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

ObjectiveThis study proposes a Complete Coverage Path Planning (CCPP) algorithm based on a sine-constrained Rulkov-Like Hyper-Chaotic (SRHC) mapping. The work addresses key challenges in robotic path planning and focuses on improving coverage efficiency, path unpredictability, and obstacle adaptability for mobile robots in complex environments, including disaster rescue, firefighting, and unknown-terrain exploration. Traditional methods often exhibit predictable movement patterns, fall into local optima, and show inefficient backtracking, which motivates the development of an approach that uses chaotic dynamics to strengthen exploration capability.MethodsThe SRHC-CCPP algorithm integrates three components: (1) SRHC Mapping A hyper-chaotic system with nonlinear coupling (Eq. 1) generates highly unpredictable trajectories. Lyapunov exponent analysis (Fig. 3), phase-space diagrams (Fig. 1), and parameter-sensitivity studies (Table 1) confirm chaotic behavior under conditions such as a=0.01 and b=1.3. (2) Memory-Driven Exploration—A dynamic visitation grid prioritizes uncovered regions and reduces redundancy (Algorithm 1). (3) Collision detection combined with normal-vector reflection reduces oscillations in cluttered environments (Fig. 4). Simulations employ a Mecanum-wheel robot model (Eq. 2) to provide omnidirectional mobility.Results and Discussions(1) Efficiency: SRHC-CCPP achieved faster coverage and improved uniformity in both obstacle-free and obstructed scenarios (Figs. 8～10). The chaotic driver increased path diversity by 37% compared with rule-based methods. (2) Robustness: The algorithm demonstrated initial-value sensitivity and adaptability to environmental noise (Fig. 5). (3) Scalability Its low computational overhead supported deployment in large-scale grids (>104 cells).ConclusionsThe SRHC-CCPP algorithm advances robotic path planning by: (1) Merging hyper-chaotic unpredictability with memory-guided efficiency, which reduces repetitive loops. (2) Offering real-time obstacle negotiation through adaptive reflection mechanics. (3) Providing a versatile framework suited to applications that require high coverage reliability and dynamic responsiveness. Future work may examine multi-agent extensions and three-dimensional environments.