Kinematic simulative analysis of virtual potential field method for AUV trajectory planning

Abstract

This paper deals with an analysis of applicability, capabilities, benefits and pitfalls of using a virtual potential field approach to autonomously planning trajectories in non-communicating autonomous underwater vehicles (AUV-s). Virtual potentials represent an approach to this problem with cross-layer design features. Examples of different layers of control that can be achieved with the same fundamental approach are: obstacle-avoidance, energy-optimal trajectories, forming up with other moving agents, controlled formation fragmentation into well-posed sub-formation etc. This paper shows, on the basis of extensive simulated experiments, that such a trajectory planner based on virtual potentials, guarantees good extendibility, scalability and performance in a hard-real-time hardware-in-theloop system.

Auv Formations Achieved by Virtual Potentials Trajectory Planning in a Simulated Environment

“…A trajectory planner for AUVs must be stable, energy efficient, have robust, certifiable and tested collision avoidance. In the context of multiple AUV working and moving in concert, a coordinated trajectory planner must in addition rely as much as possible on sensing rather than on communication (Barisic et al 2006(Barisic et al , 2007a(Barisic et al , 2007b. Implementation-wise, such a trajectory planner must be implemented as well designed, safe, fast, bug-free code that is multithreaded.…”

“…However, such a trajectory planning method, running on embedded hardware as but one of the many hardreal-time modules of a hierarchical control system [ref], draws up high levels of processor commitment In order to reduce the number of layers in such a hierarchical system and to dispense with the greatest number of interdependencies and links which would have to be periodically checked and thereby induce overhead in the processor cycle, the trajectory planner should possess cross-level design features. Therefore, a trajectory planner based on the virtual potential method was developed by Barisic et. al (2006Barisic et.…”

“…Therefore, a trajectory planner based on the virtual potential method was developed by Barisic et. al (2006Barisic et. al ( , 2007aBarisic et.…”

“…Section 2 defines the additions to the mathematics of the method in (Barisic et al 2006(Barisic et al , 2007a(Barisic et al , 2007b in order to assure forming behavior, proposing two solutions to the formation problem. Section 3 presents the results of simulations in comparison with the action of the algorithm in (Barisic et al 2006(Barisic et al , 2007a(Barisic et al , 2007b which does not explicitly include formation support, and the comparison of the two methods, and also proposes avenues for further research by presenting challenges and instances of suboptimal behavior. Section 4 concludes the paper.…”

Formation Guidance of AUVs Using Decentralized Control Functions

“…A halting P-complete algorithm is introduced that for every x 0 ∈C Σ i 0 , triggering at t 0 with ε(t 0 )=sup x(t > t 0 ) − x ∞ | x 0 characterizing a ε-ball centered on the particular x ∞ and containing all x(t > t 0 ), to intervene in E(W i ) guaranteeing that this entire ball is outside (a possibly existing) new C Σ ′ i 0 (with x ′ 0 ← x(t 0 )). Out of the two listed strategies for dealing with local minima, the authors have published extensively on strategy 2 (Barisic et al, 2007a), (Barisic et al, 2007b). However, strategy 1 represents a much more robust and general approach.…”