Robotics are viable solutions to many unsolved problems, and access to readily available and robust hardware is making it a reality. Apart from the hardware, incorporating existing smart technologies makes it possible for robots to map out their movements around objects more efficiently.
Smartphones contribute towards making the autonomous mobile robot (AMR) a reality and increase its applications in the modern world. Current phones can run navigation algorithms that control how an AMR behaves in a real-world setting.
So, how does this work and how can you integrate smartphone technologies into an AMR project?

Autonomous Mobile Robot Definition

An AMR is a robot that understands and moves through an environment without following a predetermined path or being under the control of an operator. One good example of this machine is the robotic vacuum. This robot makes use of sensors to interpret its environment, so it carries out a task in the most efficient path and manner possible.

AMRs navigate around buildings, workstations, fixed objects, and variable objects, such as people. They work out an efficient route to achieve a task and collaborate with operators in picking and sorting operations. For example, they perform non-value-adding functions, like picking up, transporting, and dropping products, as well as freeing up labourers to perform value-adding tasks, such as checking and packing orders.

How to Use Smartphones for Autonomous Robot Navigation

Smartphones are omnipresent in modern society, and up-to-date units have plenty of useful features that include high computational power, quality cameras, and an array of sensors. These built-in sensors make these phones an excellent candidate for use with AMRs.
A smartphone brings the following capabilities to an autonomous robot build:


An AMR’s navigation means that it can find its way across an environment. After all, a basic mobile robot does not stay in one position.


The robot finds its position in the environment using sensor measurements, which help it perform the next task at hand.


During movement, an AMR uses sensors to identify key features to map its environment. The most common sensors for mapping include digital cameras, sonar sensors, and proximity sensors. Mapping depends on the environment’s size, perception noise, and actuation.

Path planning

Real-time navigation requires looking ahead at outcomes of possible actions that an AMR takes using an onboard processor. A smartphone’s memory and its processor can help provide a relatively obstacle-free path with low memory usage and execution times.


Smartphone operating systems have a Software Development Kit (SDK) that allows programmers to create apps. SDK enables rapid prototyping and development, which makes the handheld device a promising candidate for sensing in AMRs.

How to Optimise Autonomous Robot Navigation Through Networking

Using Smartphones to Navigate an Autonomous Mobile Robot 1

Most AMRs carry out specific tasks, and more extensive facilities acquire different robots from diverse manufacturers—and they only communicate with similar robots.

To solve this problem, you require an interface that defines the interconnection procedures for AMRs and their operation control systems, so different robots using different phones share a standard network. The adoption of a single interface also encourages and speeds up the process of adopting a single AMR to carry out various tasks.

Robot Requirements for Autonomous Navigation

In addition to an intelligent digital servo drive for robots, building for autonomous navigation should meet the following criteria:

  • The AMR should work without requiring the installation of markers or infrastructure.
  • Applications should provide intuitive user interfaces and algorithms capable of configuring and optimising themselves, so anyone can implement them without expert knowledge.
  • Flexible navigation applications should allow AMRs to be easily adapted to various environments and applications.
  • Support for visualised travel plans and augmented reality is critical for simplifying and accelerating the adjustment of a fleet of AMRs.

Mobile robot learns to navigate on its own

The Berkeley Autonomous Driving Ground Robot (BADGR) is a successful AMR that utilises an autonomous self-supervision and learning-based navigation system to overcome real-world challenges. A processor gathers information from an onboard camera, a measurement unit sensor, GPS, and a 2-dimensional lidar sensor.

The robot learns from collected data to navigate autonomously and determine what actions lead to which outcomes through self-supervision and a neural network predictive model. The self-improving BADGE is capable of distinguishing off-road and urban environments to identify a path with the least obstacles.

In Conclusion

Creating an autonomous mobile robot is now possible with the right hardware. Adding smartphone technologies to the build helps address mapping, localisation, and path planning problems in robot navigation.

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