Navigation of Mobile Robot Using the PSO Particle Swarm Optimization
Bibliographic record
Abstract
Robots are being used increasingly in different fields like industry and space applications. Nowadays there are even demands for application of robots in homes and hospitals. These robots should be able to move and navigate at indoor areas which consist of fixed and movable obstacles like walls and chairs, respectively. There is not a fixed map of obstacles in these applications and the robot should detect obstacles and decide how to move to achieve the goal while avoiding obstacles. In this paper, an intelligent approach for navigation of a mobile robot in unknown environments is proposed. Particle Swarm Optimization(PSO) method be used for finding proper solutions of optimization problems. At first the robot navigation problem is converted to optimization problem. Then PSO method searches the solution space to find the proper minimum value. Based on position of goal. an evaluation function for every particle in PSO is calculated. In each iteration of the algorithm, the global best position of particle is selected and the robot moves to next calculated point in order to reach the goal. To be practical, it’s assumed that Robot can detect only obstacles in a limited radius of surrounding with its sensors. Environment is supposed to be dynamic and obstacles can be fixed or movable.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".