Lakhasly

Different types of automation systems for farm vehicles are invented and introduced in agricultural fields.Experimental tests illustrated that in the stationary test, distance measurement errors were between +-0.31% and +-0.37% in the distance range of 5.2-6.8 m. In the dynamic test at the four constant travel speeds, maximum lead error (when the master was ahead), and maximum lag error (the slave was ahead), were 32 and 23 cm, respectively.As an example of drive assistance, to allow the operator to focus more on other systems and processes taking place on and around the machine, Foster et al. developed an automatic velocity controller for a self-propelled hydrostatic drive-train windrower to improve operator performance, reduce operator fatigue and increase the productivity of the machine.As implements get wider, this task becomes more difficult, so different marking systems have been developed, such as using a disc coulter to mark the correct driving distance while cultivating and using foam markers while spraying and crop edge detection.In terms of the involvement of a human operator in maneuvering the vehicle, Mizushima et al. classified automated guidance systems into operator-assistant and autonomous systems.More precisely, Blackmore et al. categorized automated farm machinery on three groups; (1) drive assistance (2) automatic steering and (3) autonomous machines.Various driver assistance help to reduce the complexity and difficulty of field operations as well as to help improve efficiency.An operator-assistant system uses an auto-track function to guide the vehicle, following crop rows automatically, and relies on a human operator to drive the vehicle to make endrow turns [10].A Proportional-Integral-Differential (PID), closed loop control system, is applied to satisfy the desired conditions.In a research by Zhang et al. a leading tractor as a master and another unmanned agricultural machine, which follows the other leading one as a slave are evaluated.Both vehicles used GPS signals and inertial sensors to evaluate their positions.Two tractors cooperated in three modes: standard mode, obstacle avoidance mode and headland turning mode.In another similar research Lee et al. evaluated, two self-propelled citrus canopy shake and catch for citrus harvesting, worked in pair (mater and slave), one on each side of the row of citrus trees.Two mentioned vehicles synchronized accurately using laser scanner.High trackin

Different types of automation systems for farm vehicles
are invented and introduced in agricultural fields. These are
different from those that are used in stationary and post
processing automatic systems. In terms of the involvement
of a human operator in maneuvering the vehicle, Mizushima et al. classified automated guidance systems into
operator-assistant and autonomous systems. An operator-assistant system uses an auto-track function to guide
the vehicle, following crop rows automatically, and relies
on a human operator to drive the vehicle to make endrow turns [10]. While autonomous systems are the result
of self-sustaining processes of constitution of an identity
under precarious circumstances [11]. More precisely,
Blackmore et al. categorized automated farm machinery
on three groups; (1) drive assistance (2) automatic steering
and (3) autonomous machines. Various driver assistance
help to reduce the complexity and difficulty of field operations as well as to help improve efficiency. Overlaps and
skips of operations should be kept to the minimum rate
for higher efficiency. The distance between the working
envelope of the implement and the previously treated area
is usually judged by eye and relies on the skill and experience of the driver. As implements get wider, this task
becomes more difficult, so different marking systems have
been developed, such as using a disc coulter to mark the
correct driving distance while cultivating and using foam
markers while spraying and crop edge detection. In automated steering the driver attends to other tasks such as
unknown object avoidance, safety, vehicle awareness, end
of row turn and other non-automated tasks. these systems may remove part of the steering task from
the operator, they cannot be considered to be autonomous,
as the driver must carry out many other tasks apart from
steering. A clear distinction between an automatically
steered tractor and an autonomous tractor is the presence
of operator. An autonomous tractor must be capable of
working without an operator [12]. As an example of drive
assistance, to allow the operator to focus more on other
systems and processes taking place on and around the
machine, Foster et al. developed an automatic velocity controller for a self-propelled hydrostatic drive-train windrower to improve operator performance, reduce operator
fatigue and increase the productivity of the machine. A
Proportional–Integral–Differential (PID), closed loop control system, is applied to satisfy the desired conditions. The
experimental results show that the velocity control system
is stable, tracks the reference and rejects disturbances sufficiently well [13].
Other than these categories there are some automated
vehicles that act as a slave for a master vehicle. A master–slave system between agricultural vehicles can be
regarded as an intermediate step on the road to completely
autonomous agricultural vehicles. In a research by Zhang
et al. a leading tractor as a master and another unmanned
agricultural machine, which follows the other leading one
as a slave are evaluated. Both vehicles used GPS signals
and inertial sensors to evaluate their positions. The path
segment to guide the unmanned slave vehicle will be transferred from the master tractor periodically using wireless
communication. Two tractors cooperated in three modes:
standard mode, obstacle avoidance mode and headland
turning mode. They concluded that a significant challenge
is determination of the dimension of a tolerance zone that
slave tractor will move in it [14]. In another similar research
Lee et al. evaluated, two self-propelled citrus canopy shake
and catch for citrus harvesting, worked in pair (mater and
slave), one on each side of the row of citrus trees. Two
mentioned vehicles synchronized accurately using laser
scanner. It is mentioned that unreliable synchronization
causes inefficiency in the catching system which degrades
the capability of the citrus harvesting system. A GPS is
used on the master only for speed measurement. For synchronization, the measured speed transferred to a PC on
slave via a wireless RS232 transducer. A PID control system is applied for steering actuation and the K values are
determined by Ziegler–Nichols rules. Experimental tests
illustrated that in the stationary test, distance measurement
errors were between ±0.31% and ±0.37% in the distance
range of 5.2–6.8 m. In the dynamic test at the four constant
travel speeds, maximum lead error (when the master was
ahead), and maximum lag error (the slave was ahead), were
32 and 23 cm, respectively. Root Mean Square Error
(RMSE) was smaller than 10 cm [15,16]. Master–slave system is applicable in some limited farm operations.
However, since two machines are working in a close manner, the required hardware and software is not very expensive and simple techniques are applied. High trackin