Planning Practical Paths for Tentacle Robots

Robots with many degrees of freedom with one fixed end are known as tentacle robots due to their similarity to the tentacles found on squid and octopus. Tentacle robots offer advantages over traditional robots in many scenarios due to their enhanced flexibility and reachability. Planning practical paths for these devices is challenging due to their high degrees of freedom (DOFs). Sampling-based path planners are a commonly used approach for high DOF planning problems but the solutions found using such planners are often not practical in that they do not take into account soft application-specific constraints during the planning process. This paper describes a general sample adjustment method for tentacle robots, which adjusts the randomly generated nodes within their local neighborhood to satisfy soft constraints required by the problem. The approach is demonstrated on a planar tentacle robot composed of ten Robotis Dynamixel AX-12 servos.