Path planning of UUV based on HQPSO algorithm with considering the navigation error

Ma, Yiwei; Feng, Wei; Mao, Zhaoyong; Li, Heng; Meng, Xiangyao

doi:10.1016/j.oceaneng.2021.110048

Cited by 11 publications

(1 citation statement)

References 3 publications

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“…An improved sparrow search algorithm for path-planning for a mobile robot is reported in [ 46 ]. Furthermore, hybrid Quantum-behaved Particle Swarm Optimization (HQPSO), a variant of classical PSO, has been used for path generation for an unmanned underwater vehicle (UUV) [ 47 ]. Modified ant-colony optimization (ACO) is used for path planning for AGV-based parking system is detailed in [ 48 ].…”

Section: Related Workmentioning

confidence: 99%

A Novel Path Planning Strategy for a Cleaning Audit Robot Using Geometrical Features and Swarm Algorithms

Pathmakumar

Muthugala

Samarakoon

et al. 2022

Sensors

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Robot-aided cleaning auditing is pioneering research that uses autonomous robots to assess a region’s cleanliness level by analyzing the dirt samples collected from various locations. Since the dirt sample gathering process is more challenging, adapting a coverage planning strategy from a similar domain for cleaning is non-viable. Alternatively, a path planning approach to gathering dirt samples selectively at locations with a high likelihood of dirt accumulation is more feasible. This work presents a first-of-its-kind dirt sample gathering strategy for the cleaning auditing robots by combining the geometrical feature extraction and swarm algorithms. This combined approach generates an efficient optimal path covering all the identified dirt locations for efficient cleaning auditing. Besides being the foundational effort for cleaning audit, a path planning approach considering the geometric signatures that contribute to the dirt accumulation of a region has not been device so far. The proposed approach is validated systematically through experiment trials. The geometrical feature extraction-based dirt location identification method successfully identified dirt accumulated locations in our post-cleaning analysis as part of the experiment trials. The path generation strategies are validated in a real-world environment using an in-house developed cleaning auditing robot BELUGA. From the experiments conducted, the ant colony optimization algorithm generated the best cleaning auditing path with less travel distance, exploration time, and energy usage.

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Section: Related Workmentioning

confidence: 99%