Path Planning for Tentacle Robots Using Soft Constraints

Tentacle robots - robots with many degrees of freedom with one fixed end - 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 number of degrees of freedom (DOFs). Sampling-based path planners are a common approach to the high DOF planning problem associated with tentacle robots but the solutions found using such planners are often not practical in that they do not take into account soft application-specific constraints. This paper describes a general sample adjustment method for tentacle robots which adjusts the nodes and edges generated by the sampling-based planners within their local neighborhood to satisfy soft constraints associated with the problem. Experiments with real and simulated tentacle robots demonstrate that our approach is an effective enhancement to the basic probabilistic planner to find practical paths.