Snake robots are uniquely qualified to investigate a large variety of settings including archaeological sites, natural disaster zones, and nuclear power plants. For these applications, modular snake robots have been tele-operated to perform specific tasks using images returned to it from an onboard camera in the robots head. In order to give the operator an even richer view of the environment and to enable the robot to perform autonomous tasks we developed a structured light sensor that can make three-dimensional maps of the environment. This paper presents a sensor that is uniquely qualified to meet the severe constraints in size, power and computational footprint of snake robots. Using range data, in the form of 3D pointclouds, we show that it is possible to pair high-level planning with mid-level control to accomplish complex tasks without operator intervention.

This paper details the design and architecture of a series elastic actuated snake robot, the SEA Snake. The robot consists of a series chain of 1-DOF modules that are capable of torque, velocity and position control. Additionally, each module includes a high-speed Ethernet communications bus, internal IMU, modular electro-mechanical interface, and ARM based on-board control electronics.

We present a method of estimating a snake robot's motion inside and outside of straight pipes using only knowledge of the robot's joint angles. We accomplish this by introducing a novel method to constructing a body frame for the robot that is aligned with the centerline of the pipe. In addition to greatly simplifying the motion model this method allows us to accurately estimate the diameter of the pipe. We present experimental results using a 16-DOF snake robot traversing the inside and outside of straight pipes.

We present a general method of estimating a snake robot's motion over flat ground using only knowledge of the robot's shape changes over time. Estimating world motion of snake robots is often difficult because of the complex way a robot's cyclic shape changes (gaits) interact with the surrounding environment. By using the virtual chassis to separate the robot's internal shape changes from its external motions through the world, we are able to construct a motion model based on the differential motion of the robot's modules between time steps. In this way, we effectively treat the snake robot like a wheeled robot where the bottom-most modules propel the robot in much the way the bottom of the wheels would propel the chassis of a car. Experimental results using a 16-DOF snake robot are presented to demonstrate the effectiveness of this method for a variety of gaits that have been designed to traverse flat ground.