1. Block Diagram of System The robot should be a Powered Brain with Sensors and Motors that interacts with its Environment, fleshed out in more detail. Designing a Small-Scale Autonomous Path-Finding Robot Blake Hayman Dr. Takis Zourntos, Nebu Mathai Texas A&M University, Electrical & Computer Engineering NSF/DOD REU Research Methods Before researching anything, you first look at your project’s predecessors. We examined several robot projects that were meant to mimic life, and we looked at as many as could be found. Many older projects were not well- documented, but much work was generated in this field in the late ‘90s. Source Sampling: Gallagher, John C. “The Once and Future Analog Alternative: Evolvable Hardware and Analog Computation.” Proceedings of 2003 NASA/DOD Conference on Evolvable Hardware. IEEE 2003. Holland, Owen. “The First Biologically Inspired Robots.” Robotics, vol. 21, pg. 351 – 363. Kumagai, Jean. “Halfway to Mars,” IEEE Spectrum. March 2006. Walter, W. Grey – “A Machine That Learns.” Scientific American, vol. 185, is. 5, pg. 60-63. August 1951. Walter, W. Grey – “An Imitation of Life.” Scientific American, vol. 182 pg. 42-45. 1950. Once an analysis of previous attempts is complete, we decide ways to build upon them and introduce new ideas. With the new ideas, we research methods of implementation and begin searching out pieces for the robot’s physical design. Abstract & Purpose Our eventual purpose with this project is to create a fully analog small-scale autonomous robot that can home in on a specific target autonomously. However, this path-finding robot is still in the prototyping and algorithm testing phase. We are thus a bit more lenient on using digital parts, and the design is not expected to be perfect. However, even with some digital components, our robot’s design should be very similar to the expected final product. Design The sensor system contains two microphones and an ultrasonic rangefinder that receive data from the environment. The microphones’ analog signals must go through additional conditioning (an amplifier and a filter each) before they can be sent to the RIO FPGA (brain). The RIO first converts the sensor system’s analog signals to digital for processing; then, it performs calculations based on the signal data to create data useful for the control algorithm. The dynamic systems-based algorithm will control the actuation and behavior of the robot and send output to the motor control functions based on its specified behaviors. The motor control functions then send desired velocities in the form of analog voltages to the motors, which actuate based on this signal. This actual velocity is read by shaft encoders, which send digital signals back to the motor control functions for correction processing. The drive shafts interact with gear systems, the chassis, and wheels before outputting a change to the environment (actuation). Results & Conclusions A 17-page PDF document that summarizes our work over the summer, our recommendations as to design and part choice, and justification of our choices. Nine weeks is generally not enough time to finish a paradigm-shifting project like ours. Research on creating an autonomous analog path-finding robot will continue into the Fall with new undergrad 405 and 485 students, who will implement and improve on our prototype design. Prototype Schematic Algorithms & Subsystem Control Acknowledgements Dr. Takis Zourntos Anthony Pham Nebu Mathai Marcus Dunn Emily Weisbrook