UUV’s Hierarchical DE-Based Motion Planning in a Semi Dynamic Underwater Wireless Sensor Network

Abstract

This paper describes a reflexive multilayered mission planner with a mounted energy efficient local path planner for unmanned underwater vehicle's (UUV) navigation throughout complex subsea volume in a time variant semi-dynamic operation network. The UUV routing protocol in underwater wireless sensor network is generalized with a homogeneous dynamic knapsack-traveler salesman problem emerging with an adaptive path planning mechanism to address UUV's long-duration missions on dynamically changing subsea volume. The framework includes a base layer of global path planning, an inner layer of local path planning and an environmental sublayer. Such a multilayer integrated structure facilitates the framework to adopt any algorithm with real-time performance. The evolutionary technique known as differential evolution (DE) algorithm is employed by both base and inner layers to examine the performance of the framework in efficient mission timing and its resilience against the environmental disturbances. Relying on reactive nature of the framework and fast computational performance of the DE algorithm, the simulations show promising results and this new framework guarantees a safe and efficient deployment in a turbulent uncertain marine environment passing through a proper sequence of stations considering various constraint in a complex environment.

Current advancements on autonomous mission planning and management systems: An AUV and UAV perspective

“…MahmoudZadeh [28] defined mission planning as a joint problem of discrete natured task priority assignment and continuous natured vehicle routing toward the destination in graph-like terrain. They mapped the distributed tasks in a specific operation area by a network, which took the complication of the vehicle routing in a graph-like terrain into account.…”

Differential Evolution in Wireless Communications: A Review

“…As seen in the review, most of the optimisation problems in wireless networks are multi-objective and multimodal in nature, of which DE was applied to obtain the desired solutions. It appears that coverage area maximisation and energy consumption minimisation are the two major areas where Differential Evolution is applied in wireless networks and communications, which was part of the submission of [190]. The networks are usually modelled as a multi-objective optimisation problem where variables are optimised using some constraints.…”

Exploiting a fleet of UAVs for monitoring and data acquisition of a distributed sensor network

“…This is a complex NP-hard optimization problem with a strict time restriction of which solving is a nontrivial process that brute force algorithms might not be computationally fast enough to tackle the real-time action-reaction requirements of the problem [14]. Currently, no polynomial-time algorithm is known yet to solve such a complex NP-hard problem [15], and meta-heuristics are proposed as viable candidates to deal with the complexity of these problems as suggested by numerous studies in the area [16,17].…”