Omnidirectional Platform for Autonomous Mobile Industrial Robot
Bibliographic record
Abstract
Omnidirectional mobile platforms are holonomic robots that can independently and simultaneously perform translational and rotational motions. In order to develop an autonomous omnidirectional mobile manipulator, this paper presents a platform based on four mecanum wheels. It has a higher carrying capacity and mobility than a standard four-wheel platform. The used manipulator is a Fanuc LR Mate 200 iD/7l robot with an R-30iB Mate Plus Controller. The heavy weight of the industrial arm and the controller makes collision-free navigation a challenge. To navigate with this robot in an unknown semi-structured indoor environment, a Hokuyo 2D Lidar and a Realsense D435i camera have been used. The Central Processing Unit is an Nvidia Jetson TX2 running Ubuntu Linux on which ROS (robot operating system) was installed. The robot is capable of autonomously performing Simultaneous Localization and Mapping (SLAM), navigation, obstacle detection, and object recognition, vision-guided robot motions. A map of our workplace was generated. Most mobile robot motion control approaches rely on dynamic or kinematic models. The study also covers mathematical modeling of the four-wheeled omnidirectional platform that leads to the robot's kinematics. The simulations were carried out using MATLAB to establish and verify the kinematic model of the omnidirectional platform. The robot was controlled to follow curves with a constant translation velocity of 1m/s.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".