Limit Cycle-Based Artificial Fields for Obstacle Avoidance in Robot Path Planning
Bibliographic record
Abstract
This paper introduces a novel limit cycle-based approach for obstacle avoidance and compares its performance against the conventional Attractive-Repulsive method, a widely used artificial potential field (APF) technique. While traditional APF methods model obstacles as repulsive fields and the goal as an attractive field, the proposed method incorporates a virtual limit cycle around each obstacle, enabling the robot to engage tangentially with the obstacle boundary and navigate around it smoothly. The limit cycle field is designed to drive the robot along a near-optimal path without the need for complex computations or manual switching strategies. Simulation results demonstrate that the proposed method consistently yields shorter and smoother trajectories compared to the AttractiveRepulsive approach, while maintaining low computational overhead. Although experiments were conducted on a two-degree-of-freedom cable-driven robot in a vertical plane, the methodology can be generalized to a broad range of robotic systems with higher degree-of-freedom.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".