Extended State Machines for Robust Robot Performance in Complex Tasks

Abstract

Most field robots today work under partial or complete guidance of an operator. The operator monitors, or at times augments, the control inputs of the robot to achieve better results or desired behavior. Robots that are operated remotely and over low bandwidth channels limit the involvement of the operator, leaving them vulnerable to unanticipated scenarios. The NASA Space Robotics Challenge (SRC), held in 2016–17, posed a challenge to operate a simulated Valkyrie R5 humanoid robot over a minimum bandwidth of 64-4k bits/second uplink, 50k-380k bits/second downlink, and a maximum latency of 20 seconds. To achieve this, we designed and implemented extended state machines that allow a robot to perform known tasks autonomously in a partially known environment along with the flexibility to perform system critical interventions manually, if required. The main intuition behind our approach is to combine (a) sensor data redundancy for object detection and (b) 2-stage motion planning approach using state machines to successfully accomplish complex tasks. The complex tasks demonstrated are aligning a communication dish, picking up a solar panel, and deploying solar panels autonomously. The overall system design allowed successful completion of tasks even after subtask failures and/or complete communication loss.