Five Mistakes Robotics Startups Should Avoid

A few years ago, robots were inflexible, hard to program, stationary and, except in industries with long production cycles, prohibitively expensive. Moreover, they were blind, deaf, unable to sense touch, not intelligent, and could not be trusted to work near people.

Today, thanks to greater computing power, sophisticated software that simplifies programming and operation, machine learning (ML), machine vision (MV), more and better sensors, and other advances, robots are more flexible and capable than ever. Moreover, the relative cost of robotics has decreased.

As demand for quality and productivity grows amid a shortage of skilled workers, the use of robotics in manufacturing is expanding. New robotic applications are emerging in many industries, including pharmaceuticals and agriculture. Robots are also leaving factory floors to perform more household services and other tasks.

In its World Robotics Report, the International Federation of Robotics estimates the cumulative market value of non-manufacturing professional service robots for 2019 through 2021 at $37 billion, almost three times the previous figure.

These new opportunities are attracting a growing number of pioneers in new robotic applications. Here are five pitfalls robotics startups should avoid, according to The Robot Report. 

Developing software in a bubble

Many startups begin by developing software that automates a manufacturing task. The robot is often an afterthought, chosen based on cost and availability.

Images of factory robots often conjure up automotive assembly lines, where robots work with synchronized precision easily. However, this is the result of years of collaborative work by many engineers coordinating and refining the process.

Startups should consider all components of the task at the outset to make integration easier at the end. This is especially relevant for companies that have little or no experience in robotics.

Underestimating the step from prototype to production

When developing a new product, a concept goes from idea to prototype during design and construction. A common startup mistake is underestimating what it takes to move from prototyping to production.

Detailed engineering must make every deployment of the solution successful for the end user. The solution must be durable, reliable and easy to use. After installation, the customer will need support, and startups must think about how to meet these service needs.

The transition between these stages requires a significant change in the skills a startup needs. The last stage will require far more time and effort than many expect.

Not building a turnkey solution

A startup's automation offering must account for the relevant settings. The product must be easy to install and convenient to use.

If deployment requires engineers to be on site for several days performing custom setup, the number of solutions will be limited not only by the speed of building systems but also by the number of skilled engineers available for field work.

Startups that want to build a system for thousands of locations need a turnkey solution.

Focusing on technology rather than problems

Automation users usually do not care how the solution works, whether the system uses machine learning or new self-correcting algorithms, but whether it works.

Startups often have trouble with this, especially in their early years. When trying to attract venture capital, startups need to explain not only the problem they solve but also the cutting-edge technologies they use.

However, when offering customers a new version of a robotic forklift, they need to know how well and how fast it works and what happens if it jams, not about the elegance of the design. The focus should be on solving users' problems, which is what they will pay for.

Trying to reinvent the wheel

The worst thing a startup can do is spend time, money and effort creating products that are already on the market. For example, building its own motion control software. Robot hardware and motion planning software have been developed in symbiosis over the past 40 years, with the software optimizing the robot's reliability and service life to achieve the best return on investment for the customer.

Another startup mistake is developing their own robotic arms from scratch when they already exist. Instead, it is worth leveraging the years of knowledge embedded in the manipulators currently available from major robotics suppliers.

Moreover, given the size of investments and the importance of return on industrial automation, an unknown system with unproven results offered by a newcomer may pose too great a risk for the end user. No one wants to pay to be a guinea pig or a bad example that becomes a lesson for other companies.

Startups should consider collaborating with well-established robotics partners to use their knowledge and experience, as well as their distribution systems, support networks and brand recognition, to their advantage.