Improving Time-Optimal Maneuvers of Two-Link Robotic Manipulators
Bibliographic record
Abstract
HIS Note discusses in detail numerical results of a parametric study for rest-to-rest time-optimal maneuvers of rigid two-link robotics manipulators. The problem of minimizing the time of planar maneuvers in terms of control and structure was considered parametrically. First, the time-optimal control strategy was applied. This strategy led to a bang-bang control in which the motors operated with the maximum torques changing directions at the switch time. The solutions were obtained by directly using Pontryagin’ s minimum principle (PMP). The analysis was then repeated for different lengths of particular links and for different torques applied at particular joints. The total length of the manipulator and the resultant torques generated by the shoulder and elbow motors were kept constant. For the numerical calculations, the set of data characterizing the IBM 7535 B 04 robot discussed in Refs. 1 and 2 was adapted. II. Time-Optimal Control of Two-Link Robotic Manipulators The equations derived from PMP and the corresponding boundary conditions form a two-point boundary value problem (TPBVP). Here we present the solutions generated by a numerical procedure that combinesthe forward-backwardmethod with the shooting method to directly solve the TPBVP. 3 The procedure that is capable of determining the states, the costates, and the switching functions with a high numerical accuracy was discussed in more detail in Ref. 4. The procedure was used by the authors in Ref. 5 examine the effects of orientation of the plane of motion on the time-optimal maneuvers in the gravitational e eld. That work is extended here to include the effects of the links and the torque ratios. Maneuvers of a two-link robotics manipulator are considered in plane y, z as shown in Fig. 1. Mass moments of inertia of the links with respect to their centers of mass, located at lc1 and lc2, respectively, are I1 and I2. The states are x1 = } 1, x2 = C } 1, x3 = } 2, and x4 = C
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| 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".