ANMIP: Adaptive Navigation based on Mutual Information Perception in Uncertain Environments
Bibliographic record
Abstract
Navigation in uncertain environment has become a hot research topic. Some path planning algorithms have been proposed to address the uncertainty problem, such as Canadian Traveller’s Problem (CTP) algorithm. However, these algorithms usually require accurate environmental information, which is often difficult to obtain. In addition, their decisions are based on robot current perceptions and the previous navigation experience is usually ignored, whereas the past experience is an important reference for people to optimize policies. In order to address these issues, we propose a mutual information perception based navigation method for efficient navigation in uncertain environment. First, an efficient CTP solver is proposed to quickly generate policy based on the obtained environment information. Second, a topological map representation method is proposed for map decomposition. In order to perceive environmental information, a block judgment interface module is proposed. Then, the door edge resolver algorithm is proposed to absorb the experience of the previous navigation. Finally, we design a complete information updating mechanism based on Wilson confidence interval, so that the robot can update its perception of the environment and realize adaptive navigation in uncertain environments. The experimental results show that by comparing with the existing move_base navigation system, our method has better performance in average navigation cost and navigation success rate.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".