Real-time collision-free motion planning of nonholonomic robots using a neural dynamics based approach
Bibliographic record
Abstract
A novel neural dynamics based approach to smooth, continuous and collision-free path generation of an autonomous nonholonomic mobile robot is proposed. The robot behavior, such as target acquisition and obstacle avoidance, are completely controlled by two control variables, the heading direction and the forward velocity of the robot. The dynamics of these control variables is characterized by a biologically inspired shunting neural model, whose inputs are from the target and obstacles that are acquired relying on measurable sensors information only. The target input produces an attractive force, while the obstacle inputs form repulsive forces to the mobile robot. Each force votes for a certain value of control variables that have unique values at a certain time. The collision-free path and the velocity control commands of the robot are generated through the dynamics of control variables. The kinematic constraints of mobile robot is respected. A series of simulation results show that the proposed approach can be successfully applied to both static and dynamic environments, as well as multi-robot systems with effective and efficient computation.
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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 | 0.000 |
| 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) | 0.001 | 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".