Waypoint-Based Path Planning for Autonomous Robots with PSO
Bibliographic record
Abstract
Autonomous robots are essential for numerous applications requiring high precision and involving significant risks. However, planning and optimizing paths for a single robot in a dynamic environment remains a highly complex task. This challenge necessitates a balance between obstacle avoidance, distance optimization, and efficiency. Traditional strategies often utilize a wide range of path planning algorithms, such as local avoidance, A*, and ACO. Nevertheless, an approach employing waypoints and a “danger zone” in conjunction with a Particle Swarm Optimization (PSO) algorithm can be an effective method for path optimization. Here, we demonstrate that this path planning algorithm provides an efficient and rapid means to generate a path to a goal in an environment with a single obstacle. Through simulations conducted in IR-SIM, a Python-based robotics simulator, we show that a customized PSO, equipped with a multi-objective fitness function, effectively analyzes both intersections with an area around the obstacle (the “danger zone”) and the total path distance. This approach yields paths with over a 97% success rate in guiding the robot to its goal and exhibits relatively low convergence times. Our results illustrate PSO’s effectiveness as a path planning algorithm, highlighting its adaptability to various types of obstacles and positions within a 2D environment. This strategy represents an advancement in the use of heuristic algorithms for autonomous robot path planning, leading to a faster, less computationally demanding algorithm with a high success rate, capable of avoiding diverse obstacle types and easily adaptable to a broad range of single-robot environmental problems.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".