Performance Simulation of a Solar-Powered and Hand Gesture Controlled Lawn Robot using Dynamic Movement Primitives (DMP)

Hand-gesture interpretation and control in robotics describe the interconnection between human and machine elements in the computer vision world. Pruning a structured environment is time-consuming and labor-intensive. Therefore, it requires management by a self-propelled machine. The path planning mode allows the robot to move along a specified path. Various studies on lawn mower robots focus more on obstacle avoidance with hand gesture interpretation and control implemented to take care of path definition. This study targets the development of a solar-powered lawn mower robot using hand gesture control as a path-planning technique. The robotic system continuously operates using charged batteries via solar energy stored in photovoltaic cells. The robot control mechanism was implemented via the use of infrared sensors to avoid obstruction on its path, and hand gesture interpretation via a DSP processor for path planning. The performance evaluation of the robot was based on field experiments and simulations using SolidWorks, defined in terms of area covered, lawn availability, energy utility, and optimum turning velocity. The evaluation revealed that the machine’s efficiency is almost 100% based on the area covered, the percentage availability of the robot is 95%, and the average energy utility of 7.7 KWh was also obtained. The optimum turning velocity of 0.096 m/s at work with a completion time of 20 minutes was obtained by simulation. This robot is useful for any environment, both structured and semi-structured.

Keywords: Area covered, Digital signal processing, Hand gesture, Robotic, Obstacle avoidance.