Seeing is believing

 

How is technology changing the way robots ‘see’ and perceive the world? Michelle Mooney asks the experts.

Robotic perception is at the forefront of transformative change, redefining how machines interact with their surroundings and process complex data. In an era where automation extends beyond industrial confines into diverse fields such as education, underwater exploration, and logistics, the ability of robots to perceive and understand their environment has become a fundamental determinant of their success. Here, Robotics & Automation Magazine brings together insights from industry leaders who are navigating the intricate challenges and breakthroughs associated with developing advanced perception systems.

Louis Esquerre-Pourtere, head of research and development at Exotec, discusses the essential role of designing hardware and algorithms that adapt to fluctuating conditions, ensuring seamless, 24/7 operations in demanding environments; Dr Farshad Badie, dean of faculty of computer science and informatics at the Berlin School of Business and Innovation, sheds light on integrating multi-sensor data fusion for interactive, real-time applications in educational settings; and Coena Das, robotics engineer at the National Robotarium, highlights the unique challenges faced in underwater robotics, where traditional sensors fall short, requiring novel solutions such as acoustic imaging.

Together, they explore a range of sensor technologies, the balance between processing speed and accuracy, and the integration of AI and machine learning, to understand the strides and hurdles in crafting robots capable of perceiving their environments as dynamically as humans do.

What are the key challenges your company faces in developing robot perception systems?

Louis Esquerre-Pourtere: Perception is the first step in robot movement; it is essential that a robot should have a clear understanding of its environment, this allows it to make the right decision, especially if it is autonomous. At Exotec we see several key elements that can be challenging regarding perception. One of these is the environment the robot operates in. The solution, in this case, the robot, must be reliable and the service provider must consider the possible variations of the environment such as light, temperature, dust, floor levelling, and many more environmental considerations. All those parameters need to be taken in account in both the hardware design and the algorithm of the robot to provide an end- to end solution that will be compatible with a maintenance-free 24/7 usage. Other aspects to consider are precision and performance: the perception solution must be designed according to the robot position needs to find the right balance between precision and computing time. Bad choice in the hardware design can lead to non-competitive solution or to a solution that is unreliable and causes problems onsite.

Dr Farshad Badie: One of the key challenges we face when developing robotic perception systems is creating robust systems that can seamlessly adapt to dynamic environments, such as educational settings where student interactions vary. Ensuring that our robot, BOTSBI can accurately interpret and respond to complex student inquiries in real-time is another challenge, as it requires sophisticated natural language processing and a deep understanding of context. Additionally, integrating BOTSBI with our diverse virtual learning environments (that operate across multiple platforms) presents technical hurdles in terms of compatibility and real-time responsiveness. Getting perception wrong here can lead to all kinds of problems with general intelligibility.

Coena Das: Sensor limitations are a big problem. Even the most advanced sensors have inherent limitations that must be addressed. These include range constraints that limit the robot’s perception distance, resolution issues that affect the detail of sensed data, and interference problems that can arise from environmental factors or other nearby sensors. Another common issue is that of real-time processing. One of the most critical challenges is achieving a balance between the accuracy of perception and the computational resources required for real-time processing. High-accuracy algorithms often demand significant processing power, which can lead to delays in decision-making. Furthermore, there are many data challenges that make the widespread implementation of robotic perception systems difficult right now, which is why they can only be used in limited circumstances. The development of robust perception systems relies heavily on diverse and high-quality training datasets. Obtaining such datasets can be time-consuming and expensive, particularly for specialised or rare scenarios. Additionally, the process of annotating this data accurately is labour-intensive and prone to human error. This means we still have a long way to go in perfecting this process.

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