substantially greater functionality can be created. In the last ten years alone, significant developments include the Velodyne 2.5D scanning LIDAR system, flash LIDARs, and automotive radars and LIDARs. This increase in breadth and quality, and decrease in cost have enabled the recent burst in unmanned system activity.I see this trend not only continuing, but accelerating. While Moore’s law is not producing material advances in computing power, sensors that produce data in more computer ready formats such as 2.5D rangers reduces the need for server farm to process data. Furthermore, advances in algorithms, GMAT-Verbalincluding stochastic and Bayesian frameworks, have finally given robotic devices a robust toolset to incorporate multiple conflicting noisy measurements. It truly is an exciting time to be developing new unmanned systems!
One of the major considerations in any field robotics development is determining how the system will perceive its surroundings. You can choose to use a reliable, but high cost standard such as one or more scanning LIDARs, a newer automotive RADAR or sonar sensor, or even camera based vision systems. The choice you make ends up determining how successful the project will be both in its development cost, unit cost, and development time. Unmanned driverless cars have been demonstrated many times even more than 20 years ago, including CMU’s NavLab. However, these systems were in general far from commercialization, because the sensors available were very brittle with respect to real world scenarios.As the cost and capability of the 1Y0-A17 sensor ecosystem has improved, autonomous systems of
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