Energy-Optimal Trajectory Planning with Vehicle's Dynamic Model Considerations
Bibliographic record
Abstract
Indoor Autonomous Vehicles (IAVs) play a crucial role in load transportation, aligning with the objectives of Industry 4.0 in the industry sector. As IAVs operate on battery power, efficient energy usage is essential for mission completion. Hence, this paper presents a novel methodology for incorporating the dynamic vehicle model into the trajectory planning process to generate energy-optimal trajectories. To achieve this, the Timed Elastic Band (TEB) algorithm is selected as the base trajectory planner, which is then enhanced with a dynamic vehicle model. The approach is tested on an Autonomous Forklift (AF). The first step involves the development of a dynamic model for the AF, followed by the creation of a dataset using this model. Then a deep neural network (DNN) model is trained on this dataset to predict the required torques for the vehicle's straight-line and rotational movements. Finally, the trained model is integrated into the TEB planner, resulting in an optimized version, referred to as OTEB, for determining the most efficient trajectories. Simulation results demonstrate that the proposed trajectory planner significantly optimizes energy consumption, highlighting its effectiveness in real-world applications.
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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 | 0.000 | 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".