Navigation and Path Planning of Mobile Robots
Bibliographic record
Abstract
Autonomous mobile robots grapple with the complexities of navigating their operational environment and exercising independent decision-making.A principal challenge in robotics lies in path planning, i.e., identifying the optimal route from an origin to a destination.Various approaches to surmount this challenge have been probed by researchers, taking into account parameters such as the environment, the type of robot, and application prerequisites.A proficient path-planning algorithm stands as a linchpin for secure mobile robot navigation and the triumphant execution of robotics applications.Typically, the primary objective of the navigation process is to minimize the distance traversed, given its implications on other metrics such as processing time and energy consumption.This research aims to shed light on the pivotal components of mobile robot environment representation, navigation, and offers an analysis of certain path-planning techniques.When the development of navigation algorithms initially kicked off, classical techniques such as Artificial Potential Field (APF), Cell Decomposition, and Roadmap gained popularity.However, compared to their predecessors, heuristic path planning techniques like Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) have recently witnessed a surge in exploration.This research presents a balanced examination of the merits and demerits of these methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".