Robotics: War for Precision or Safety

 Sparrow, Robin, and Cardinal are robotic future working in an Amazon warehouse, sparrow is a long robotic arm that can pick up the items before they are packed in boxes. Robin starts from there to sort the items according to their mailing destination and Cardinal could put them in a waiting cart before they get loaded on the truck. According to officials, Sparrow can sort out 65% of inventory products which will be going to increase near future.

This robotics revolution will be going to be massive in the warehousing and E-commerce industry. Amazon has constantly said that robots will eliminate the rework caused by the human workforce nearly to zero where other workers can do more engaging and competing tasks.

For decades mechatronic industries are training robots to act like a human and perform duties with more compelling tasks like human brains, respond like a human brain but thanks to today’s artificial intelligence software and programming algorithms which have now made it possible for engineers to program the robot more like multitasking performing machines with minimum errors.

Technology and computer scientist are terming robots reliable enough to perform human actions, and duties and ready for deployment. Experts at Illinois university say they are now competent enough by adding cameras for fast photo mapping by synchronizing with artificial intelligence algorithms. 

But besides this advancement concerns deep by the top economic analysts that this war for precision and efficiency will go hard on the human workforce, it pushes them to feel more competitive for hitting average targets, competing with robots which can be catastrophic for safety as more robots could result in higher incident rates, work injuries, and tough human surveillance. The interesting fact is that jobless and fewer human requirements were deep concerns before but now even more robotic reliance is based on more workforce requirements and jobs after the pandemic shift.

Elon Musk has continuously said that he would automate Tesla’s manufacturing completely, BMW, Toyota, and other automobile giants are already working with 70 % robotic machines designed to perform single and multitasking assembling duties. FedEx has invested $200 to do sorting and picking, the race between the packaging and mailing industry has led them to invest heavily and rely on robot features for error reduced to less than 1 percent, the race is for not only precision and time but revenues also, all the picking and sorting models have made companies financial numbers skyrocketing. 

Companies dealing in the robotics business have irked their sales estimation from past years $200 to $400 million this year and will be spotting on above $700 next coming year. From Microsoft Kinect motion sensing games to single-performing tasks, today’s 3d technology, and AI programming which are modeled on the human brain and binary numbers algorithms, make robots more compatible for scanning images, abrupt decision-making tasks, surveillance data management making the process more reference-based and replace by the human workforce.

But more respectively, in the future, this race will be more based on the safety and scrutiny of robots mainly in the process, warehouse, and mailing industry because the more complex the process to be artificially intelligent, the more safe closures and resets are required. Adding robots to process can make injuries more fatal than it seems on a theoretical basis because one step further than picking and sorting, robots could be designed to analyze the load, and type of products to be categorized which makes them more powerful based intelligent enough for abrupt decisions. 

Programming the AI algorithm with safety will surely be a big challenge for robotic scientists and engineers as the biggest task is to ensure the AI system with human values to test reliability and identify health hazards in binary programs. Implementation traceability will be the core issue that can be measured on a safety scale before introducing program bias into decision-making to prevent it from violating of personal privacy. Do’s and Don’t’s are also to be identified according to some uniform safety policy categorized by process and target industry robot designed for.

AI algorithm for self-learning could be a game changer as humans repairs themselves by self-instruction from past mistakes. Developing logical controllers for self-learning or supercomputer for self-examined safety policies makes our goals and machines identical, and also can produce traceability of legal responsibilities. This can also prevent robots from being manipulated for social manipulation and autonomous weapons identification.

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