Real-time collision-free path planning and tracking control of a nonholonomic mobile robot using a biologically inspired approach
Bibliographic record
Abstract
In this paper, a novel biologically inspired neural network approach is proposed for dynamic collision-free path planning and tracking control of a nonholonomic mobile robot in a nonstationary environment. The real-time collision-free trajectory of the mobile robot with obstacle avoidance is generated by a topologically organized neural network, where the dynamics of each neuron is characterized by a shunting equation derived from Hodgkin and Huxley's biological membrane equation. The configuration space of the mobile robot constitutes the state space of the neural network. The varying environment is represented by the dynamic activity landscape of the neural network, where the neural activity propagation is subject; to the kinematic constraint of the nonholonomic mobile robot. Thus no local collision checking procedures are needed. The tracking velocities are generated by a novel neural dynamics based controller, which is based on two shunting models and the conventional backstepping technique. Unlike the backstepping controllers that produce velocity commands with sharp jumps, the proposed tracking controller can generate smooth, continuous commands, not suffering from the velocity jump problem. The effectiveness and efficiency of the proposed approach are demonstrated through simulation and comparison studies.
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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 | 0.000 | 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".