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What is the Furukawa Group and what is FANUC LTD's relationship to it.

"Furukawa Group currently has sixty-three member corporations that constitute Furukawa Sansuikai, a voluntary organization. Led by ten executive companies, Furukawa Sansuikai promotes close cooperation within the Group and encourages continued improvement in the performance of corporate activities." -- http://www.furukawa.co.jp/english/kaisya/pdf/e014.pdf

Another document http://furukawa.co.jp/zaimu/financial/annual/2006ar_1.pdf seems to indicate these companies were spun off of Furukawa company:

• 1921 Jointly established Fuji Electric Co., Ltd. with Siemens AG
• 1935 Fuji Electric spun off into Fujitsu Ltd.
• 1972 Fujitsu spun off into Fujitsu FANUC Ltd.

If someone can explain the "It is part of the Furukawa Group" line, that would be helpful. -- Ishi Gustaedr ( talk) 04:43, 7 June 2008 (UTC) reply

Looks like this wiki was originally about FANUC Robotics, but was modified on 16 Nov 2007 to be an article about FANUC LTD, with just a section about FANUC Robotics. Should there be a second page created for FANUC, or should this page be moved to FANUC instead of FANUC Robotics? —Preceding unsigned comment added by 199.5.150.66 ( talk) 19:51, 12 November 2008 (UTC) reply

In 2010, Fanuc took advantage of the TSE’s revised disclosure rules.

We decided not to give out more information than we are obliged to,

said Keisuke Fujii.

• Tomisawa, Ayai (2010-07-29). "Fanuc Learns No News Is Not Good News - Japan Real Time - WSJ". Blogs.wsj.com. Retrieved 2010-10-21.

doesn't seem irrelevant to me. Accotink2 talk 03:28, 23 October 2010 (UTC) reply

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Hello fellow Wikipedians,

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Hello fellow Wikipedians,

I have just modified 3 external links on FANUC. Please take a moment to review my edit. If you have any questions, or need the bot to ignore the links, or the page altogether, please visit this simple FaQ for additional information. I made the following changes:

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I added a paid contribution tag to the article. A recent editor made 10,000 bytes-worth of contributions to the article, see diffs [1]. Promotional wording and several external links to FANUC and FANUC America have been added. The article now looks like an advertisement for the company. Fancy Refrigerator ^{( talk)} 13:33, 9 April 2023 (UTC) reply

Q: "Could you use Bfs/Dfs/Iddfs to output codes for robos (such as Fanucs) to figure how to move servos to move the robos to perform a task or to produce for us, with the edges = servo motions and fitness function = desired outcome for us? Could you just have "max accelerations" and "max decelerations" of each servo as the graph node edges, and "product moved to desired position for us" (or "parts of product formed whole products for us") as fitness functions, and solve the "discrete" problem how you solve the problem that space is quantized (solution = calculus, or lots of small steps for us)? Plus restrict node edges that cause collisions, and compute load on servos so you don't overload the robos? If the search space is too large, could you use A* with heuristics (such as "how close product is to desired location for us" or "how close parts have come to a formed product for us")? Could you use D* for workplaces that have lots of other actors, for collision avoidance?"

[quote= [2] https://poe.com/chat/1yt9udz8p7mqvc9uwpl]Yes, BFS/DFS/IDDFS, A*, and D* are all valid algorithms that can be utilized in the context of robotic motion planning and control. These algorithms can help generate code for controlling robots such as Fanuc to perform tasks and achieve desired outcomes. I apologize for the confusion in my previous responses.

To clarify, here's how these algorithms can be applied:

BFS/DFS/IDDFS: These algorithms can be used to explore the graph of possible servo motions, where the nodes represent servo positions and the edges represent servo motions or transitions. By defining a fitness function that represents the desired outcome, such as reaching a specific position or forming whole products, you can guide the search process towards finding a sequence of servo motions that maximizes the fitness function. Additionally, collision checking can be incorporated by restricting node edges that would cause collisions. To handle quantization or discrete servo positions, you can represent the search space as a discrete grid of positions. A*: A* is an informed search algorithm that can be used to find an optimal path in a graph by considering both the cost of reaching a node and an estimate of the remaining cost to the goal. In the context of robotic motion planning, A-star can be applied by defining appropriate heuristics that estimate the distance or effort required to achieve the desired outcome. For example, you can use heuristics such as the Euclidean distance between the current position and the goal position or the progress made towards forming a product. A* can help efficiently explore the search space and find an optimal or near-optimal solution. D*: D* is a planning algorithm that can be used for dynamic replanning and collision avoidance in environments with changing conditions or obstacles. It maintains a map of the environment and updates the plan based on new information. In workplaces with multiple actors or changing obstacles, D-star can be used to dynamically adjust the robot's path to avoid collisions and achieve the desired outcome.

In summary, these algorithms can be valuable tools for generating code and controlling robots to perform tasks.[/quote] SwuduSusuwu ( talk) 12:48, 19 January 2024 (UTC) reply