Before beginning any robot task, users must position the robot's base, a task that now depends entirely on user intuition. While slight perturbation is tolerable for robots with moveable bases, correcting the problem is imperative for fixed-base robots if some essential task sections are out of reach. For mobile manipulation robots, it is necessary to decide on a specific base position before beginning manipulation tasks. This paper presents Reuleaux, an open source library for robot reachability analyses and base placement. It reduces the amount of extra repositioning and removes the manual work of identifying potential base locations. Based on the reachability map, base placement locations of a whole robot or only the arm can be efficiently determined. This can be applied to both statically mounted robots, where position of the robot and work piece ensure the maximum amount of work performed, and to mobile robots, where the maximum amount of workable area can be reached. Solutions are not limited only to vertically constrained placement, since complicated robotics tasks require the base to be placed at unique poses based on task demand. All Reuleaux library methods were tested on different robots of different specifications and evaluated for tasks in simulation and real world environment. Evaluation results indicate that Reuleaux had significantly improved performance than prior existing methods in terms of time-efficiency and range of applicability.
These methods allow planning for simple tasks, but they require a task to be completely specified prior to evaluation. @cite_13 explores the case where a specific trajectory is not given; rather the task has been simplified into a generalized workspace environment where the robot must work. Here, competing constraints are given. However the operator still evaluates and confirms the final base placement of the robot on the mobile platform. For a simple operation with few operating points, this can be done manually, but for more complex parts and tasks such as welding pipes or assembling parts in constrained spaces, a manual approach for validation may not be feasible.
@cite_13
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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 .