Inverse kinematics and path planning methods for discretely-actuated hyper-redundant manipulators
Bibliographic record
Abstract
Hyper-redundant manipulators are long and slender robotic arms capable of searching, exploring, and operating in confined environments. The very large number of actuated degrees of freedom requires the complex activation of simultaneous actuators to perform point-to-point motions between two poses. There exists two broad classes of motion planning algorithms in high dimensional spaces, effector-based and joint-based methods. While effector-based resolution methods can provide a quick solution to the end-effector path, they require an inverse kinematics solver to fit the joint space to the desired trajectory. Meanwhile, joint-based planners solve the path planning problem directly at the actuator level, but the statistical nature of these processes is sensitive to the dimensionality of the problem and falters in higher order systems. The goal of this work is to improve the joint based path planning of hyper-redundant manipulators by exploiting the behavioural strategies observed in octopuses. The research focuses on two search algorithms for the resolution of the inverse kinematics and the path planning problem of discretely-actuated manipulators. The first part of this thesis presents an inverse kinematics method inspired by the dynamic quasi-articulated structure of octopus limbs. A novel probabilistic roadmap path planner, based on the antagonistic muscle contraction waves responsible for the movement of the tentacle, is then proposed along with a method for focusing the search area during the roadmap construction phase. Keywords: hyper-redundant manipulators, inverse kinematics, obstacle avoidance, path planning
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".