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Aaron Johnson

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Overview

I developed a framework for detecting, identifying, and recovering within one stride from faults and other leg contact disturbances encountered by a walking hexapedal robot.

Overview
Overview of fault detection, identification, and recovery framework.

Virtual Proprioception

Detection is achieved by means of a software contact event sensor with no additional sensing hardware beyond the commercial actuators’ standard shaft encoders. Recovery proceeds as necessary by means of a recently developed topological gait transition. The dynamics of the motor and leg are modeled and used to run an open loop observer. This model includes the PD control as well as gravity compensation and network time delay terms. When the leg contacts the ground, a large, clear spike in the virtual sensor is evident. The level returns to near zero in recirculation.

Ground Contact Detection:

The output of this observer is passed through an identification stage to decide if the leg state is

  • in flight (recirculation)
  • an expected disturbance, namely the ground
  • an unexpected disturbance, such as a wall or rock
  • a missing disturbance, when there is no ground contact due to a leg break or other fault

Additionally there are two transitional stages that help make identification more accurate and timely

Reactive Behaviors

Maze Solving The robot uses the front two legs as an obstacle sensor (instead of a range finder, whiskers, laser scanner, etc). When the leg experiences an unexpected disturbance and is in the forward direction, the robot has hit a wall. When a wall is detected, the robot will back up and turn right. A more sophisticated maze solving algorithm could also be used.

Leg Break When the robot detects a missing ground contact from a leg break or other fault, it will switch to a 5 legged gait which saves the body from hitting the ground on every step.

Current Work

Current Sensors We have recently upgraded the robot hardware to include a motor current sensor that can be used to drive an additional software sensor similar to the one described here.

Terrain Identification By using a combination of these virtual proprioceptive sensors as well as a standard IMU we hope to develop a terrain classifier to decide what type of terrain the robot is running on based on how it “feels” to the robot.

Publications

Disturbance Detection, Identification, and Recovery by Gait Transition in Legged Robots
Aaron M. Johnson, G. Clark Haynes, D. E. Koditschek
Proceedings of the 2010 IEEE/RSJ Intl. Conference on Intelligent Robots and Systems, October 2010. Full PDF

Autonomous Legged Hill and Stairwell Ascent
Aaron M. Johnson, Matthew T. Hale, G. C. Haynes, and D. E. Koditschek
IEEE International Symposium on Safety, Security, and Rescue Robotics, November 2011. Full PDF

Terrain Identification for RHex-type Robots
Camilo Ordonez, Jacob Shill, Aaron M. Johnson, Jonathan Clark, and Emmanuel G. Collins
Proceedings of the SPIE Defense, Security, and Sensing Conference, Unmanned Systems Technology XV (8741), May, 2013. Full PDF


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