Multiprocessor-compatible inverse kinematics and path planning for robots
Bibliographic record
Abstract
Novel algorithms in robot inverse kinematics and path planning are proposed. Emphasis is placed on real-time execution speed with multiprocessors and adaptability to unpredictable environments. The inverse kinematics algorithm is an iterative solution which is applicable to many classes of industrial robots, and is stable at and around singularities. The method is based on a simple functional analysis of each link of a manipulator and projecting vectors on the coordinate frame of each joint. Heuristic rules are used to control a mobile manipulator base and to guide the manipulator in the case of non-convergence caused by joint limits. The path planning algorithm uses a potential surface in a quantized configuration space. Paths are guaranteed to be collision-free for all parts of the robot. Local minimum regions on the potential surface are filled on demand by extending the obstacles. Arbitrarily shaped obstacles in 3-dimensions can be handled. Using a hierarchical collision detection technique, high execution speed can be maintained even with many complex shaped obstacles in the workspace. The path planning method can theoretically be applied to any manipulator with any degrees of freedom. The implementation of the inverse kinematics and path planning algorithms in a parallel hierarchical multiprocessor computer structure designed for the control of robots is proposed and investigated. Communication among the processors is by point-to-point message passing via asynchronous serial links with message buffers. Computer simulations are used to demonstrate the appropriateness and feasibility of this computer structure for robot control.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".