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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Humanoid robots are envisioned in general household tasks. To be able to fulfill a given task the robot needs to be equipped with knowledge concerning the manipulation and interaction in the environment and with knowledge about its own capabilities. When performing actions, e.g. opening doors or imitating human reach to grasp movements special 3-d trajectories are followed with the robot's end-effector. These trajectories can not be executed in every part of the robot's arm workspace. Therefore a task planner has to determine if and how additional degrees of freedom such as the robot's upper body or the robot's base can be moved in order to execute the task-specific trajectory. An approach is presented that computes placements for a mobile manipulator online given a task-related 3-d trajectory. A discrete representation of the robot arm's reachable workspace is used. Task-specific trajectories are interpreted as patterns and searched in the reachability model using multi-dimensional correlation. The relevance of the presented approach is demonstrated in simulated positioning tasks. We have developed the CHIMP CMU Highly Intelligent Mobile Platform robot as a platform for executing complex tasks in dangerous, degraded, human-engineered environments. CHIMP has a near-human form factor, work-envelope, strength, and dexterity to work effectively in these environments. It avoids the need for complex control by maintaining static rather than dynamic stability. Utilizing various sensors embedded in the robot's head, CHIMP generates full three-dimensional representations of its environment and transmits these models to a human operator to achieve latency-free situational awareness. This awareness is used to visualize the robot within its environment and preview candidate free-space motions. Operators using CHIMP are able to select between task, workspace, and joint space control modes to trade between speed and generality. Thus, they are able to perform remote tasks quickly, confidently, and reliably, due to the overall design of the robot and software. CHIMP's hardware was designed, built, and tested over 15i¾?months leading up to the DARPA Robotics Challenge. The software was developed in parallel using surrogate hardware and simulation tools. Over a six-week span prior to the DRC Trials, the software was ported to the robot, the system was debugged, and the tasks were practiced continuously. Given the aggressive schedule leading to the DRC Trials, development of CHIMP focused primarily on manipulation tasks. Nonetheless, our team finished 3rd out of 16. With an upcoming year to develop new software for CHIMP, we look forward to improving the robot's capability and increasing its speed to compete in the DRC Finals. A pick-and-place operation in a 3-dimensional environment is a basic operation for humans and multi-purpose manipulators. However, there may be a difficult problem for such manipulators. Especially, if the object cannot be moved with a single grasp, regrasping, which can be a time-consuming process, should be carried out. Regrasping, given initial and final poses of the target object, is a construction of sequential transition of object poses that are compatible with two poses in the point of grasp configuration. This paper presents a novel approach for solving the regrasp problem. The approach consists of a preprocessing and a planning stage. Preprocessing, which is done only once for a given robot, generates a look-up table which has information on kinematically feasible task space of the end-effector throughout the entire workspace. Then, using this table, the planning automatically determines a possible intermediate location, pose and regrasp sequence leading from the pick-up to put-down grasp. With a redundant robot, it is shown experimentally that the presented method is complete in the entire workspace and can be implemented in real-time applications due to rapid regrasp planning time. The regrasp planner was combined with an existing path. Abstract In which order can a product be assembled or disassembled? How many hands are required? How many degrees of freedom? What parts should be withdrawn to allow the removal of a specified subassembly? To answer such questions automatically, important theoretical issues in geometric reasoning must be addressed. This paper investigates the planning of assembly algorithms specifying (dis) assembly operations on the components of a product and the ordering of these operations. It also presents measures to evaluate the complexity of these algorithms and techniques to estimate the inherent complexity of a product. The central concept underlying these planning and complexity evaluation techniques is that of a “non-directional blocking graph”, a qualitative representation of the internal structure of an assembly product. This representation describes the combinatorial set of parts interactions in polynomial space. It is obtained by identifying physical criticalities where geometric interferences among parts change. It is generated from an input geometric description of the product. The main application considered in the paper is the creation of smart environments to help designers create products that are easier to manufacture and service. Other possible applications include planning for rapid prototyping and autonomous robots.