Trajectory Planning for a Wheeled Mobile Robot and its Robotic Arm
Bibliographic record
Abstract
A temporal planning algorithm that is implemented on a wheeled mobile robot is presented. This algorithm has two parts: the first part is developed to control the motion of the mobile robot, and the second part is designed to control a 6-degrees-of-freedom (DOF) arm attached to the robot. In most robotic applications, it is necessary for the mobile robot to plan and follow a desired path. It may also be necessary for the robot to follow a given velocity profile, which is known as temporal planning. The advantage of temporal planning method in this paper is in its simplicity and its computational efficiency. A rudimentary trajectory is first created by assigning an arbitrary time to each segment of the path. This trajectory is made feasible by applying a number of constraints and using a linear scaling technique. When a velocity profile is given, a nonlinear time scaling technique is used to fit the mobile robot’s linear velocity to the given velocity profile. A method for avoiding moving obstacles is also implemented. Simulation and experimental results showed good agreement with each other. A novelty of this paper is in developing and implementing a new method for control of a 6-DOF arm attached to the mobile robot. Two methods have been proposed and tested for position control of the robot arm; i) linear end-effector increment (LEI), and ii) linear joints increment (LJI). It is shown that LEI is more precise than LJI in trajectory tracking of robot arm; however, the singularity of Denavit-Hartenberg (DH) transformations matrix limits the application of this method for specific trajectories. The LJI is developed to avoid the singularity in DH transformation matrix. The experimental results for four different paths show the effectiveness of the LJI approach. The successful experimental results of path and temporal planning of a wheeled mobile robot and motion control of its industrial arm is reported.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".