MB-RRT: An inverse kinematics solver of reduced dimension

The evolution of manipulator robots has increased the complexity of their models and applications, requiring that the inverse kinematics (IK) methods integrated into their control systems to have features such as fast convergence, completeness, low computational cost, and the ability to avoid local minima and singularities. We propose in this paper a new probabilistic IK solver based on the probabilistic search method known as Rapidly-Exploring Random Tree (RRT), the Workspace-RRT. The technique grows the tree as a spatial representation of the manipulator on the workspace instead of the configuration space, which reduces the search space up to 3 dimensions. Based on this new representation we also present the Manipulator-Based Rapidly Random Tree (MB-RRT) by incorporating to the Workspace-RRT a new probability model and a new metric for the closest node. We evaluate the presented methods through simulated experiments in the Matlab software. First, we evaluate the impact of the proposed aspects through a comparison between the RRT-based IK solvers, which emphasizes the proposed changes as a key to make the method suitable for the IK problem. At last, we show the use of the MB-RRT for precision tasks and obstructed environments.