Landmark Guided Probabilistic Roadmap Queries

A landmark based heuristic is investigated for reducing query phase run-time of the probabilistic roadmap (\PRM) motion planning method. The heuristic is generated by storing minimum spanning trees from a small number of vertices within the \PRM graph and using these trees to approximate the cost of a shortest path between any two vertices of the graph. The intermediate step of preprocessing the graph increases the time and memory requirements of the classical motion planning technique in exchange for speeding up individual queries making the method advantageous in multi-query applications. This paper investigates these trade-offs on \PRM graphs constructed in randomized environments as well as a practical manipulator simulation.We conclude that the method is preferable to Dijkstra's algorithm or the ${\rm A}^*$ algorithm with conventional heuristics in multi-query applications.Comment: 7 Page

An adaptive framework for 'single shot' motion planning

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Includes bibliographical references (leaves 19-21).Automatic motion planning has applications in many areas such as robotics, virtual reality systems, and computer-aided design. Although many different motion planning methods have been proposed, most are not used in practice since they are computationally infeasible except for some restricted cases, e.g., when the robot has very few degrees of freedom (dof). For this reason, attention has focussed on randomized or probabilistic motion planning methods. When many motion planning queries will be performed in the same environment, then it may be useful to pre-process the environment with the goal of decreasing the difficulty of the subsequent queries. Examples are the roadmap motion planning methods, which build a graph encoding representative feasible paths (usually in the robot's configuration space, which is the parametric spacer representing all possible positions and orientations of the robot in the workspace). Indeed, recently several probabilistic roadmap methods (PRMs) (including our group's obstacle-based PRM ) have been used to solve many difficult planning problems involving high-dimensional C-spaces that could not be solved before. However, if the start and goal configurations are known a priori, only one (or a very few) queries will be performed in a single environment, then it is generally not worthwhile to perform an expensive preprocessing stage, particularly if there are time constraints as in animation or virtual reality applications. In this case, a more directed search of the free configuration space is needed (e.g., as opposed to roadmap methods which are designed to try to cover the entire freespace). Motion planning methods that operate in this fashion are often called single shot methods. In our current work, we are developing an adaptive framework for single shot motion planning (i.e., planning without preprocessing). This framework can be used in any situation, and in particular, is suitable for crowded environments in which the robot's free C-space has narrow corridors. The main idea of our framework is that one should adaptively select a planner whose strengths match the current situation, and then switch to a different planner when circumstances change. This approach requires that we develop a set of planners, and characterize the strengths and weaknesses of each planner in such a way that we can easily select the best planner for the current situation. Our experimental results show that adaptive selection of different planning methods enables the algorithms to be used in a cooperative manner to successfully solve queries that none of them would be able to solve on their own

Real-time reach planning for animated characters using hardware acceleration

We present a heuristic-based real-time reach planning algorithm for virtual human figures. Given the start and goal positions in a 3D workspace, our problem is to compute a collision-free path that specifies all the configurations for a human arm to move from the start to the goal. Our algorithm consists of three modules: spatial search, inverse kinematics, and collision detection. For the search module, instead of searching in joint configuration space like most existing motion planning methods do, we run a direct search in the workspace, guided by a heuristic distance-to-goal evaluation function. The inverse kinematics module attempts to select natural posture configurations for the arm along the path found in the workspace. During the search, candidate configurations will be checked for collisions taking advantage of the graphics hardware – depth buffer. The algorithm is fast and easy to implement. It allows real-time planning not only in static, structured environments, but also in dynamic, unstructured environments. No preprocessing and prior knowledge about the environment is required. Several examples are shown illustrating the competence of the planner at generating motion plans for a typical human arm model with seven degrees of freedom