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.
One of the challenges in developing a useful robotic system is designing a platform or workspace that lets the robot to effectively complete the desired task. Initial work on path planning and reachability was typically performed for simple grasp points on objects @cite_14 , which involves creating a map representing the areas of high dexterity for the manipulators. This work was then extended to the use of reachability maps to solve the inverse reachability task @cite_6 and @cite_12 , where the optimal base placement was found in order to perform the desired grasp on an object. Work done by @cite_10 examined ways to simplify the reachability map by generating a capability map, a simplified structure that permits faster and more efficient searches of the map in order to solve the inverse reachability problem.
@cite_14 @cite_10 @cite_12 @cite_6
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Mobile humanoid robots have the capability of accomplishing complex tasks in human dominated environments. In order to execute a task with its arm, a robot needs to place its base reasonably first. In previous work, a positionbased reachability map solved this problem by matching a precomputed inverse kinematics database with discretized versions of the task path. However, its capability is restricted to the end effector with which the database was computed. This paper proposes an orientation-based reachability map which supports on-line end effector frame extensions. The added extension capability enables robots to handle partially constrained task paths with different tool frames, which would be non-trivial for previous methods without recomputing a reachability map. We discuss the differences between the new method and its predecessors, and analyze extension transformation matrix to provide mathematical proof for the extension capability of our reachability map. Our experimental results show that the orientation-based reachability map is as fast as the positionbased reachability map while benefiting from the additional extension capability. We address the problem of planning a path for a ground robot through unknown terrain, using observations from a flying robot. In search and rescue missions, which are our target scenarios, the time from arrival at the disaster site to the delivery of aid is critically important. Previous works required exhaustive exploration before path planning, which is time-consuming but eventually leads to an optimal path for the ground robot. Instead, we propose active exploration of the environment, where the flying robot chooses regions to map in a way that optimizes the overall response time of the system, which is the combined time for the air and ground robots to execute their missions. In our approach, we estimate terrain classes throughout our terrain map, and we also add elevation information in areas where the active exploration algorithm has chosen to perform 3-D reconstruction. This terrain information is used to estimate feasible and efficient paths for the ground robot. By exploring the environment actively, we achieve superior response times compared to both exhaustive and greedy exploration strategies. We demonstrate the performance and capabilities of the proposed system in simulated and real-world outdoor experiments. To the best of our knowledge, this is the first work to address ground robot path planning using active aerial exploration. The utility of a mobile manipulator largely depends on its kinematic structure and mounting point on the robot body. The reachable workspace of the robot can be obtained offline and modeled as a discretized map called Reachability map. A Capability map is obtained by including some quality measure for the local dexterity of the manipulator, which helps to identify good and bad regions for manipulation. Once the maps are obtained based on forward or inverse kinematic methods, they can be used for numerous analysis tasks such as robot kinematics and workspace quality assessment, robot mounting point analysis or redundancy and failure analysis. This paper covers basic aspects of the Reachability and Capability map generation and storage, and shows particular applications of the maps for space robotics. Navigating in search and rescue environments is challenging, since a variety of terrains has to be considered. Hybrid driving-stepping locomotion, as provided by our robot Momaro, is a promising approach. Similar to other locomotion methods, it incorporates many degrees of freedom—offering high flexibility but making planning computationally expensive for larger environments. We propose a navigation planning method, which unifies different levels of representation in a single planner. In the vicinity of the robot, it provides plans with a fine resolution and a high robot state dimensionality. With increasing distance from the robot, plans become coarser and the robot state dimensionality decreases. We compensate this loss of information by enriching coarser representations with additional semantics. Experiments show that the proposed planner provides plans for large, challenging scenarios in feasible time.