Biomimetic Robotic System for Efficient Larva Removal in Paddy Fields:A Simulation-Based Study

The development of innovative pest management techniques is essential for better farming, especially with regard to rice water weevils (RWW). Although using chemicals is effective, there is significant risks for both the environment and human health. This proposed work details the usage of a duck-shaped robot which mimics features of natural creatures like duck to assist in eliminating RWW larvae in rice fields without the need of pesticides. The robot’s efficiency and utility are enhanced by a number of design elements. A major priority was terrain adaptability to test the guarantee that the robot could navigate across uneven and wet paddy fields. Additionally, bioinspired algorithms were employed which includes the Dynamic Swarm Optimization with Communication Networks, A* Algorithm with Heuristic Function Learning, the Adaptive Greedy Algorithm with Learning, Ant Colony Optimization with Pheromone Decay and Environmental adaptation, Particle swarm Optimization with multi-objective optimization. Different performance metrics such as total time, total distance, average time per larva, average distance per larva, frequency of larva removal and average speed are calculated for each simulation run. On an average, the robot removed 1.64 larvae per second, took 0.61 seconds per larva removal and the speed was around 1.47 units per second. On comparison between the algorithms ACO with Pheromone Decay and Environmental adaptation, PSO with Multi-Objective Optimization, ACO consistently performed well than PSO in terms of total time taken across all runs. The average total time taken by ACO is 1211.93 seconds, and the total time taken by PSO is 1480.03 seconds. Selection of algorithm depends on different factors such as for quick and local adjustments Adaptive Greedy is effective; for exact paths, A* is better; for team efforts in robots and faster removal of larvae, Dynamic Swarm and ACO fit well and for overall balanced performance across various metrics, ACO works best. This way empowers farmers with eco-friendly, efficient pest control, enhancing crop health and yield with minimal effect.