The best-known manufacturer of autonomous vacuums declared bankruptcy this week, and no one should be surprised. The home-vacuum robot began, like most things, with war. In August 1990, the same month and year Saddam Hussein invaded Kuwait, three MIT roboticists incorporated the company that would eventually become iRobot, the maker of the Roomba. In its first decade, iRobot began to assemble a small-droid A-team for the theater of combat. The Ariel defused mines; the PackBot handled bomb disposal.

Nobody is immune to knock-offs, bad decision making and tariffs. The company which popularized robot vacuum cleaners around the world has filed for Chapter 11 bankruptcy . The company plans to sell its assets to its primary supplier, China's Picea Robotics, in the hope of maintaining its business. Everyone's got a strident opinion as to why iRobot fell from grace. The rugged individualists blame limp regulators on both sides of the pond (and their hatred for big tech) for blocking Amazon's attempted purchase in 2023.

The giant, remote-controlled vehicle -- somewhere between a tractor trailer, a tank and a Zamboni in appearance -- slowly rolled across the dry, brittle grass growing between the tangle of freeways making up the 101 and 23 interchange in Thousand Oaks. And as it rolled over the land, that fire incinerated any brush it encountered, leaving only a thin smoke cloud billowing from the top of the machine, some flashes of orange and red from behind its metal skirt and, in its wake, a desolate, smoldering black line. BurnBot isn't the fastest way to rid a landscape of dangerously flammable vegetation (it tops out at around 0.5 mph) but it can do something that traditional vegetation management techniques cannot: with almost surgical precision, it can kill the flammable brush sitting within feet of homes and highways on even the hottest and driest days and with virtually no safety risks or disruptions to daily life. On a recent summer afternoon, as wildland firefighters maneuvered the machine and mopped up the charred earth on a stretch of highway about 30 miles west of Los Angeles on the 101, a who's who of SoCal's wildfire leadership looked on -- from the California Department of Forestry and Fire Protection, local fire departments, Caltrans, the U.S. forest and park services, Southern California Edison and state Legislature. The sweet smoky smell of wildland fire permeated the hot midday air.

According to Alg. 2, in each exploration, at least one leaf node will be expanded. Thus, we have the conclusion that AdaOPS is guaranteed to terminate. First, we will demonstrate that the value of any belief can be formulated as an integral. This lemma is a concentration inequality of self-normalized importance sampling estimator. The ESS threshold µ for adaptive resampling is set to .

The company behind Roomba robovacs told investors earlier this week that revenue was substantially down and it's struggling to pay its debts. Amazon was briefly tapped to acquire the robot company iRobot, but the threat of a European Commission investigation led to the retailer terminating the deal -- apparently happy enough to pay off the 94 million termination fee. That, however, isn't enough to tackle the 200 million loan iRobot took out to survive long enough for Amazon to come to the rescue. It's extra rough when the company announced, just the week before, a bunch of new models, including a new Roomba that can compact debris and dust, so it only needs to be emptied every few weeks. At the same time, rival robot vacuum cleaners are getting more versatile, more complicated and more intriguing.

The Roomba 205 DustCompactor Combo Robot is being advertised as "the industry's first onboard mechanical debris-compacting system." In other words, it squeezes dust and debris together like, well, a garbage compactor. This allows users to go eight weeks without having to empty the vacuum. It also eliminates the need for a dedicated debris bin. Otherwise, the 205 is a full-featured hybrid vacuum/mop.

Devindra: We are here what is this, the beginning of night one of CES officially? Devindra: guess we have already suffered through basically day minus one. Devindra: One thing I want our listeners to understand is that we have already seen a lot of things we kind of know where the CES is headed. And, I think this is a cursed show Cherlynn. How do you feel about that? Yeah, I think I mean, Devindra, I'll let you speak to your situation, but we've had team members who have fallen deathly ill. We have also, like, people who have completely had to miss their flights, international flights. It's been quite Engadget team, but we have a really, really good team of people. Everyone's got great attitudes and, like, our spirits are high. You want to just get the stuff going.

With the advancement of deep learning methods it is imperative that autonomous systems will increasingly become intelligent with the inclusion of advanced machine learning algorithms to execute a variety of autonomous operations. One such task involves the design and evaluation for a subsystem of the perception system for object detection and tracking. The challenge in the creation of software to solve the task is in discovering the need for a dataset, annotation of the dataset, selection of features, integration and refinement of existing algorithms, while evaluating performance metrics through training and testing. This research effort focuses on the development of a machine learning pipeline emphasizing the inclusion of assurance methods with increasing automation. In the process, a new dataset was created by collecting videos of moving object such as Roomba vacuum cleaner, emulating search and rescue (SAR) for indoor environment. Individual frames were extracted from the videos and labeled using a combination of manual and automated techniques. This annotated dataset was refined for accuracy by initially training it on YOLOv4. After the refinement of the dataset it was trained on a second YOLOv4 and a Mask R-CNN model, which is deployed on a Parrot Mambo drone to perform real-time object detection and tracking. Experimental results demonstrate the effectiveness of the models in accurately detecting and tracking the Roomba across multiple trials, achieving an average loss of 0.1942 and 96% accuracy.

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