Prediction based guidance for real-time navigation of mobile robots in dynamic cluttered environments
Bibliographic record
Abstract
Real-time motion-planning in autonomous vehicle navigation applications has typically referred to the on-line trajectory-planning problem to reach a designated location in minimal time. In this context, past research achievements have been subjected to three main limitations: (i) only the problem of interception (position matching) has been considered, whereas the problem of rendezvous (velocity matching) has not been rigorously investigated; (ii) obstacles have been commonly treated as moving with constant velocity as opposed to being highly maneuverable and following a priori unknown trajectories; and, (iii) mostly, structured indoor terrains have been considered. This Thesis addresses the abovementioned drawbacks by proposing the use of a novel advanced guidance-based rendezvous methodology in allowing an autonomous vehicle to accurately and safely maneuver in the presence of dynamic obstacles on realistic terrains. The objective is time-optimal rendezvous with static or dynamic targets. The proposed on-line motion-planning method minimizes rendezvous time with the target, as well as energy consumption, by directly considering the dynamics of the obstacles and the target, while accurately determining a feasible way to travel through an uneven terrain. This objective is achieved by determining rendezvous maneuvers using the Advanced Predictive Guidance (APG) law. Namely, the navigation method is designed to effectively cope with maneuvering targets/obstacles by predicting their future velocities and accelerations. The terrain navigation algorithm, also developed within the framework of this Thesis, computes a safe path through a realistic terrain that also minimizes the rendezvous time. All developed algorithms are seamlessly integrated into one overall vehicle guidance algorithm. Extensive simulation and experimental analyses, some of which are reported herein, have clearly demonstrated the time efficiency of the proposed rendezvous method on realistic terrains as well as the robustness of the proposed algorithm to measurement noise.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".