Abstract-- Effective collective decision-making in swarm robotics often requires balancing exploration, communication and individual uncertainty estimation, especially in hazardous environments where direct measurements are limited or costly. We propose a decentralized Bayesian framework that enables a swarm of simple robots to identify the safer of two areas, each characterized by an unknown rate of hazardous events governed by a Poisson process. Robots employ a conjugate prior to gradually predict the times between events and derive confidence estimates to adapt their behavior . Our simulation results show that the robot swarm consistently chooses the correct area while reducing exposure to hazardous events by being sample-efficient. Compared to baseline heuristics, our proposed approach shows better performance in terms of safety and speed of convergence. The proposed scenario has potential to extend the current set of benchmarks in collective decision-making and our method has applications in adaptive risk-aware sampling and exploration in hazardous, dynamic environments. Collective decision-making under uncertainty is a fundamental challenge in multi-robot systems, including domains such as collective perception, environment classification, and spatial consensus [1]-[4]. Decentralized systems (e.g., robot swarms) operate under strict limitations on sensing, communication, and memory. Instead of sharing/storing complete observation histories, robots must maintain compact model representations of their knowledge. It is crucial to develop efficient strategies for collective decision-making, especially when observations are sparse, noisy [5], and gathered from stochastic processes [6]. This is typically characterized as a best-of-n problem [3], [7].

Many drivers are highly curious today about the Autonomous Vehicles (AV) dream. Will this dream come true, and when? One of the core technology that needs to be implemented in AV is the inertial navigation system (INS). These systems integrate many sensors together in what we called "sensor fusion" schemes. These sensors include LiDAR, cameras, GPS receivers, Radars, accelerometers, gyroscopes, and many more. The general sensor fusion scheme integrates all the sensors together using a very common algorithm, named the "Kalman Filter", to fuse all sensors optimally (in a mean-squared-error sense).

A robot avatar that mimics the motions of a human controller could take the place of workers in several dangerous jobs, such as tree trimming and construction, by the end of 2022. The challenge: If a tree branch gets too close to a power line, it can cause electrical outages or, even worse, dangerous fires (as Californians know all too well). To avoid this, utility companies have to regularly trim trees near their lines. But it's dangerous work, as workers are dozens of feet above the ground, using sharp power tools to trim trees while power lives are still active -- this puts them at risk of falls, cuts, and electrocution, all at once. By some estimates, tree trimming is one of the most dangerous jobs in the country.

Limited access highways have been accepted as a mechanism to optimize transport for many years. According to the Transportation Research Board (TRB), limited access highways offer greater capacity; improved safety; reduced fuel consumption; less pollution; more positive impacts on motorists; and more positive impacts on neighborhoods. From the point-of-view of autonomy, limited access highways offer use-models with a significant reduction in complexity. Not surprisingly, many of the initial applications for autonomy have focused on highways. In passenger cars, the focus has been on functionality such as advanced cruise control with underlying capabilities such as lane-keeping and of course collision avoidance.

More children are being vaccinated around the world today than ever before, and the prevalence of many vaccine-preventable diseases has dropped over the last decade. Despite these encouraging signs, however, the availability of essential vaccines has stagnated globally in recent years, according the World Health Organization. One problem, particularly in low-resource settings, is the difficulty of predicting how many children will show up for vaccinations at each health clinic. This leads to vaccine shortages, leaving children without critical immunizations, or to surpluses that can't be used. The startup macro-eyes is seeking to solve that problem with a vaccine forecasting tool that leverages a unique combination of real-time data sources, including new insights from front-line health workers.

The XPrize Foundation has named 21 semifinalists for its $7 million Shell Ocean Discovery XPrize contest to advance technologies for deep water exploration, including San Diego's Orca Robotics. The semifinalists were announced at the Catch the Next Wave conference in San Diego on Thursday, with teams from 13 countries making the cut. Several teams affiliated with U.S. universities are among the semifinalists, including Duke, Rutgers, Texas A&M, Lehigh University, Virginia Tech and Old Dominion. "These semifinalist teams are on the cutting-edge, pushing the boundaries in developing deep-sea underwater technologies that will work in the lightless, cold depths to fully map one of our world's final frontiers like never before," said Jyotika Virmani, who is overseeing the contest as head of XPrize's Energy and Environment Group. Teams have tapped technologies ranging from underwater gliders to robotic swarms and autonomous vehicles.