Despite the performance advantages of modern sampling-based motion planners, solving high dimensional planning problems in near real-time remains a challenge. Applications include hyper-redundant manipulators, snake-like and humanoid robots. Based on the intuition that many of these problem instances do not require the robots to exercise every degree of freedom independently, we introduce an enhancement to popular sampling-based planning algorithms aimed at circumventing the exponential dependence on dimensionality. We propose beginning the search in a lower dimensional subspace of the configuration space in the hopes that a simple solution will be found quickly. After a certain number of samples are generated, if no solution is found, we increase the dimension of the search subspace by one and continue sampling in the higher dimensional subspace. In the worst case, the search subspace expands to include the full configuration space - making the completeness properties identical to the underlying sampling-based planer. Our experiments comparing the enhanced and traditional version of RRT, RRT-Connect, and BidirectionalT-RRT on both a planar hyper-redundant manipulator and the Baxter humanoid robot indicate that a solution is typically found much faster using this approach and the run time appears to be less sensitive to the dimension of the full configuration space. We explore important implementation issues in the sampling process and discuss its limitations.
Additionally, some works use RRTs for mobile manipulators. Vannoy @cite_4 propose an efficient and flexible algorithm for operating in dynamic environments. The work of Berenson @cite_22 provides an application of their technique to a 10-DoF mobile manipulator.
@cite_4 @cite_22
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We present an optimization-based approach to grasping and path planning for mobile manipulators. We focus on pick-and-place operations, where a given object must be moved from its start configuration to its goal configuration by the robot. Given only the start and goal configurations of the object and a model of the robot and scene, our algorithm finds a grasp and a trajectory for the robot that will bring the object to its goal configuration. The algorithm consists of two phases: optimization and planning. In the optimization phase, the optimal robot configurations and grasp are found for the object in its start and goal configurations using a co-evolutionary algorithm. In the planning phase, a path is found connecting the two robot configurations found by the optimization phase using Rapidly-Exploring Random Trees (RRTs). We benchmark our algorithm and demonstrate it on a 10 DOF mobile manipulator performing complex pick-and-place tasks in simulation. For mobile manipulators envisioned in home environments a kitchen scenario provides a challenging testbed for numerous skills. Diverse manipulation actions are required, e.g. simple pick and place for moving objects and constrained motions for opening doors and drawers. The robot kinematics and link limits however are restrictive. Therefore especially a constrained trajectory will not be executable from arbitrary placements of the mobile manipulator. A two stage approach is presented to position a mobile manipulator to execute constrained linear trajectories as needed for opening drawers. In a first stage, a representation of a robot armpsilas reachable workspace is computed. Pattern recognition techniques are used to find regions in the workspace representation where these trajectories are possible. A set of trajectories results. In the second stage mobile manipulator placements are computed and the corresponding trajectories are checked for collisions. Compared to a brute force search through the workspace, the success rate of finding a mobile manipulator placement can be increased from 2 to 70 . We present an approach to path planning for manipulators that uses Workspace Goal Regions (WGRs) to specify goal end-effector poses. Instead of specifying a discrete set of goals in the manipulator's configuration space, we specify goals more intuitively as volumes in the manipulator's workspace. We show that WGRs provide a common framework for describing goal regions that are useful for grasping and manipulation. We also describe two randomized planning algorithms capable of planning with WGRs. The first is an extension of RRT-JT that interleaves exploration using a Rapidly-exploring Random Tree (RRT) with exploitation using Jacobian-based gradient descent toward WGR samples. The second is the IKBiRRT algorithm, which uses a forward-searching tree rooted at the start and a backward-searching tree that is seeded by WGR samples. We demonstrate both simulation and experimental results for a 7DOF WAM arm with a mobile base performing reaching and pick-and-place tasks. Our results show that planning with WGRs provides an intuitive and powerful method of specifying goals for a variety of tasks without sacrificing efficiency or desirable completeness properties. We present a new retraction algorithm for high DOF articulated models and use our algorithm to improve the performance of RRT planners in narrow passages. The retraction step is formulated as a constrained optimization problem and performs iterative refinement on the boundary of C-Obstacle space. We also combine the retraction algorithm with decomposition planners to handle very high DOF articulated models. The performance of our approach is analyzed using Voronoi diagrams and we show that our retraction algorithm provides a good approximation to the ideal RRT-extension in constrained environments. We have implemented our algorithm and tested its performance on robots with more than 40 DOFs in complex environments. In practice, we observe significant performance (2–80X) improvement over prior RRT planners on challenging scenarios with narrow passages.