Investigators: PI: Andrew Browning (AeroVironment), Co-PI: Sean Humbert (CU), Holger Krapp (Imperial College), Geof Barrows (Centeye)
The best currently available artificial intelligence is highly dependent upon large volumes of data, and significant computational power, severely limiting applications for low-SWAP on-the-edge applications. Moreover, performance in the real-world rarely lives up to expectation, which is often acceptable for consumer applications but not for most industrial or defense related applications. In contrast, biological systems can learn to recognize behaviorally relevant objects from a single trial, and are capable of robust operation in highly complex, dynamic, and unknown environments. The simplest intelligent biological systems are insects, which can perform goal-directed activities, including goal recognition, goal prioritization, 3D environment understanding, and navigation with as few as 1000 neurons. We postulate that the key to their success with such limited computation is highly integrated sensing, computation, and actuation.
- Escobar-Alvarez HD, Ohradzansky M, Keshavan J, Ranganathan BN and Humbert JS, “Bio-Inspired Approaches for Autonomous Small Object Perception and Avoidance,” IEEE Transactions on Robotics, Vol. 35, No. 5, 2019. DOI:10.1109/TRO.2019.2922472.