A Hybrid Architecture for Planning and Execution of Multi-Behavior Data Acquisition Missions
Bibliographic record
Abstract
This paper addresses the issue of designing integrated deliberative-reactive architectures for multi-behavior robot navigation control. The objective of the study is to devise and investigate a methodology for designing robust planning and control systems equipped with a high level of intelligence and capable of navigating a mobile platform, at a high level of performance, in complex environment conditions, where the mobile robot multi-task operation is subject to different behaviors. A formal model of the integrated architecture is presented. Components of the model incorporate hybrid intelligence techniques, allowing the robot to perform different patterns of behavior for different purposes. Metaheuristic procedures enhance the deliberative level producing the optimal global path and the optimal sub-global path. Multiple search methods are proposed to optimize and enable multi-behavior path planning navigation based on waypoints approach. A behavior selector is employed for controlling and executing the appropriate behavior to perform complex tasks along the global path. On the reactive level, fuzzy behavior-based systems are employed to execute different robot tasks including conflicting behaviors. A navigation behavior control module regulates the relation between the navigation levels and as well as executes control on each navigation component. Although designed for the execution of data acquisition missions, the proposed architecture is general enough to show good performance in a variety of complex conditions. Experimental results obtained by using a Khepera robot demonstrate the validity of the presented hybrid architecture in a critical dynamic and complex environment.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".