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 hyperredundant 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 promising MRAs and detour sampling domains approximates our productive regions, and we bias sampling regions to cover these domains more. Finally, we present a novel kinodynamic motion planner designed for complex dynamics environments with many obstacles. We define a motion database that is constructed as a preprocessing and can replace time-consuming propagation function integration of kinodynamic sampling-based planners. Motion interpolation is applied for retrieving large set of motions in the continuous state space. During iterations, interpolated motions are selectively validated by the employed simulator in order to increase the efficiency of the planner. Constructed motion database can be also used for other problems such as different composition of obstacles or different start/goal state. Overall, proposed methods achieve several times improvement over the state-of-the-art planning algorithms.

Sampling-based motion planning algorithm to handle a narrow passage problem = 좁은 길 문제를 해결하기 위한 샘플링 기반 모션 플래닝 알고리즘

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Sampling-based motion planning algorithm to handle a narrow passage problem = 좁은 길 문제를 해결하기 위한 샘플링 기반 모션 플래닝 알고리즘