Evolutionary Neural Network-Based Obstacle Avoidance for a Planetary Exploration Rover
Bibliographic record
Abstract
During space missions, a planetary exploration rover is subject to two main communication issues that are inherent to the distance and orbital difference between Earth and the target planet, the communication transmission delay and the limited transmission window.Besides, the computational resources are restricted onboard the vehicles for space missions.Based on these facts, the rover is desired to traverse on the target planet efficiently with fewer human commands and less computational needs, as well as with an autonomous decision-making scheme -allowing the rover to perform autonomous science.The proposed autonomous path planning system presents a set of genetically evolved neural network controllers for local path planning of a mobile robot.Travelling environments are partitioned into three categories according to distribution patterns of targets and obstacles.Each evolved network is adopted to direct the rover travelling in one category of partitioned environments achieving a sequence of targets with obstacle avoidance.With a set of pre-learned networks, the rover would be adaptable to traverse in new environments of specific category.Input to the network is the range and bearing data measured from current position of the rover to surrounding obstacles and the approaching target.It outputs a turning angle ratio processed to be direction of the rover for next move.Genetic algorithm is used to obtain the evolved network by developing behavior strategies through evolutionary iterations.Simulation results indicate that evolved neural controller can adapt to novel environments and generate satisfying path for the rover in a computationally economic manner.
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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) | 0.000 | 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 | 0.001 | 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".