Online Path Planning of Mobile Robots Based on African Vultures Optimization Algorithm in Unknown Environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Autonomous mobile robots developed using metaheuristic algorithms are increasingly becoming a hot topic in control and computer sciences. Specifically, finding the shortest root to the goal and avoiding hurdles are current subjects of autonomous mobile robots. The main drawbacks of classic methods are the incapacity to move the robot in a dynamic and unknown environment, deadlock in a local minimum and complicated environments, and incapacity to foretell the speed vector of obstacles and non-optimality of the route. This article exhibits a recent path planning approach that utilizes the African Vultures Optimization (AVOA) for navigation of the mobile robot in static and dynamic unknown environments with a dynamic target. The proposed online optimization approach is used in three different environments including an environment with unknown static obstacles, an environment with unknown dynamic obstacles, and an environment with a dynamic target. The proposed approach can solve a local minima problem in the environment with static obstacles. The online optimization method is performed using two phases which are the sensors’ reading phase and the path calculation phase and the results are given based on computer simulation in different unknown environments. A comparative study was conducted between the suggested algorithm and two other algorithms and the results showed that the AVOA algorithm was better in avoiding obstacles successfully including the local minima situation. Finally, the average enhancement rates in the path length compared with the Adaptive Particle Swarm Optimization (APSO) and the Hybrid Fuzzy-Wind Driven Optimization (WDO) are 2.21% and 1.02207%, respectively.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 it