Scenario-based Model Predictive Control for Path Planning and Obstacle Avoidance
Bibliographic record
Abstract
A basic problem in path planning is to regulate an appropriate path with the purpose of minimizing a cost function of interest under conditions of obstacle avoidance and model dynamics satisfaction. The cost function can be further minimized if some constraints are allowed to be violated with a guaranteed probability in some practical cases. However, very few results are reported about path planning with a preferred probability of constraints violation. In this paper, we investigate the scenario-based stochastic model predictive control (SCMPC) problem for path planning and obstacle avoidance. We find out the main reason that prevents the SCMPC approach to work for path planning is that the obstacles are commonly formulated as non-convex constraints, and a fundamental assumption in the field of SCMPC approach is the convexity of all the constraints. To address this problem, we propose a novel concept of candidate path, which is used to explicitly denote all the possible combinations of linear constraints. A conditional-scenario algorithm is accordingly developed that turns the original non-convex optimization problem to several convex subproblems. Simulation results verify the validity of the presented theories.
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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".