A Bionic Social Learning Strategy Pigeon-Inspired Optimization for Multi-Unmanned Aerial Vehicle Cooperative Path Planning

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(2), P. 910 - 910

Published: Jan. 17, 2025

This paper proposes a bionic social learning strategy pigeon-inspired optimization (BSLSPIO) algorithm to tackle cooperative path planning for multiple unmanned aerial vehicles (UAVs) with detection. Firstly, modified (PIO) is proposed, which incorporates strategy. In this modification, the global best replaced by average of top-ranked solutions in map and compass operator, while center local landmark operator. The also proves algorithm’s convergence provides complexity analysis. Comparison experiments demonstrate that proposed method searches optimal solution guaranteeing fast convergence. Subsequently, path-planning model, detection units’ network cost estimation are constructed. developed BSLSPIO utilized generate feasible paths UAVs, adhering time consistency constraints. simulation results show generates at minimum effectively solves UAVs’ problem.

Language: Английский

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Path planning for mobile robots in complex environments based on enhanced sparrow search algorithm and dynamic window approach

Robotica, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 24

Published: May 16, 2025

Abstract Traditional path planning algorithms often encounter challenges in complex dynamic environments, including local optima, excessive lengths, and inadequate obstacle avoidance. Thus, the development of innovative is essential. This article addresses mobile robot where traditional methods converge to leading suboptimal struggle with To overcome these limitations, we propose an integrated algorithm, enhanced sparrow search algorithm combined window approach (ESSA-DWA). The first utilizes ESSA for global planning, followed by facilitated DWA. Specifically, incorporates Tent chaotic initialization enhance population diversity, effectively mitigating risk premature convergence optima. Moreover, adjustments inertia weight during process enable adaptive balance between exploration exploitation. integration a strategy further refines individual updates, thereby improving performance. smoothness, Floyd employed optimization, ensuring more continuous trajectory. Finally, combination DWA uses key nodes from generated as reference points ensures that closely follows while also enabling real-time detection effectiveness has been validated through both simulations practical experiments, offering efficient viable solution problem.

Language: Английский

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Path Planning of UAV Formations Based on Semantic Maps

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(16), P. 3096 - 3096

Published: Aug. 22, 2024

This paper primarily studies the path planning problem for UAV formations guided by semantic map information. Our aim is to integrate prior information from maps provide initial on task points formations, thereby formation paths that meet practical requirements. Firstly, a segmentation network model based multi-scale feature extraction and fusion employed obtain aerial containing environmental Secondly, maps, three-point optimization optimal trajectory established, general formula calculating heading angle proposed approximately decouple triangular equation of trajectory. For large-scale points, an improved fuzzy clustering algorithm classify distance constraints clusters, reducing computational scale single samples without changing sample size improving allocation efficiency model. Experimental data show cluster method using angle-optimized achieves 8.6% improvement in total flight range compared other algorithms 17.4% reduction number large-angle turns.

Language: Английский

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Enhanced Nutcracker Optimization Algorithm with Hyperbolic Sine–Cosine Improvement for UAV Path Planning

Biomimetics, Journal Year: 2024, Volume and Issue: 9(12), P. 757 - 757

Published: Dec. 12, 2024

Three-dimensional (3D) path planning is a crucial technology for ensuring the efficient and safe flight of UAVs in complex environments. Traditional algorithms often find it challenging to navigate obstacle environments, making quickly identify optimal path. To address these challenges, this paper introduces Nutcracker Optimizer integrated with Hyperbolic Sine–Cosine (ISCHNOA). First, exploitation process sinh cosh optimizer incorporated into foraging strategy enhance efficiency nutcracker locating high-quality food sources within search area. Secondly, nonlinear function designed improve algorithm’s convergence speed. Finally, that incorporates historical positions dynamic factors introduced influence position on process, thereby improving accuracy retrieving stored food. In paper, performance ISCHNOA algorithm tested using 14 classical benchmark test functions as well CEC2014 CEC2020 suites applied UAV models. The experimental results demonstrate outperforms other across three suites, total cost planned paths being lower.

Language: Английский

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Coverage Path Planning for UAVs: An Energy-Efficient Method in Convex and Non-Convex Mixed Regions

Drones, Journal Year: 2024, Volume and Issue: 8(12), P. 776 - 776

Published: Dec. 20, 2024

As an important branch of path planning, coverage planning (CPP) is widely used for unmanned aerial vehicles (UAVs) to cover target regions with lower energy consumption. Most current works focus on convex regions, whereas others need pre-decomposition deal non-convex or mixed regions. Therefore, it necessary pursue a concise and efficient method the latter. This paper proposes two-stage named Shrink-Segment by Dynamic Programming (SSDP), which aims limited energy. First, instead decomposing then SSDP formulates optimal shrinking rings Second, dynamic programming (DP)-based approach segment overall UAVs in order meet limits. Experimental results show that proposed achieves less overlap consumption compared state-of-the-art methods.

Language: Английский

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