A review on snake robot testbeds in granular and restricted maneuverability spaces Academic Article uri icon


  • © 2018 Elsevier B.V. This article reviews the state of the art in evaluating snake robots for small spaces such as a collapsed building where the snake is either locomoting in restricted maneuverability spaces, such as narrow pipes or tunnels, or pushing through granular regions, such as dirt and rubble. It makes recommendations on designing a testbed that can enable a comprehensive evaluation of a snake robot's overall capability and an objective comparison of different snakes. A survey of 31 papers reveals that 20 testbeds were used to test snake robots in restricted maneuverability environments. All of those were built specifically to test a particular snake robot rather than for comparison with other snake robots, but each offers insights into designing comprehensive, comparative testbeds. The article analyzed these 20 testbeds in terms of how well they addressed the previously established disaster robotics metrics of scale (a dimensionless number) and four traversability elements, i.e. verticality, tortuosity, accessibility elements, and surface properties. This review suggests that two kinds of general testbeds are in need for the snake robot community: (1) a testbed with high physical fidelity for measuring suitability for a target application, and (2) a testbed which provides a dimensionless comparison of different snake robots. The review is expected to benefit the community in several ways. It can help form a consensus on a suite of metrics and test methods to incorporate into a testbed for evaluating and comparing different types of snake robots and capturing the performance of snake robots in more realistic work envelopes. The metrics and test methods can also pro-actively inform snake robot design as they offer more formally quantified work envelopes, thus accelerating technology transfer. The use of scale and traversability is expected to be applicable to robots in general.

author list (cited authors)

  • Xiao, X., & Murphy, R.

citation count

  • 4

publication date

  • December 2018