Greenhouse Mobile Robot Navigation Using Wheel Revolution Encoding and Learning Algorithm

Repetitive and dangerous tasks such as harvesting and spraying have made robots usable in the greenhouses. the mechanical structure and navigation algorithm are two important parameters in the design and fabrication of mobile greenhouse robots. in this study, a four- wheel differential steering mobile robot was designed and constructed to act as a greenhouse robot. then, the navigation of the robot at different levels and actual greenhouses was evaluated. the robot navigation algorithm was based on the path learning, so that the route was stored in the robot memory using a remote control based on the pulses transmitted from the wheels encoders; then, the robot automatically traversed the path. robot navigation accuracy was tested at different surfaces (ceramics, concrete, dense soil and loose soil) in a straight path 20 meters long and a square path, 4×4 m. then, robot navigation accuracy was investigated in a greenhouse. robot movement deviation value was calculated using root mean square error (rmse) and standard deviation (sd). the results showed that the rmse of deviation of autonomous method from manual control method in the straight path to the length of 20 meters in ceramic, concrete, dense soil and loose soil were 4.3, 2.8, 4.6 and 8 cm, and in the 4×4 m square route were 6.6, 5.5, 13.1 and 47.1 cm, respectively.

Greenhouse Mobile Robot Navigation Using Wheel Revolution Encoding and Learning Algorithm

Repetitive and dangerous tasks such as harvesting and spraying have made robots usable in the greenhouses. The mechanical structure and navigation algorithm are two important parameters in the design and fabrication of mobile greenhouse robots. In this study, a four wheel differential steering mobile robot was designed and constructed to act as a greenhouse robot. Then, the navigation of the robot at different levels and actual greenhouses was evaluated. The robot navigation algorithm was based on the path learning, so that the route was stored in the robot memory using a remote control based on the pulses transmitted from the wheels encoders; then, the robot automatically traversed the path. Robot navigation accuracy was tested at different surfaces (ceramics, concrete, dense soil and loose soil) in a straight path 20 meters long and a square path, 4×4 m. Then, robot navigation accuracy was investigated in a greenhouse. Robot movement deviation value was calculated using root mean square error (RMSE) and standard deviation (SD). The results showed that the RMSE of deviation of autonomous method from manual control method in the straight path to the length of 20 meters in ceramic, concrete, dense & nbsp; soil and loose soil were 4.3, 2.8, 4.6 and 8 cm, and in the 4×4 m square route were 6.6, 5.5, 13.1 and 47.1 cm, respectively.