Real-time collision-free path planning and tracking control of a nonholonomic mobile robot using a biologically inspired approach
Why this work is in the frame
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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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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 | 0.001 | 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) | 0.000 | 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 it