Steering Control of Ackermann Architecture Weed Managing Mobile Robot

A robot that finds and eliminates weeds from crops is called a weed control robot. Weeds deplete primary crops moisture supplies and hinder their development. They may be harmful to both human and animal health and result in losses in crop yield. Herbicides and other chemicals have been used for many years to eradicate weeds from crops; nevertheless, these chemicals harm plants and contaminate the environment. In this work, a novel semantic weeds detection approach based on PC/BC-DIM network has developed which shows outbreaking performance and classification results compared to the state-of-art approaches. We developed an autonomous weed control robot which consists of Ackermann Architecture and delta robot. Delta robot have a camera on its base that is used to detect the real time weeds in the environment. First of all, image is acquired by camera and with the help of image processing techniques we are able of detecting the weed from other crops and eliminate them by the electrical discharging method in which electrodes are connected at its end effector that will burn the weed detected. We also developed a system for path planning and obstacle avoidance for navigation of mobile robot in which we used the technique of stereo vision that will capture the stereo images of environment and find their disparity. With the help of depth information, robot will be able to detect the object in its way and avoids the obstacle and find the shortest path to navigate in field using A* algorithm. The results obtained from this work are simulation based which are detection of weed in field images using image processing and path planning of robot using stereo images of field. The system has a fairly good overall accuracy of 81.25%. The efficiency of the system is moderate, but the relatively high False Positive Rate and RMS Error suggest that the system need improvement to reduce significant errors and false positives. Our future work involves the removal of weed and implementation of simulated results to hardware.