SummaryDART (Dynamic Animation and Robotics Toolkit) is a collaborative, cross-platform, open source library created by the Graphics Lab and Humanoid Robotics Lab at Georgia Institute of Technology with ongoing contributions from the Personal Robotics Lab at University of Washington and Open Source Robotics Foundation. The library provides data structures and algorithms for kinematic and dynamic applications in robotics and computer animation. DART is distinguished by its accuracy and stability due to its use of generalized coordinates to represent articulated rigid body systems in the geometric notations (Park, Bobrow, and Ploen 1995) and Featherstone's Articulated Body Algorithm (Featherstone 2008) using a Lie group formulation to compute forward dynamics (Ploen and Park 1999) and hybrid dynamics (Sohl and Bobrow 2001). For developers, in contrast to many popular physics engines which view the simulator as a black box, DART gives full access to internal kinematic and dynamic quantities, such as the mass matrix, Coriolis and centrifugal forces, transformation matrices and their derivatives. DART also provides an efficient computation of Jacobian matrices for arbitrary body points and coordinate frames. The frame semantics of DART allows users to define arbitrary reference frames (both inertial and non-inertial) and use those frames to specify or request data. For air-tight code safety, forward kinematics and dynamics values are updated automatically through lazy evaluation, making DART suitable for real-time controllers. In addition, DART provides flexibility to extend the API for embedding user-provided classes into DART data structures. Contacts and collisions are handled using an implicit time-stepping, velocity-based LCP (linear complementarity problem) to guarantee non-penetration, directional friction, and approximated Coulomb friction cone conditions (Stewart and Trinkle 1996). DART has applications in robotics and computer animation because it features a multibody dynamic simulator and various kinematic tools for control and motion planning.

Abstract-Traversing environments with arbitrary obstacles poses significant challenges for bipedal robots. In some cases, whole body motions may be necessary to maneuver around an obstacle, but most existing footstep planners can only select from a discrete set of predetermined footstep actions; they are unable to utilize the continuum of whole body motion that is truly available to the robot platform. Existing motion planners that can utilize whole body motion tend to struggle with the complexity of large-scale problems. We introduce a planning method, called the "Randomized Possibility Graph", which uses high-level approximations of constraint manifolds to rapidly explore the "possibility" of actions, thereby allowing lower-level motion planners to be utilized more efficiently. We demonstrate simulations of the method working in a variety of semi-unstructured environments. In this context, "semiunstructured" means the walkable terrain is flat and even, but there are arbitrary 3D obstacles throughout the environment which may need to be stepped over or maneuvered around using whole body motions.

We present a general system with a focus on addressing three events of the 2013 DARPA Robotics Challenge (DRC) trials: debris clearing, door opening, and wall breaking. Our hardware platform is DRC‐HUBO, a redesigned model of the HUBO2+ humanoid robot developed by KAIST and Rainbow, Inc. Our system allowed a trio of operators to coordinate a 32 degree‐of‐freedom robot on a variety of complex mobile manipulation tasks using a single, unified approach. In addition to descriptions of the hardware and software, and results as deployed on the DRC‐HUBO platform, we present some qualitative analysis of lessons learned from this demanding and difficult challenge.

No abstract

This paper presents the implementation of inverse kinematics to achieve teleoperation of a physical humanoid robot platform. The humanoid platform will be used to compete in the DARPA Robot Challenge, which requires autonomous execution of various search and rescue tasks, such as cutting through walls, which is a very practical application to robotics. Using a closed-form kinematic solution and a basic feedback controller, our objective of executing simple tasks is realized via teleoperation. Joint limits and singularities are accounted for using the different cases in the kinematic solution; and a decision method is implemented to determine how to position the end-effector when the goal is outside the feasible workspace.