1. The vacuum robots generally use 2D LiDARs. Using just LiDAR information it is extremely difficult to classify what is an obstacle and what is not. For example, a curtain can be blocking an entrance area and hence need not be treated as an obstacle. In cases like this the robot uses its bumper sensors to hit this objects to see if they can be moved into. This gives a perception that the robots aren't trying to avoid obstacles but rather they are trying to clean the maximum area possible. A lot of the new age robots now have feature to disable this behavior. In some cases these robots literally may not see above a certain height due the LiDAR being planar e.g. cords above a certain height. This also leads into entanglement. To prevent this a lot of expensive vacuums generally above $500, have started using cameras which can classify objects and decide to hit it or avoid it. They have also included features wherein the robot detects entanglement with a cord or a napkin on the floor and asks the user to select to avoid that area/object in the future. But often backlighting from windows, shadows, glossy floors, dark rugs, and mixed lighting temperatures, etc can throw of the algorithms. To tackle these challenges you also now see small LED flash lights have been included with the robots to tackle low lighting 2. It is difficult to answer this without knowing the make and model of the robot. But generally pet modes need aggressive pathing, stronger suction, higher brush speeds and extra passes on the covered portion. Hence the system may dial back on the frequency of certain perception tasks to save battery. Also to pull more hair the robot may want to push closer to objects where hairs generally accumulate. The algorithm may also need higher confidence before avoiding something in pet mode. It could also happen that strong suction and higher brush speeds may lead to vibrations or robot pitch changes where imagery is unstable and blurry. 3. Reliable low-cost 3D perception sensors would be a big boost to the industry. Furthermore, household objects are long-tail. Hence, improving ML models that handle new toys or cords or odd lighting without constant tuning will help a lot. 4. Use no-go zones where the robot constantly gets entangled. Look for suggestions from the robot's app for no-go zone suggestions or requests to mark an object obstacle or not. Clean the hardware regularly and be realistic about the expectations.
Tech & Innovation Expert, Media Personality, Author & Keynote Speaker at Ariel Coro
Answered 4 months ago
I've spent over a decade explaining tech for millions of Spanish-speaking viewers on Despierta America and testing hundreds of gadgets at CES. Robot vacuums hit my radar early, and I've watched this exact trade-off frustrate consumers for years. The core challenge is processing power and real-time decision-making. When a robot vacuum cranks up suction to maximum (pet mode), it's dedicating more computing resources to motor control and less to vision processing. It's like your phone getting hot when gaming--something has to give. The sensors need milliseconds to identify obstacles, but at higher speeds with stronger suction, that split-second delay means the vacuum is already committed to its path. Add in the chaos of lamp cords that look different in every lighting condition or pet toys that come in thousands of shapes, and even the best computer vision struggles. From what I've seen at CES covering farming robots that use similar tech, the breakthrough will likely come from edge computing--putting more powerful AI chips directly in the devices rather than relying on cloud processing. Blue River Technology's weed-spraying robots can identify plants at speed because they process everything locally. Robot vacuums need that same capability, but it adds $200+ to manufacturing costs that most consumers won't pay. My practical advice? Run your robot vacuum when you're home for the first few times and map out its trouble spots. Remove obvious obstacles before each run--yes, it defeats the "set and forget" promise, but five minutes of prep saves you from finding your vacuum tangled in charging cables. And honestly, if you have pets, accept that you'll need to choose: either get the deep clean and baby-proof your floors, or let it run in gentle mode and supplement with manual vacuuming weekly.
I've launched dozens of tech products including advanced robotics like the Robosen Transformers line, so I've seen this challenge from the product development side. The fundamental issue is processing power allocation--these devices have limited computational resources, and manufacturers have to choose where to invest those cycles. When we worked on the Robosen Elite Optimus Prime and Buzz Lightyear robots, we faced similar trade-offs between performance modes and precision control. The pet mode example you mentioned makes perfect sense: higher suction power requires more aggressive brush roll speeds and increased motor output, which creates more vibration and reduces the precision of sensor readings. The robot essentially becomes "louder" to its own sensors when operating at max power, making fine object detection harder. The real breakthrough won't be a single technology--it's integration architecture. Right now, most robot vacuums run vision and LIDAR systems somewhat independently, then try to reconcile conflicting data. What's needed is a unified processing approach where all sensors feed into a single real-time decision engine, similar to how autonomous vehicles process data. This requires significantly more powerful onboard processors, which directly impacts cost and battery life. For consumers, the simple answer is lighting--most vision-based systems struggle in low light, which is why testing in well-lit rooms shows dramatically better obstacle avoidance. Also, pick up obviously problematic items before running the vacuum. Expecting a $500 robot to steer around charging cables is like expecting budget autonomous driving--the technology exists but not at that price point yet.
As the founder of WhatAreTheBest.com, I have extensively analyzed robot vacuums and their performance. Robot vacuums face difficulties because their ability to clean and avoid obstacles depends on shared limited resources, including sensors, computing power, operational duration, and available space. The system uses its powerful suction force along with its forceful brush operation to lift cords, toys, and pet waste before the sensors can accurately identify these items, particularly when dealing with objects that have low contrast or flexible materials. The Pet or deep-clean modes operate by lowering the chassis while they slow down avoidance checks and make the firmware prioritize floor contact to enhance cleaning performance, but they decrease both reaction speed and sensor exposure. The actual solution requires improved sensor data integration with accelerated artificial intelligence processing inside the device rather than increased vacuum power. The public can assist by clearing floor areas before cleaning, enhancing home illumination, implementing virtual boundaries for restricted areas, and performing thorough cleaning cycles only when necessary. Albert Richer, Founder WhatAreTheBest.com
I've worked with AI vision models, and here's what I've learned about robot vacuums. They get smarter about spotting socks versus dirt when trained on real-world data. But there's a catch - when you crank up the suction power, the robot has less processing power for its camera vision. That's why pet mode struggles sometimes. My tip? Clear out loose stuff before starting and keep that software updated for better obstacle spotting.
When people ask why robot vacuums struggle to both clean powerfully and avoid everyday obstacles, it comes down to tradeoffs I've seen repeatedly while testing automation and search-driven consumer tech over the years. Small, unpredictable objects like cords, pet toys, and pet waste don't behave like walls or furniture—they shift, tangle, and compress, which confuses sensors that are optimized for rigid shapes. Stronger suction and aggressive brush rolls also pull debris inward faster, leaving less reaction time for cameras or LIDAR to decide whether something is dirt or a hazard. I've seen robots that vacuum exceptionally well turn a phone charger into a winch cable in seconds, simply because the cleaning system overpowered the avoidance logic. That same tradeoff explains why a "pet mode" that boosts hair pickup can reduce obstacle avoidance from an engineering perspective. When suction, brush speed, and downward pressure increase, the system prioritizes cleaning performance over caution, and software thresholds for stopping or rerouting get relaxed to prevent constant interruptions. As for whether a breakthrough is needed, the biggest gap isn't raw sensors—it's real-time decision-making that combines vision, depth, and tactile feedback fast enough to react before contact. Until that improves, the most effective thing consumers can do is prep the environment: pick up cords, block high-risk areas, run robots on a schedule when floors are clearer, and use room-by-room mapping instead of whole-house runs. In practice, a few minutes of setup often makes a mid-range robot perform better than a premium one left to navigate a cluttered floor blindly.