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Home Made Robots / Re: soldering motor attachments
« Last post by ranch vermin on Today at 08:59:07 am »
yeh art, probably not a good idea to solder the motors together,   but use of lead could be in the design if it wasnt used in a critical bearing point.

Im not sure what to do,  maybe the plastic is a better bet,   i think making a good cube between the 90 degree motors would be strong enough, then u cant shake the robot apart.

bit by bit,  the body is coming together,  the brain is more important to get,  its what all the robots you see are missing today.
Home Made Robots / Re: soldering motor attachments
« Last post by Art on Today at 02:58:38 am »
There are many searchable videos/articles on converting recyclable plastics like milk containers and such to solid, usable plastic items that can be repurposed for a variety of things.

Solder is nothing more than a bridge between two metallic points in order to maintain or allow conductivity, not structure.

Many of those low-cost camera gimbals might do the trick for your needs.

Lastly, there is the ever trusty J.B.Weld here in the states that even turns clear after being mixed and hold with something around 4,400 psi of strength yet remains, fileable, sandable, drillable and paintable. Fairly cheap as well!

Good luck on your project but no structural solder. A lot of it is still made with lead.
General Project Discussion / Re: Wish Lists
« Last post by ranch vermin on May 23, 2018, 12:46:11 pm »
confibularity.    pack of fixes.
Home Made Robots / Re: soldering motor attachments
« Last post by ranch vermin on May 23, 2018, 12:43:19 pm »
Maybe if you bake sugar bonds in an oven...  :)

There might be some way to do it,  sometimes things only work if they are done just right.   (like mixing sugar with something and getting something more than a hard ginger nut.)  ginger nuts are pretty hard, but there could be a way to get it tougher,  takes many iterations and experiments. =)
General Project Discussion / Re: Wish Lists
« Last post by Art on May 23, 2018, 12:42:34 pm »
@ infurl,

"...That way I can seamlessly merge all the tuples, and hence all the data, into a single body of knowledge, complete with provenance and confidence levels...."

Perhaps you have seen that Black Adder sketch where Dr. Johnson announces his compiled and completed English Dictionary. Completed that is until Black Adder offers his own "contrafibularity", causing the wise Dr. to pencil in this newest word. ;)

Will it ever be finished? Probably not but I do admire your goal and determination! Best of luck in this endeavor!
General Chatbots and Software / Re: KorrBot
« Last post by LOCKSUIT on May 23, 2018, 12:28:56 pm »
Yep that's better in all ways around. Human Speech only developed not long ago. Visual sentences were what all animals used only. "Hammer smashing crystals.". There's no way to interpret it wrongly. It's universal. First you teach it, then it discovers more.

General Chatbots and Software / Re: KorrBot
« Last post by ranch vermin on May 23, 2018, 12:24:05 pm »
wow first time ive seen this,   done a very good job, showing off the power of assignment.

This would be cool for a supervised learning/implanting technique.

what I would think to do if I was making this would be to combine it with machine vision, and then the robot could search through the video for the labels and maybe you could get the robot to have "implantable methods" - that are just written in plain english to it.

Cause how else would you do it?  You need labels or you cant tell the robot anything.  otherwise its just stuck in its primitive goal or your doing basicly this but in a more primitive way.

Why cant you just tell the robot and then it learns - because theres the problem where it cant distinguish the language labels from the actual thing that it associates with, and this would skip past that issue.
Home Made Robots / Re: soldering motor attachments
« Last post by ivan.moony on May 23, 2018, 12:14:42 pm »
Maybe if you bake sugar bonds in an oven...  :)
General Chatbots and Software / Re: KorrBot
« Last post by LOCKSUIT on May 23, 2018, 12:10:13 pm »
Another video to add to the base!

Robotics News / Making driverless cars change lanes more like human drivers do
« Last post by Tyler on May 23, 2018, 12:00:02 pm »
Making driverless cars change lanes more like human drivers do
23 May 2018, 4:59 am

In the field of self-driving cars, algorithms for controlling lane changes are an important topic of study. But most existing lane-change algorithms have one of two drawbacks: Either they rely on detailed statistical models of the driving environment, which are difficult to assemble and too complex to analyze on the fly; or they’re so simple that they can lead to impractically conservative decisions, such as never changing lanes at all.

At the International Conference on Robotics and Automation tomorrow, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) will present a new lane-change algorithm that splits the difference. It allows for more aggressive lane changes than the simple models do but relies only on immediate information about other vehicles’ directions and velocities to make decisions.

“The motivation is, ‘What can we do with as little information as possible?’” says Alyssa Pierson, a postdoc at CSAIL and first author on the new paper. “How can we have an autonomous vehicle behave as a human driver might behave? What is the minimum amount of information the car needs to elicit that human-like behavior?”

Pierson is joined on the paper by Daniela Rus, the Viterbi Professor of Electrical Engineering and Computer Science; Sertac Karaman, associate professor of aeronautics and astronautics; and Wilko Schwarting, a graduate student in electrical engineering and computer science.

“The optimization solution will ensure navigation with lane changes that can model an entire range of driving styles, from conservative to aggressive, with safety guarantees,” says Rus, who is the director of CSAIL.

One standard way for autonomous vehicles to avoid collisions is to calculate buffer zones around the other vehicles in the environment. The buffer zones describe not only the vehicles’ current positions but their likely future positions within some time frame. Planning lane changes then becomes a matter of simply staying out of other vehicles’ buffer zones.

For any given method of computing buffer zones, algorithm designers must prove that it guarantees collision avoidance, within the context of the mathematical model used to describe traffic patterns. That proof can be complex, so the optimal buffer zones are usually computed in advance. During operation, the autonomous vehicle then calls up the precomputed buffer zones that correspond to its situation.

The problem is that if traffic is fast enough and dense enough, precomputed buffer zones may be too restrictive. An autonomous vehicle will fail to change lanes at all, whereas a human driver would cheerfully zip around the roadway.

With the MIT researchers’ system, if the default buffer zones are leading to performance that’s far worse than a human driver’s, the system will compute new buffer zones on the fly — complete with proof of collision avoidance.

That approach depends on a mathematically efficient method of describing buffer zones, so that the collision-avoidance proof can be executed quickly. And that’s what the MIT researchers developed.

They begin with a so-called Gaussian distribution — the familiar bell-curve probability distribution. That distribution represents the current position of the car, factoring in both its length and the uncertainty of its location estimation.

Then, based on estimates of the car’s direction and velocity, the researchers’ system constructs a so-called logistic function. Multiplying the logistic function by the Gaussian distribution skews the distribution in the direction of the car’s movement, with higher speeds increasing the skew.

The skewed distribution defines the vehicle’s new buffer zone. But its mathematical description is so simple — using only a few equation variables — that the system can evaluate it on the fly.

The researchers tested their algorithm in a simulation including up to 16 autonomous cars driving in an environment with several hundred other vehicles.

“The autonomous vehicles were not in direct communication but ran the proposed algorithm in parallel without conflict or collisions,” explains Pierson. “Each car used a different risk threshold that produced a different driving style, allowing us to create conservative and aggressive drivers. Using the static, precomputed buffer zones would only allow for conservative driving, whereas our dynamic algorithm allows for a broader range of driving styles.”

This project was supported, in part, by the Toyota Research Institute and the Office of Naval Research.

Source: MIT News - CSAIL - Robotics - Computer Science and Artificial Intelligence Laboratory (CSAIL) - Robots - Artificial intelligence

Reprinted with permission of MIT News : MIT News homepage

Use the link at the top of the story to get to the original article.
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