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.

The versatility of snake robots has led to their use in a wide variety of settings, including archaeological sites, natural disaster zones, and nuclear power plants. Currently, snake robots locomote through these rugged environments using repeatable pre-programmed motions, often with underwhelming performance. This paper presents the novel design of a control architecture that addresses the limitations of pre-programmed motions by using contact information from the environment. The controller uses contact force optimization, a concept taken from the field of manipulation, in combination with gain scheduling, to perturb existing gait controllers to perform better in three-dimensional environments. The efficacy of the controller is tested in a simulation of a snake robot on rugged terrain.