Scientists from China's Zhejiang University have unveiled a drone swarm capable of navigating through a dense bamboo forest without human guidance. The group of 10 palm-sized drones communicate with one another to stay in formation, sharing data collected by on-board depth-sensing cameras to map their surroundings. This method means that if the path in front of one drone is blocked, it can use information collected by its neighbors to plot a new route. The researchers note that this technique can also be used by the swarm to track a human walking through the same environment. If one drone loses sight of the target, others are able to pick up the trail.

Engineers at EPFL have developed a predictive control model that allows swarms of drones to fly in cluttered environments quickly and safely. It works by enabling individual drones to predict their own behaviour and that of their neighbours in the swarm. There is strength in numbers. By flying in a swarm, they can cover larger areas and collect a wider range of data, since each drone can be equipped with different sensors. One reason why drone swarms haven't been used more widely is the risk of gridlock within the swarm.

The mathematical engineer and robotics PhD student from the Swiss Federal Institute of Technology Lausanne, or EPFL, had already built a computer model to simulate the trajectories of five autonomous quadcopters flying through a dense forest without hitting anything. But an errant copter wouldn't survive a tête-à-tête with a physical tree. So Soria built a fake forest the size of a bedroom. Motion-capture cameras lined a rail hanging above the space to track the movement of the quadcopters. And for "trees," Soria settled on a grid of eight green collapsible kids' play tunnels from Ikea, made of a soft fabric.

There is strength in numbers. By flying in a swarm, they can cover larger areas and collect a wider range of data, since each drone can be equipped with different sensors. Preventing drones from bumping into each other One reason why drone swarms haven't been used more widely is the risk of gridlock within the swarm. Studies on the collective movement of animals show that each agent tends to coordinate its movements with the others, adjusting its trajectory so as to keep a safe inter-agent distance or to travel in alignment, for example. "In a drone swarm, when one drone changes its trajectory to avoid an obstacle, its neighbors automatically synchronize their movements accordingly," says Dario Floreano, a professor at EPFL's School of Engineering and head of the Laboratory of Intelligent Systems (LIS).

Shared mobility-on-demand services are expanding rapidly in cities around the world. As a prominent example, app-based ridesourcing is becoming an integral part of many urban transportation ecosystems. Despite the centrality, limited public availability of detailed temporal and spatial data on ridesourcing trips has limited research on how new services interact with traditional mobility options and how they impact travel in cities. Improving data-sharing agreements are opening unprecedented opportunities for research in this area. This study examines emerging patterns of mobility using recently released City of Chicago public ridesourcing data. The detailed spatio-temporal ridesourcing data are matched with weather, transit, and taxi data to gain a deeper understanding of ridesourcings role in Chicagos mobility system. The goal is to investigate the systematic variations in patronage of ride-hailing. K-prototypes is utilized to detect user segments owing to its ability to accept mixed variable data types. An extension of the K-means algorithm, its output is a classification of the data into several clusters called prototypes. Six ridesourcing prototypes are identified and discussed based on significant differences in relation to adverse weather conditions, competition with alternative modes, location and timing of use, and tendency for ridesplitting. The paper discusses implications of the identified clusters related to affordability, equity and competition with transit.