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This paper aims to incorporate one intelligent particle swarm optimization (IPSO) controller to realize an optimum path in unknown environments. In this paper, the fitness function of IPSO is designed with intelligent design parameters, solving the path navigation problem of an autonomous wheeled robot towards the target point by avoiding obstacles in any unknown environment.

This controller depends on randomly oriented positions with all other position information and a fitness function. Evaluating the position’s best values, this study gets the local best values, and finally, the global best value is updated as the current value after comparing the local best values.

The path navigation of the proposed controller has been compared with particle swarm optimization algorithm, BAT algorithm, flower pollination algorithm, invasive weed algorithm and genetic algorithm in multiple challenging environments. The proposed controller shows the percent deviation in path length near 14.54% and the percent deviation in travel time near 4% after the simulation. IPSO is applied to optimize said parameters for path navigation of the wheeled robot in different simulation environments.

A hardware model with a 32-bit ARM board interfaced with a global positioning system (GPS) module, an ultrasonic module and ZigBee wireless communication module is designed to implement IPSO. In real-time, the IPSO controller shows the percent deviation in path length near 9%.

The purpose of this work is to propose quad wheel robot with path navigation using an intelligent novel algorithm named as obstacle-avoiding intelligent algorithm (OAIA).

The paper proposes OAIA algorithm, which is used to minimize the path distance and elapsed time between source and goal.

The hardware implementation of the Quad Wheel Robot design includes a global positioning system (GPS) module for path navigation. An ultrasonic module (HC SR04) is mainly used as the sensing unit for the system. In the proposed scheme, the GPS locator (L80) is used to obtain the current location of the robot, and the ultrasonic sensor is utilized to avoid the obstacles. An ARM processor serves as the heart of the Quad Wheel Robot.

This paper includes real-time implementation of quad wheel robot for various coordinate values, and the movement of the robot is captured and analysed.

The proposed OAIA is capable of estimating the mobile robot position exactly under ideal circumstances. Simulation and hardware implementation are carried out to evaluate the performance of the proposed system.

The purpose of this work is to make an IoT-based low-cost and power-efficient portable system to control irrigation using a threshold value algorithm and to measure soil-irrigation-related parameters such as soil moisture, soil temperature, humidity and air temperature.

This paper presents a threshold value algorithm to optimize power consumption and to control irrigation process.

The system uses ESP-12F 8266 as the main microcontroller unit to monitor and control irrigation system. The system also consists of an actuator system that triggers automatically based on a threshold value algorithm. An open-source cloud platform is used to monitor and store all the data for future perspective. To make the system run for a long time without any human intervention, a solar panel is used as an alternate source of energy for charging the 12V lithium-ion battery. The battery takes 2.64 h for full charging considering peak intensity of sunlight. A capacitive moisture sensor is included using less expensive 555 timer and calibrated to measure water content in the soil. The 555 timer is used in astable mode of configuration to generate a signal of 572 KHz. The calibrated sensor data when compared with a standard SEN0193 moisture sensor shows an error of 3.4%. The prototype model is made to optimize the power consumption. This can be achieved by utilizing sleep mode of ESP-12F 8266. The total cost involved to make the system is 3900.55 Indian rupees and around US$54.90.

The device is tested in a flower garden during winter season of Nagaland, India, for 75 days to collect all the data and to automate the irrigation process.

The proposed threshold value algorithm optimizes the power consumption of the device, and wastage of water is reduced up to 60% as compared to the traditional method of irrigation.