Path planning and image processing are critical signal processing tasks for robots, autonomous vehicles, animated characters, etc. The ultimate goal of the path planning problem being addressed in this dissertation is how to use a reconfigurable Analog Very Large Scale Integration (AVLSI) circuit to plan a path for a Micro Aerial Vehicle (MAV) (or similar power constrained ground or sea robot) through an environment in an effort to conserve its limited battery resources. Path planning can be summarized with the following three tasks given that states, actions, an initial state, and a goal state are provided. The robot should: 1) Find a sequence of actions that take the robot from its Initial state to its Goal state. 2) Find actions that take the robot from any state to the Goal state, and 3) Decide the best action for the robot to take now in order to improve its odds of reaching the Goal. Image processing techniques can be used to visually track an object. Segmenting the object from the background is one subtask in this problem. Digital image processing can be very computationally expensive in terms of memory and data manipulation. Path planning and image processing computations are typically executed on digital microprocessors. This dissertation explores an evolution of analog signal processing using Field Programmable Analog Arrays (FPAAs); it describes techniques for mapping different solutions onto the hardware, and it describes the benefits and limitations. The motivation is lower power, more capable solutions that also provide better algorithm performance metrics such as time and space complexity. This may be a significant advantage for MAVs, ocean gliders or other robot applications where the power budget for on-board signal processing is limited.