination of the APFs method and the Kane's method was researched by Yang et al [22] to achieve coordinate motion planning for Multi-HUG formation in an environment with obstacles. Ren et al [23] proposed an approach based on fuzzy concept to solve coordination problems of multiple gliders, which considers influence of the surrounding environment.…”

Section: Open Accessmentioning

confidence: 99%

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Coordinate Control, Motion Optimization and Sea Experiment of a Fleet of Petrel-II Gliders

Xue

Wang

et al. 2018

Chin. J. Mech. Eng.

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The formation of hybrid underwater gliders has advantages in sustained ocean observation with high resolution and more adaptation for complicated ocean tasks. However, the current work mostly focused on the traditional gliders and AUVs. The research on control strategy and energy consumption minimization for the hybrid gliders is necessary both in methodology and experiment. A multi-layer coordinate control strategy is developed for the fleet of hybrid underwater gliders to control the gliders' motion and formation geometry with optimized energy consumption. The inner layer integrated in the onboard controller and the outer layer integrated in the ground control center or the deck controller are designed. A coordinate control model is proposed based on multibody theory through adoption of artificial potential fields. Considering the existence of ocean flow, a hybrid motion energy consumption model is constructed and an optimization method is designed to obtain the heading angle, net buoyancy, gliding angle and the rotate speed of screw propeller to minimize the motion energy with consideration of the ocean flow. The feasibility of the coordinate control system and motion optimization method has been verified both by simulation and sea trials. Simulation results show the regularity of energy consumption with the control variables. The fleet of three Petrel-II gliders developed by Tianjin University is deployed in the South China Sea. The trajectory error of each glider is less than 2.5 km, the formation shape error between each glider is less than 2 km, and the difference between actual energy consumption and the simulated energy consumption is less than 24% actual energy. The results of simulation and the sea trial prove the feasibility of the proposed coordinate control strategy and energy optimization method. In conclusion, a coordinate control system and a motion optimization method is studied, which can be used for reference in theoretical research and practical fleet operation for both the traditional gliders and hybrid gliders.

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Section: Open Accessmentioning

confidence: 99%

“…Motion simulation results of a three-glider fleet motion controlled by this method has been achieved in our early work (more details see Refs. [22,33]). …”

Section: Multibody System Model Based On Kane's Equationmentioning

confidence: 99%

Coordinate Control, Motion Optimization and Sea Experiment of a Fleet of Petrel-II Gliders

Xue

Wang

et al. 2018

Chin. J. Mech. Eng.

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“…Kreuger [22] then replaced the single term cost-function with one that that incorporates a mixture of various linear terms, including energy, obstacle regions, distance, time and excess travel speed. Use of the artificial potential field cost-terms allows fast convergence and is easy to apply to irregularly shaped obstacles [23].…”

Section: Optimization Algorithms For Planningmentioning

confidence: 99%

“…The marine environment poses a rich field of challenges for AUV path planning systems, such as ocean currents, irregularly shaped terrains and dynamic and uncertain obstacles [23,29]. Dynamic path planning is a continual process which generates a new path by adapting previously computed paths for onwards travel.…”

Section: Problem Formulationmentioning

confidence: 99%

Efficient Path Re-planning for AUVs Operating in Spatiotemporal Currents

Zeng

Sammut

Lammas

et al. 2014

J Intell Robot Syst

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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.

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“…In recent years, researchers start applying the APF technique in formation control of MAS. For example, a method combining the APF technique with Kane's method has been proposed to solve the multi-hybrid-driven underwater glider motion planning and obstacle avoidance problem [7]. Similarly, Asl et al [8] has used another technique, called Asexual Reproduction Optimization, which is based on the APF and is combined with the leader-follower formation method to formulate the path planning problem.…”

Section: Introductionmentioning

confidence: 99%

Formation control for multiple unmanned aerial vehicles in constrained space using modified artificial potential field

Yin¹,

Cam²,

Roy³

2017

MMEP

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This paper addresses a formation tracking control issue for multiple Unmanned Aerial Vehicles (UAV) flying through a constrained space. Based on the Formation Potential Field (FPF), a Modified Artificial Potential Field (MAPF) technique is proposed, which guarantees to generate and maintain a given formation while avoiding collisions. The technique has two phases. UAVs are gathered around the formation center first during Phase 1, and the formation is then achieved in Phase 2. Furthermore, an obstacle repulsive potential field is introduced into this approach to deal with collision avoidance under environmental constraints. By doing so, discontinuities of the original Formation Potential Field can be avoided, and the stability of formation generation could be enhanced. Simulation results are presented to illustrate the validity of proposed approach.

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Motion planning for multi-HUG formation in an environment with obstacles

Cited by 46 publications

References 20 publications

Coordinate Control, Motion Optimization and Sea Experiment of a Fleet of Petrel-II Gliders

Coordinate Control, Motion Optimization and Sea Experiment of a Fleet of Petrel-II Gliders

Efficient Path Re-planning for AUVs Operating in Spatiotemporal Currents

Formation control for multiple unmanned aerial vehicles in constrained space using modified artificial potential field

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