news    views    podcast    learn    |    about    contribute     republish    

manipulators

by   -   November 22, 2008

This a subject for research and development, of course, but it’s my ‘job’ to make this vision as accessible as I can, to both anticipate what that R&D might produce and describe it in plain language.

 

First, these machines will necessarily have sensory components. Digital cameras and microphones are practically a given, but they may also have infrared imaging, radar and/or laser scanning, chemical sensors to provide something akin to a sense of smell, pressure/stress sensors for a sense of touch, probes for soil moisture, temperature, pH, O2 content, and nutrient availability, weather instruments, and some means of locating themselves very precisely relative to the boundaries of a field or other stationary reference. Compared to most machines, they will have available a rich collection of information about their environments, rich compared even with what human senses provide.

 

Next, they will have significant computer processing power, sufficient to take the data streams from all of these sensory devices, find patterns in them, compare them with each other and with historical data (including the exact position of every seed and when it was planted), create and update a real time 3-dimensional model of their immediate surroundings, locate items of interest within that model, choose a course of action, and send the detailed instructions to the machine’s moving parts, closely monitoring their progress.

 

Finally, they will have various moving parts, likely including high resolution or specialized sensory components that can be sent in for a closer look. Those moving parts might include a range of grips, from fine tweezers to something strong enough to uproot small trees, mechanical snips, lasers with enough power to fry a meristem, high-pressure water jets capable of slicing through the stem of a plant, fingers to move other plant material out of the way, a vacuum for sampling air at ground level or removing insects, sprinklers and sprayers, trowels of various sizes, and, of course, the soil probes mentioned earlier. Such tools might be combined into sets incorporated into units which could be plugged onto the ends of articulated arms and quickly switched out.

 

That’s a basic outline, but we need to return to the data processing hardware and the code it runs to fill out the picture, since it can make the difference between an expensive toy and a productive machine that more than pays for itself. A major task the processor must perform is resource scheduling, and to do that effectively it must sort actions into those that can be performed without moving anything massive (slow) and without switching out tool units, those which require either movement or a tool switch but must nevertheless be accomplished before moving on, those which can be left until a future pass over the same area but not indefinitely, and those which can be left undone unless it becomes convenient to do them. Efficient scheduling also means mapping the movement of even the smallest parts so they proceed smoothly from one thing to the next, without having to retrace their paths more than is unavoidable.

 

An important point to be taken away from the previous paragraph is that scrimping on computing hardware and software is likely to prove counterproductive, by reducing the overall capacity of the machine disproportionately. We should expect the computing components to represent a substantial fraction of the overall cost of the machine, and we shouldn’t be surprised if they also consume a substantial fraction of its energy budget. Better to invest an extra 10-20% to make a given physical machine capable of performing the work of two, and to invest 1 or 2 kilowatt-hours to save ten.

 

Something which should be apparent from this mental exercise as a whole is that what’s being proposed is largely a simple extrapolation of technologies which already exist. There are already mechanical arms and mechanical grips; there are already sensors and various means of controlling machine operation. What’s mainly missing is the software which would turn data streams into a 3D model in a horticultural context, choose what to do, schedule resources, and map out the details. That’s a lot left to be done, requiring a significant investment for a long term payoff, but it’s a fairly straightforward problem, and divisible into more manageable chunks. Let’s get to it!

 

Reposted from Cultibotics.

by   -   September 14, 2007

I’ve been thinking about this – the application of robotics to horticulture on a scale large enough to replace (some significant portion of) conventional agriculture – for a very long time, and I’m prone to glossing over points that may not seem at all obvious to others.

 

For example, if these robots that I’ve been talking about aren’t engaged in tillage, what are they doing? That remains an open question, since there are undoubtedly useful techniques I haven’t yet thought of, but, for an idea of what might be possible, consider what gardeners can accomplish with their own two hands and short-handled tools. That’s the scale of manipulation I have in mind, working with individual plants and the spaces into which they’re to be inserted.

 

Would such robots have human-like hands? [Probably] only in the vaguest sense; they’re likely to have manipulators with opposable, finger-like appendages. Would they stir the soil like a gardener does with a trowel? Maybe. Would they use something like snips to do pruning? Probably, although there might be a better approach to pruning than mechanical snips, like a high velocity water jet (such as are used to cut steel in some industrial settings).

 

It isn’t necessary, nor even desirable, to exactly replicate the set of techniques used by a gardener. Such machines would need a repertoire of techniques sufficient to manage a garden, but while some of their techniques might seem quite familiar, others might be quite beyond the capability of a human gardener.

 

For example, if a machine were able to identify a weed seedling early enough, it need only destroy the seedling’s meristem to interrupt the growth of a weed. This requires very little energy, and might be accomplished by a precisely targeted, high velocity water droplet [or flechette-shaped bit of ice, or even a pulsed laser]. Using this method, a machine might deal with several weed seedlings per second, limited only by the speed with which it could identify them and reorient the nozzle [or mirror], all without any disruption to surrounding plants.

 

More tenacious weeds that sprout from roots could be pulled out, except that they sometimes break off just below the soil surface, and their roots may pass below plants you’d rather not disturb. An option would be steam injection, through a tube inserted next to the stem. Another option would be coring, removing a cylinder of soil around the stem to a depth of a few inches. Yet another option would be to use electrical current to heat the weed. These are all techniques that a gardener might use, but, except for grabbing ahold of the base of the stem and pulling the plant out, they aren’t common.

 

Compared with weeding, seed planting would be relatively simple. On the other hand, transplanting seedlings started elsewhere would be more challenging, although mechanical systems for this purpose probably already exist and could be used as a model [and the use of compressed peat or compost pots, which are left in place to enrich the soil, would simplify the process].

 

Dealing with mid-season issues, like insects and nematodes, microbial infections, plant nutrient deficiencies, and so forth, is hugely complicated, and will require considerable development effort. But small-scale machines have an advantage in that they can deal very specifically with the effected leaf, plant, or location, and also in that, because they would revisit each location frequently, they should be able to catch problems early.

 

Harvest is also somewhat complicated, since each crop type presents its own set of challenges. What works for wheat doesn’t work so well for maize. What works for tomatoes won’t be sufficient for pumpkins. [Specialized] hardware attachments may be needed in some cases.

 

This vision isn’t a fantasy, but there’s a lot of work to be done.

 

Reposted from Cultibotics.



Autonomous Aircraft by Xwing
July 12, 2021


Are you planning to crowdfund your robot startup?

Need help spreading the word?

Join the Robohub crowdfunding page and increase the visibility of your campaign