The study evaluates the Centimeter Level Augmentation Service (CLAS) of the Quasi-Zenith Satellite System (QZSS) for controlling a robot tractor. The QZSS transmits augmentation information through an L6 signal to enhance the positioning accuracy of the Global Navigation Satellite System (GNSS). Besides accessing the augmentation information through the L6 signal using a commercial QZSS receiver, this paper also introduces a method for using CLAS with a dual frequency receiver that cannot receive the L6 signal. Stationary and dynamic positioning experiments prove that the QZSS is able to improve the accuracy and availability of the current GNSS. Compensating for the biases of the CLAS positioning results relative to the current GNSS, a robot tractor works along with GNSS-based navigation within 5 cm accuracy.
Fuel consumption and power take-off (PTO) power requirement were measured for a 33.8 kW two-wheel drive tractor when used for operating a 1.6 m rotavator with 36 “L” shaped blades in sandy clay loam soil at an average soil moisture content of 8.8 ± 1% (dry basis) at IIT Kharagpur, India. Field experiments were conducted for a tractor with rotavator at seven different engine speeds (between 35 and 75% of full throttle engine speed), gear settings (L2 and L3) and depths of operation (60, 80 and 100 mm). Depth of operation, engine speed and gear setting were found to affect the fuel consumption of tractor. For the same PTO power consumption, lesser fuel consumption of tractor was observed in gear up conditions. A variation from −3.60 to −19.67% was observed while comparing the observed fuel consumption values with those predicted by the American Society of Agricultural and Biological Engineers (ASABE D 497.7) model. These variations were due to non-inclusion of gear settings in the ASABE fuel consumption model. Hence, an attempt was made to modify the ASABE fuel consumption model by incorporating gear settings in terms of speed ratio (peripheral speed of the rotavator to forward speed of the tractor i.e. u/v ratio). The developed fuel consumption model comprising engine speed, PTO power consumption and u/v could predict the observed values with a variation of ±6%.

