Sampling-based motion planning algorithm to handle a narrow passage problem

Abstract

Robot motion planning problem has been actively studied since 1970`s and has many applications, such as part disassembly, autonomous vehicle and computer graphics. Sampling-based algorithms have been successfully used to give a probabilistically complete solution for a variety of types of robots and constraints. Sampling-based algorithms, however, suffer when narrow passages are included in an environment. This phenomenon become worse as more constraints are related such as kinematic and dynamics constraints, because of its high degrees-of-freedom and complexity of extensions. In this thesis, we present novel sampling-based planners to efficiently handle various environments that have different characteristics and constraints. We first present a bridge line-test that can identify narrow passage regions in the configuration space, and then selectively performs an optimization-based retraction only at those regions. We also propose a non-colliding line-test, a dual operator to the bridge line-test, as a culling method to avoid generating samples near wide-open free spaces. These two line-tests are performed with a small computational overhead. As a result, proposed planner shows better performance than recent works for a variety of environments that have or do not have narrow passages. For a hyper-redundant robot manipulator, we define productive regions in the task space as a set of states that can lead effectively to a goal state. To check whether a node of a random tree is in productive regions or not, we construct a maximum reachable area (MRA) for a node in the task space, where a manipulator can reach from the node by using an employed local planner. When the MRA of a node contains the goal state, we call it promising and bias our sampling to cover promising MRAs. When the MRA does not contain the goal state, we call it unpromising and construct a detour sampling domain for detouring operations from obstacles constraining the manipulator. The union of prom...