A Stability and Terminal Constraints-Based Obstacle Avoidance Framework for Autonomous Industrial Vehicles Under Complex Scenarios
Bibliographic record
Abstract
Unmanned industrial vehicles are prone to dynamic instability during emergency obstacle avoidance. Variations in multi-dimensional stability over time pose significant challenges to both collision avoidance and stability control, resulting in hesitation and conservatism in vehicle decisions, similar to those of human drivers. This paper analyzes the avoidance distance within the context of multidimensional stability boundaries and proposes a framework that integrates high-level behavioral decision-making with low-level motion tracking. First, the pure braking distance and safety distance are calculated considering the vehicle's longitudinal and lateral stability. A terminal constraints-based optimization method is then employed to account for the diverse motion states of surrounding vehicles during the motion planning of the ego vehicle. Additionally, the multi-objective optimization problem of obstacle avoidance is reformulated as a control problem with well-defined objectives. Then, the performance of super-twisted sliding mode control (STSMC) and model predictive control (MPC) across different obstacle avoidance distances is subsequently compared. Finally, the proposed framework is validated on a hardware-in-the-loop (HIL) platform. The results demonstrate that the decisionmaking planner, based on avoidance distance, provides collisionfree navigation for industrial vehicles in complex scenarios. It also accurately categorizes obstacle avoidance challenges, significantly enhancing both vehicle stability during avoidance and the computational efficiency of the system.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".