The broader impact/commercial potential of this I-Corps project is the development of reliable autonomous vehicle systems that may overcome existing limitations by utilizing sensor measurements to adapt its path while the vehicle is in motion. As RADAR, LiDAR, and SONAR sensor can be used on the onboard robotic platforms, markets may utilize autonomous aerial, underwater, and ground vehicles. One application is the development of a more reliable and safe autonomous driving system. Another example is an autonomous aerial/underwater vehicle that may assist in gathering quality data of wild animal habitats and populations while minimizing the damage of natural environment. Defining the requirements of the existing autonomous systems will help establish a general standard for a reliable design for autonomous robotic systems.
This I-Corps project is based on the development of a sensor path planning algorithm that may adapt its direction based on sensor measurements while the vehicle is in motion. Information theory and probabilistic sensor measurement models are used in the algorithm to decide the vehicle’s path such that the most informative measurements can be obtained. The algorithm is capable of considering the confidence level based on object identification and the vehicle can automatically re-plan its path based on the information gathered in real-time. This technology is based on research on effective and efficient methods to utilize and process the sensor measurements towards path planning. Technical results demonstrate that the proposed algorithm can compute the minimum-time path while achieving a satisfactory confidence level from obtained sensor measurements. The application of the path planning algorithm will give a higher level of autonomy, resulting in a more reliable integrated system.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
