Application of an improved ACO integrating BFS and Laplacian smoothing strategy in mobile robot path planning
Bibliographic record
Abstract
Purpose In mobile robot path planning, algorithms such as PSO and GA are widely applied but have issues such as premature convergence and insufficient path smoothness. Although ant colony optimization (ACO) has advantages in path diversity and global search capability, it faces limitations including poor initial guidance, slow convergence, time-consuming computation and excessive redundant turning points. This paper proposes an enhanced ACO integrating multiple improvement strategies to accelerate convergence, improve search efficiency, smooth trajectories and enhance the overall execution efficiency of robots. Design/methodology/approach The method first uses BFS to pre-search a feasible path, which is smoothed and used to enhance pheromone concentration, improving the ants' initial search direction. A Sigmoid dynamic heuristic factor accelerates convergence, while a dynamic pheromone evaporation rate balances global exploration and local exploitation. The pheromone update equation has been improved to prevent the overuse of frequently selected edges, thereby avoiding premature convergence to local optima. Edge usage rate information further balances exploration and exploitation. Finally, Laplacian smoothing is applied to the path to remove discrete points and sharp turns, resulting in a natural and coherent trajectory. Findings Simulations show that the improved ACO outperforms four existing algorithms in convergence speed, number of turning points and path smoothness, confirming its effectiveness in finding optimal and practical trajectories. Practical implications This method holds broad future promise in the field of mobile robotics, enabling intelligent systems to achieve more efficient and safer autonomous navigation across diverse scenarios. By significantly enhancing task execution speed and resource utilization, it lays a solid foundation for the widespread adoption and sustained development of mobile robotics technology. Originality/value This paper introduces a novel integration of BFS-based pre-search with pheromone enhancement, a Sigmoid dynamic heuristic factor, dynamic pheromone evaporation and improved pheromone updating based on edge usage rates, collectively addressing traditional ACO's weaknesses. The application of Laplacian smoothing further refines path quality. These contributions significantly improve converge.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".