This work introduces a solution to enhance human-robot interaction over limited wireless connectivity. The goal is toenable remote control of a robot through a virtual reality (VR)interface, ensuring a smooth transition to autonomous mode in the event of connectivity loss. The VR interface provides accessto a dynamic 3D virtual map that undergoes continuous updatesusing real-time sensor data collected and transmitted by therobot. Furthermore, the robot monitors wireless connectivity and automatically switches to a autonomous mode in scenarios with limited connectivity. By integrating four key functionalities: real-time mapping, remote control through glasses VR, continuous monitoring of wireless connectivity, and autonomous navigation during limited connectivity, we achieve seamless end-to-end operation.

Reliable localization is an essential capability for marine robots navigating in GPS-denied environments. SLAM, commonly used to mitigate dead reckoning errors, still fails in feature-sparse environments or with limited-range sensors. Pose estimation can be improved by incorporating the uncertainty prediction of future poses into the planning process and choosing actions that reduce uncertainty. However, performing belief propagation is computationally costly, especially when operating in large-scale environments. This work proposes a computationally efficient planning under uncertainty frame-work suitable for large-scale, feature-sparse environments. Our strategy leverages SLAM graph and occupancy map data obtained from a prior exploration phase to create a virtual map, describing the uncertainty of each map cell using a multivariate Gaussian. The virtual map is then used as a cost map in the planning phase, and performing belief propagation at each step is avoided. A receding horizon planning strategy is implemented, managing a goal-reaching and uncertainty-reduction tradeoff. Simulation experiments in a realistic underwater environment validate this approach. Experimental comparisons against a full belief propagation approach and a standard shortest-distance approach are conducted.

We propose an autonomous exploration algorithm designed for decentralized multi-robot teams, which takes into account map and localization uncertainties of range-sensing mobile robots. Virtual landmarks are used to quantify the combined impact of process noise and sensor noise on map uncertainty. Additionally, we employ an iterative expectation-maximization inspired algorithm to assess the potential outcomes of both a local robot's and its neighbors' next-step actions. To evaluate the effectiveness of our framework, we conduct a comparative analysis with state-of-the-art algorithms. The results of our experiments show the proposed algorithm's capacity to strike a balance between curbing map uncertainty and achieving efficient task allocation among robots.

Pilots can actually see through the body of the aircraft as they fly. International technology company Elbit Systems this week unveiled a helicopter vision suite for military helicopters that uses a combination of augmented reality (AR), machine learning, and artificial intelligence to provide pilots better visibility while in degraded visibility conditions. The system is powerful, according to the company, that pilots can actually see through the aircraft as they fly. The platform is built around three primary components: an AR head-mounted display (HMD), an AI-powered mission computer, and an array of sensor systems, including the Xplore radar and BrightNite multispectral payload, which includes day and Infra-Red cameras for thermal vision. These sensors are mounted to the nose of the plane and are used to generate a virtual map of the local terrain, including any obstacles such as powerlines or antennas.

If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA TODAY's newsroom and any business incentives. One of the worst parts of pet ownership is keeping up with the sheer amount of fur your dogs or cats shed on a daily basis. If you agree, maybe it's time to get a robot vacuum cleaner designed to keep up with your pet's constant shedding. These automated cleaners can be set to run on a schedule, so the only thing you have to do is occasionally empty its dust bin.

If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA TODAY's newsroom and any business incentives. Whether you just like the idea of letting a robot handle cleaning up your floors or you just don't like to vacuum, a robot vacuum cleaner can be a real help. But with so many companies making robot vacuums, how do you know if any of them are actually worth the money? Luckily, we've done the hard work for you. We have a specially built obstacle course in our labs that tests how well robot vacuums pick up dirt, navigate around ytour furniture, and deal with floor types from hardwood floors to low- and high-pile carpets.

Costly car repairs caused by dangerous potholes could soon become a thing of the past. Ford has announced that it is working on a virtual pothole map that could be released later this year. The map would show drivers in real-time where potholes are, how bad they are, and suggest alternative routes. Engineers are researching the use of sensors, cameras and embedded Wi-fi systems at the Ford Research and Innovation Centre, in Aachen, Germany. They said these technologies could gather detailed information on the potholes and beam it to a virtual cloud – where it can be made available to other drivers – in real time.