Complete Coverage Path Planning Algorithm Based on Rulkov-like Chaotic Mapping

LIU Sicong; HE Ming; LI Chunbiao; HAN Wei; LIU Chengzhuo; XIA Hengyu

doi:10.11999/JEIT250887

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LIU Sicong, HE Ming, LI Chunbiao, HAN Wei, LIU Chengzhuo, XIA Hengyu. Complete Coverage Path Planning Algorithm Based on Rulkov-like Chaotic Mapping[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250887

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LIU Sicong, HE Ming, LI Chunbiao, HAN Wei, LIU Chengzhuo, XIA Hengyu. Complete Coverage Path Planning Algorithm Based on Rulkov-like Chaotic Mapping[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250887

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Complete Coverage Path Planning Algorithm Based on Rulkov-like Chaotic Mapping

doi: 10.11999/JEIT250887 cstr: 32379.14.JEIT250887

1.
School of Command and Control Engineering, Army Engineering University of PLA, Nanjing, 210042, Jiangsu, China
2.
School of Artificial intelligence and Engineering, Jiangsu Vocational Institute of Commerce, Nanjing 210045, China
3.
School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China

Funds: National Natural Science Foundation of China, No. 62273356, National Talent Project of China, No.2022-JCJQ-ZQ-001, Provincial Primary Research and Development Plan, China, No.BE2021729, High-level Talent Innovation Project, China, No.KYZYJQJY2101, National Key Research and Development Program of China 2024YFF1401400

Received Date: 2025-09-09
Accepted Date: 2025-11-03
Rev Recd Date: 2025-11-03

Available Online: 2025-11-13

Abstract

Abstract

Objective This study proposes a novel complete coverage path planning (CCPP) algorithm based on a sine-constrained Rolkov-like hyper-chaotic (SRHC) mapping, addressing critical challenges in robotic path planning. The research focuses on enhancing coverage efficiency, path unpredictability, and obstacle adaptability for mobile robots in complex environments, such as disaster rescue, firefighting, and unknown terrain exploration. Traditional methods often suffer from predictable movement patterns, local optima traps, and inefficient backtracking, motivating the need for advanced solutions leveraging chaotic dynamics. Methods The SRHC-CCPP algorithm integrates: 1. SRHC Mapping A hyper-chaotic system with nonlinear coupling (Eq. 1) that generates highly unpredictable trajectories, validated via Lyapunov exponent analysis (Fig. 3a–b). Phase-space diagrams (Fig. 1) and parameter sensitivity studies (Table 1) confirm chaotic behavior under conditions like a=0.01a=0.01, b=1.3b=1.3. 2. Memory-Driven Exploration A dynamic visitation grid prioritizes uncovered regions, reducing redundancy (Algorithm 1). 3. Obstacle Handling Collision detection with normal vector reflection minimizes oscillations in cluttered environments (Fig. 4). Simulations employed a Mecanum-wheel robot model (Eq. 2) for omnidirectional mobility. Results and Discussions 1. Efficiency: SRHC-CCPP achieved faster coverage and superior uniformity in both obstacle-free and obstructed scenarios (Fig. 5–6). The chaotic driver improved path diversity by 37% compared to rule-based methods. 2. Robustness: Demonstrated initial-value sensitivity and adaptability to environmental noise (Table 2). 3. Scalability: Low computational overhead enabled deployment in large-scale grids (>10⁴ cells). Conclusions The SRHC-CCPP algorithm advances robotic path planning by: 1. Merging hyper-chaotic unpredictability with memory-guided efficiency, eliminating repetitive loops. 2. Offering real-time obstacle negotiation via adaptive reflection mechanics. 3. Providing a versatile framework for applications requiring high coverage reliability and dynamic responsiveness. Future work may explore multi-agent extensions and 3D environments.
- Complete coverage path planning,
- hyper-chaotic mapping,
- memory effect,
- obstacle avoidance,
- Lyapunov exponent

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References(31)

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