Search

Forklift trucks are essential tools for manual pallet handling. Storage heights at high rack storage areas often reach up to 12 meters in which bulky Cargo can hide the view of forklift operator. Pallet recognition is afundamental issue for Industries & warehouses, Particularly, a pallet recognition approach is presented to recognize pallets in the warehouses, based on calculating the degree of similarity at each location of the palette. Once a pallet has been recognized, it is matched that data with stored data. Transporting and handling pallets in a storage area is the essential function of a forklift truck. The view of the forklift truck driver is limited during his work by the lift pole, the fork, and the roof of the forklift truck. If a pallet is loaded the view is additionally hindered by its load. When the storage rack is not at a ground level or near to it the pallet handling becomes more difficult because of the distance between the forklift truck driver and the storage rack. To support the forklift truck driver during his work, there is need to advance theforklifts with different technologies and systems which provide diversity in Working and safety, security for forklift operator and other workers. This paper shows various results from research on different pallet recognition and forklift operation systems.

Pallet recognition is an important task in warehouse operations that involves identifying and classifying pallets based on their size, shape, and colour. The efficient and accurate recognition of pallets is crucial for ensuring the smooth and safe movement of goods within a warehouse. In recent years, advancements in computer vision technology have paved the way for the development of automated pallet recognition systems that can be integrated with forklifts. This paper introduces a system for pallet recognition on forklifts using Raspberry Pi and ultrasonic sensors. The proposed system utilizes ultrasonic sensors to measure the distance between the forklift and the pallet, providing valuable information for the pallet recognition system. The system is designed to accurately identify and classify pallets based on their size. The use of ultrasonic sensors provides a costeffective and easily scalable solution, allowing the system to be used in a variety of warehouse environments. The accuracy of the proposed system is evaluated through testing on a variety of pallet types, and the results indicate a high level of accuracy in identifying and classifying pallets. The system is also designed to be user- friendly and can be easily integrated into existing forklifts.

In this paper a Raspberry Pi based automated solution system focused on the real-time face monitoring of people to detect both face masks and body temperature with the help of MLX90614 sensor has been proposed. This is implemented using Python Programming with OpenCV Library, TensorFlow, Dlib Module. A security clearance system is deployed that will allow that person to enter if they are wearing a face mask and their body temperature is in check with WHO guidelines. A programmed hand sanitizer apportioning machine is mechanized, non-contact, liquor-based hand sanitizer gadget. Liquor is essentially a dissolvable, and furthermore a generally excellent sanitizer when contrasted with fluid cleanser or strong cleanser, likewise it needn't bother with water to wash off since it is unpredictable furthermore, disintegrates in a split second after application to hands. It is too demonstrated that a convergence of >70% liquor can execute Covid in hands. Here, we have used IR sensor detects the hand put close to which detectsthe distance and the outcome isthe pump starts running out the hand sanitizer.Thus, the above said system will help the society by saving time and also helps in contaminating the spread of coronavirus. This can be implemented in public places such as colleges, schools, offices, shopping malls, etc. to inspect people. Keywords: Deep Learning, Open CV, Keras, Python, Tensor Flow, Computer Vision, Raspberry Pi, COVID-19, DLib, Sensor, Sanitizer, Infrared sensor.

LiDAR, typically used as an acronym for “’light detection and ranging’”, is essentially a sonar that uses pulsed laser waves to map the distance to surrounding objects. It is used by a large number of autonomous vehicles to navigate environments in real time. Its advantages include impressively accurate depth perception, which allows LiDAR to know the distance to an object to within a few centimetres, up to 60 metres away. It’s also highly suitable for 3D mapping, which means returning vehicles can then navigate the environment predictably —a significant benefit for most self-driving technologies. One of the key strengths of LiDAR is the number of areas that show potential for improvement. These include solid-state sensors, which could reduce its cost tenfold, sensor range increases of up to 200m, and 4-dimensional LiDAR, which senses the velocity of an object as well as its position in 3-D space. However, despite these exciting advances, LiDAR is still hindered by a key factor; its significant cost. LiDAR is not the only self-driving detection technology, with cameras as the major rival, championed by Tesla as the best way forward. Elon Musk has described LiDAR as “a fool’s errand” and “unnecessary”. The argument runs that humans drive based only on ambient visible light, so robots should equally be able to. A camera is significantly smaller and cheaper than LiDAR (although more of them are needed), and has the advantage of seeing in better resolution and in colour, meaning it can read traffic lights and signs. However, cameras have a wide host of characteristics that make them tricky to use in common driving conditions. Whereas LiDAR uses near infra-red light, cameras use visible light, and are thus more susceptible to issues when faced with rain, fog, or even some textures. In addition, LiDARs do not depend on ambient light, generating their own light pulses, whereas cameras are more sensitive to sudden light changes, direct sunlight and even raindrops