Robots that help companies to future proof their workforce


Robotics systems allow for the implementation of goods to person picking thereby replacing the task of moving and lifting heavy load and eliminating travel and searching time, enabling better utilization of space and achieving higher efficiency.


Mobile robots can be autonomous or non-autonomous, in order to do the achieve motion, they rely either on guidance devices such as sensors or physical devices that allow them to travel a pre-defined navigation route in relatively controlled space.

Non–Autonomous Mobile Robots: Guided mobile robots or non-autonomous mobile robots require some sort of guidance system or instruction to make a movement that allows them to travel pre-defined navigation maps in a controlled environment. The pre-defined navigation map such as magnetic tape, bar codes, wire or sensors installed on the environments' floor that creating an inflexible environment.

Autonomous Guided Vehicle (AGV): This AGV requires the external guidance system in the form of magnetic strips to travel. These follow a rigid form of the preset route. Typical AGV applications incorporate transportation of raw materials, work-in-progress, and finished goods in support of manufacturing production lines, and storage/retrieval or other movements in support of picking in warehousing and distribution applications. AGVs provide automated material movement for a variety of industries including Automotive, Food & Beverage, Chemical, Hospitals, Manufacturing, Pharmaceutical, Paper.

Guided Fork-lifts: This specific AGV type is inspired by the conventional human manned forklifts. These forklifts are becoming increasingly complex and intelligent full of autonomy for some applications. These could manned/unmanned traveling with the help of external devices such as tablets, human, etc. The forklift AGV is designed to provide both horizontal and vertical movement of the load.


Autonomous mobile robots (AMR) are just like humans; can make their own decisions and then perform tasks accordingly. Autonomous robots can perceive their environment and remember it. Based on this info they navigate in a controlled environment without any predefined path or electro-magnetic guidance map, that way they offer flexibility to a large extent. AMRs also optimize the travel distance by calculating the shortest path for every mission & drive efficiency in the warehouse.


AMR for Good-to-picking: This includes robots bringing mobile shelf units filled with items to a workstation. In this case, pickers remain at their workstations while software-driven AMRs deliver shelves with different materials directly to the order pickers’ workstation. Picking Assist Autonomous Mobile Robots: In this case, the robots travel to pick locations, where operators deliver (“pick”) goods based on the robot’s needs. They are an AMR base with an operator interface that provides information about picking order. The robot tells the operator “I want this item and here is where you can find it”. The user interface is also interactive, being possible to provide further info about the product or receiving info from the operator such as “picking accomplished”. Unmanned Aerial Vehicles (UAVs): These are basically drones moving large products through the air in distribution centers with the help of RFID-scanning technology to offer real-time inventory visibility in the warehouse. Guided autonomously by remote control, UAVs can sense their environment and navigate on their own. Sorting Robots: These robots play an important role in high speed sorting esp in fulfilment centers. These robots work on a mezzanine with chutes/rabbit holes for location or order positions. Sortation is easily achieved by utilizing a fleet of sorting robots that sort the orders by dumping them through chutes/rabbit holes. The dropped orders or parcels are collected in sacks, gaylords or containers, which will be shipped directly to customers.


Navigation & Safety Systems that drive Mobile Robots is the core feature of mobile robots that enables the robot to direct itself from the current position to the desired destination. Navigation of mobile robots has been traditionally understood as solving the problem proposed by these three questions:

Where am I? What are the other places related to me? How do I get to other places from here?


With the advent of the Internet of Things (IoT) & Industry 4.0 mobile robots have been used for many applications in various fields such as industry, space, defence, and other social sectors. They have been used for material handling, picking, special applications such as disinfectant robots, etc. Therefore, an intelligent mobile robot is required that could travel autonomously in various static and dynamic environments. Several techniques have been applied for mobile robot navigation and obstacle avoidance. Let’s understand some of them, LIDAR Based Navigation – There are several ways for sensors to map and track the environment and estimate mobile robot positioning. LIDAR – Light Detection and Ranging technology is an essential ingredient in robotic autonomy and navigation. It allows mobile robots to extend outside controlled situations and pre-defined task functions in unpredictable and unfamiliar situations. Lidar sensors provide a constant stream of high-resolution, 3D information about the mobile surroundings, including locating the position of objects and people. Simultaneous Localization and Mapping (SLAM) technology enables the indoor capabilities of a robot with the Lidar data. The benefits provided by SLAM technology include “easy navigation without reliance on external technologies and real-time formation of 3D maps with reduced cost and power requirement”. Vision-Based Navigation – Vision system of the robot allows the mobile robot to see its environment as a human sees and interpret the information. Vision-based navigation technique uses a computer algorithm and data from optical sensors calculate the optimal path. The algorithm translates the visual information into concentration surroundings data so that the location of mobile robot can be identified & from there it chooses an optimal path to accomplish its goal. After that, the driving system of the mobile robot will be activated to reach its destination.

For further information on Autonomous Guided Solutions, see Addverb and Global AGV.