Mobiltiy Aware Deep Q-Reinforcement Learning Model for Building Efficient Agriculture Autonomous Robots

Abstract

in recent years the research has shown that modern farms may be helpful in producing the higher amount of yields along with superior quality. Moreover, this might also help in being least dependent about the labor force. Management of digital farming and site-specific precision are few solutions, which depends on the sensor technology. Moreover, the field data collection is the best only with feasible utilization of agriculture robots (AR). For improving agriculture productivity the sensor are placed across land (geographically), these sensor sends information to multiple robots for carrying certain task such as soughing, harvesting etc. This manuscript conducted survey of various industrial robots model for agriculture environment. Using industrial robots for agricultural purpose is practically not a viable option due to complex environment. Cognitive architecture that exhibits human cognitive thinking is used for learning dynamic and complex environment with good result. In recent times, Society of Mind Cognitive Architecture (SMCA) has proposed using multi-agent and (MA) and Reinforcement learning (RL) technique. However, it is generally difficult to solve Markov decision process (MDP) problem. Thus, cannot be used under dynamic mobility and complex nature of agriculture environment. This is because MDP has many variables. For overcoming research issues, this work present mobility aware Deep Q- Reinforcement Learning (MADQRL) cognitive learning method for Society of Mind Cognitive Architecture by combining both RL and DL technique. The MADQRL are utilized for controlling mobility and communication power of robots according to dynamic environment prerequisite. Experiment outcome shows the proposed MADQRL method attain better performance than existing cognitive learning method considering memory efficiency, learning efficiency, and energy utilization.