## 4 Results and Discussion  
### Experiment 1: Manipulation and Transportation of Centrifugal Tubes  
In the current work, the robot used was able to move the tray containing centrifugal tubes in 7 min and 14 s at 20% maximum speed. 20% was achieved only in places with no obstacles within 0.75 m around the robot. In other places, the velocity is limited to 10% to avoid reaching potential obstacles at a very short distance.  
The robot was able to successfully complete the entire task and reach back to the home position by this time. The task cycle time can be reduced by running the robot at higher speeds, optimizing the motion, or avoiding delays during the manipulation. If  
Figure 6: Task flow diagram of the experiment using Petri dishes.  
Figure 7: Scanning the barcode attached to Petri dish (**a**) picking the dish (**b**) Raising the dish (**c**) Flipping the dish (**d**) Placing the dish for scanning (**e**) Scanning the QR code (**f**) Collecting the dish back.  
the robot operates at its full speed, it can complete the task within 2 min. If the robot is operated continuously for a full day, allowing a total charging time of 4 h/day, the robot could perform this task 600 times/day. This is equivalent to relieving 2.5 humans from the mundane task of moving these tubes. The overall cost of a robot like the one used in the current work is around 200,000 euros. Considering that an average salary of an employee in the biomanufacturing sector is 40,000 euros, a reasonable break-even period can be expected with an added advantage of a reduction in contamination and human errors. Similarly, at a maximum velocity of 25%, the robot took <3 min to pick the Petri dish up and scan the QR code. This task could be completed in less than 1 min if the robot is operated at its full speed. Allowing for travel time between sampling points still corresponds to several hundred tasks/day, considering that the robot is charging for 4 h/day.  
Although the robot can use the vision system to correct the error, this functionality was not used in this experiment as the robot was able to complete the task successfully by applying the fine positioning technique. The average accuracy of the fine positioning technique was found to be 10.2 mm, which is adequate for this task; results are shown in Figure 8.  
The experiment shows the ability of the robot to complete such material transfer tasks in a human-like manner. The robot can therefore replace a human operator without modifying much of the current hardware and equipment in a typical biomanufacturing facility. Figure 9 shows the robot reaching inside the storage to grasp the rack. The figure shows the dexterity capabilities of the robot to reach inside the storage to collect objects. The three-finger gripper allows the try to be grasped in a configuration similar to how a human would do the same task.  
These robots are also capable of updating their live location on the map. Figure 10 shows the location of the robot being updated on the map as it moves toward the storage area. This data can be used for motion planning when more than one robot operates in the facility. The map can also be used by a human operator residing in a remote room to  
Figure 8: Positioning error measured using the robot’s fine positioning feature in 20 repeat experiments.  
Figure 9: Robot reaching inside the storage cabinet to grasp the rack.  
monitor the robot. Overall, this data can be used for monitoring the biomanufacturing facility.