Workspace-Based Motion Planning for Quadrupedal Robots on Rough Terrain
Bibliographic record
Abstract
Legged robots have demonstrated high potential when dealing with rough terrain, for which an efficient motion planner becomes crucial. This article presents a novel approach for quadrupedal robot motion planning on rough terrain that is both conceptually straightforward and computationally efficient. Implementing the concept of workspace constitutes the cornerstone of this method: both body poses and swing-leg footholds are chosen within their corresponding workspace. A novel approach called the “cross-diagonal method” is developed to facilitate the search for new body poses. Based on the obtained body pose, the foothold for a swing leg selected within its foot workspace satisfies the reachability constraint automatically. The proposed motion planning scheme is integrated with an elevation mapping module and a state estimation module, enabling quadrupedal robots to travel through uneven terrains with high efficiency. The significance of this work is validated through simulation and physical experiments with a quadrupedal robot, which achieves high success rates in overcoming difficult terrains without prior knowledge of the environment. This approach offers the advantages of high computational efficiency, simplicity, and adaptability to different types of terrain, making it a promising solution for real-world applications.
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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 | 0.001 |
| 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".