91桃色

The compound eye of a wasp gave Prof. Burschka an idea. By moving its body back and forth, the insect detects which objects are nearby and which ones are further away. In this way it builds a mental map as it flies.

Airspace and road traffic: 60 measurements a second for greater safety

A similar principle is applied in a solution with which Prof. Burschka, the Co-Head of the Perception Group at the Munich Institute of Robotics and Machine Intelligence (MIRMI) of TUM, tries to identify impending collisions of drones or cars with other objects. His computer system checks the image points from a camera 60 times per second and determines the 鈥渃ollision conditions鈥�. 鈥淲e track up to a million pixels of an image in real time,鈥� says Burschka. To compute this 鈥渙ptical flow鈥�, he does not need a supercomputer. Instead, he works with a 鈥渕ere鈥� high-performance graphic processor that handles the images, another process to calculate the collision paths, and a camera. The researcher explains: 鈥淲e look at the detectable characteristics in the image and watch how they move across it.鈥�

Two-dimensional images as a foundation: similar to the constant bearing approach at sea

To calculate the immediate danger of a collision, the TUM professor only needs two-dimensional images from a perspective like the one used by the wasp to fix individual points and perceive changes in them 鈥� or like a sailor applying the constant bearing method. With that method, a ship is determined to be on a collision course if the absolute bearing between approaching ships shows little or no change as the distance decreases. 鈥淭he best way of detecting a potential collision is to keep an eye on which surrounding objects are not moving,鈥� says Burschka. The TUM scientist calculates where and in which direction objects fly past the camera, i.e. 鈥減enetrate the observational plane鈥�. In conventional applications, autonomous driving experts, for example, use several cameras to compute distances to nearby objects using vectors. 鈥淲hen the objects are far away from the camera, the 3D process is no longer reliable,鈥� explains Burschka. Changes in positions of points from one image to the next can no longer be seen.

Paradigm shift: time to interaction replaces metric state analysis

With the new method, rapidly approaching objects still remote from the observer are recognized as more dangerous than others that are closer but moving in the same direction. 鈥淚nstead of moving objects being prioritized by their motion alone, this is done on the basis of dynamic collision conditions,鈥� says Prof. Burschka. All 鈥渇eatures鈥� in the image are now under observation and the potentially dangerous ones can be flagged accordingly. 鈥淲e measure the 鈥榯ime to interaction鈥� 鈥� in other words, the time that will elapse before a collision occurs,鈥� he explains. The new method will enable scientists to analyze the paths of moving objects with just one camera, which will also be in motion. 鈥淯nlike metric reconstruction, this approach is much cheaper and more robust,鈥� Prof. Burschka believes. The 鈥榯ime to interaction鈥� approach would thus represent a paradigm shift in research. The professor plans to use his invention in drones, networked vehicles and service robotics.