Global Automated Guided Vehicle Market to Reach $2.5 Billion by 2024

Automated Guided Vehicles or Automatic Guided Vehicles (AGVs) are computer controlled, wheel-based and driverless vehicles used for transporting material loads or products from one place to another. The term AGV includes all transport systems that are capable of operating without an onboard operator. Automated vehicles like pallets, carts or trays are programmed to move material loads between manufacturing and warehouse facilities without human intervention. These sophisticated machines use several guidance technologies like radio waves, lasers, floor-surface mounted magnetic tape or bars, optical sensors or vision cameras for navigation. The movement of the autonomous vehicle along the floor is controlled by a combination of CAD-based software and sensor-based guidance systems. The software determines pre-defined paths and directs the autonomous vehicle to pick-up/drop-off location. The routing decisions are based on real-time feedback received on operating conditions. The precisely controlled movements of AGVs, including acceleration, deceleration, and automatic obstacle detection bumpers ensure safe movement of material loads.

Amid the COVID-19 crisis, the global market for Automated Guided Vehicle is projected to reach US$2.5 Billion by 2024, registering a compounded annual growth rate (CAGR) of 6.8% over the analysis period. Europe represents the largest regional market for Automated Guided Vehicle, accounting for an estimated 31.6% share of the global total. The market is projected to reach US$803.2 Million by the close of the analysis period. China is expected to spearhead growth and emerge as the fastest growing regional market with a CAGR of 9.4% over the analysis period. The strong focus shed on automation and production efficiency in the era of smart factory and industry 4.0 represents a key factor spurring investments in automated guided vehicles (AGVs). Smart manufacturing is the new revolution that will mark the next generation of production. Self-aware, self-optimized, self-configured equipment with the ability to wirelessly communicate with each other; advanced networking; real-time controls; more closer alignment of business management and plant floor activities and supply chains; additive manufacturing are few of the disruptive smart factory trends currently underway. Among these, "automation" lies at the heart of the smart factory concept. Almost like the beating heart, automation provides the foundation for the creation of knowledge-embedded manufacturing operations.

Benefits offered by AGVs include round the clock transit of materials; traceability that ensures that the plant manager remains informed of the material movement around the facility; ability to time-stamp pick-up, transit and delivery of items which can help in route optimization and improvements; ability to integrate information generated by AGVs into enterprise resource planning (ERP) or materials resource planning (MRP) systems; among others. There is also a growing trend in the market towards the use of multi-functional AGVs which in addition to transporting materials around the plant, can also perform other tasks which can enhance efficiency on the plant floor. For instance, when equipped with tool attachments like clamping mechanisms and positioning sensors and fixture, AGVs can also be made to perform assembly line functions.

AGVs are moving from wired operation to wireless operations. Traditional wired navigation involves the use of wires embedded into the plant floor. The signal emitted by the wire is then picked by a sensor integrated in AGVs which when propels the machine in the direction of the signal guiding it all along the wired floor. From guided to self-driving is a trend that need to be underlined given its ability to improve the efficiency of these vehicles by a factor of X. Laser-based perception or machine vision and navigation algorithms; innovations in a wide variety of sensors like hall-effect sensors, position sensors, and gyroscopes need to for context and environment/location awareness; on-board programmable logic controller (PLC) that controls acceleration or braking; embedded artificial intelligence that helps learn and take autonomous decisions in situations that are beyond the pre-programmed scenarios; are all enabling the development of self-driving, fully autonomous machines.