AUTOMATION
AI automated hoists require large amounts of sensory data to train themselves.
if the supply legislations had AI-essential health and safety requirements that were not met by the manufacturer, and if these resulted in user injury, then yes, the manufacturer would be culpable according to their declaration of conformity to the legislation. However, this would be true of any essential health and safety requirement.” It comes down to what is possibly one of the eternal verities, or at least a fundamental principle of the law: someone is always culpable. “It may be the manufacturers for not properly addressing the essential health and safety requirements. It may be the users for not properly planning the operation.” Are amended or updated regulations necessary to deal with all this, or is it covered by current regulations and principles? Again, it is probably too early to say. “It is possible, but until we can be sure that there is an issue there is little point writing requirements for one,” says Dobbs. “Most new requirements or changes to existing essential health and safety requirements are usually done following evidence of a problem, to prevent it occurring again. I have been involved in lifting equipment
26 | August 2025 |
www.hoistmagazine.com
product design and use for 25 years and could literally go on forever with the ‘what ifs’ but the only thing that can really be done is to reduce any risk to the acceptable minimum. “I don’t believe that anything can be truly
eliminated and as a last resort we all need to look out for ourselves and the people we work with.”
Keeping it practical
Now that the legalities have been made clear, insofar as they can be, let us turn to practicalities. What exactly can autonomous chain hoist do, and what do you need to make them do it?
Data is essential to autonomous operation, and data is provided through sensors, so any autonomous hoist will be laden with them. It is generally true that the more sensors you have on your equipment, the more quickly the AI learns and teaches itself. AI models are trained with large amounts of data, and the more sensors you have the more data they provide. It is an ongoing process – sensors continue to collect and provide the raw data throughout their working life, and that data continues to
train the AI model. In that sense AI and human operators are similar: the more experienced ones are better.
It is of course important that the sensors be of the right type and of adequate sensitivity. Laser sensing is one common technology for automating distance control. But lasers in an outdoor environment can be affected by fog, mist or bad weather. Induction-based sensors are an alternative. Metal – such as an end-stop – approaching a coil induces a current in it, and the closer the metal gets to the coil, the greater the current.
One application for distance sensors is in container terminals. Many of these are among the most fully automated lifting and material handling operations in the world. Autonomous operation is becoming the standard rather than the exception. Typically, container handling cranes operate above the storage area, positioning containers before lowering them into place. The hoist carriage on each crane travels along the length of the crane structure. Sensors fitted to the hoist carriage detect the distance to the ends of the structure. When that
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