The ability of robotic systems to effectively address disaster scenarios that are potentially dangerous for human operators is continuing to grow as a research and development field. This leverages research from areas such as bimanual manipulation, dexterous grasping, bipedal locomotion, computer vision, sensing, object segmentation, varying degrees of autonomy, and operator control/feedback. This paper describes the development of a semi-autonomous bimanual dexterous robotic system that comes to the aid of a mannequin simulating an injured victim by operating a fire extinguisher, affixing a cervical collar, cooperatively placing the victim on a spineboard with another bimanual robot, and relocating the victim. This system accomplishes these tasks through a series of control modalities that range from supervised autonomy to full teleoperation and allows the control model to be chosen and optimized for a specific subtask. We present a description of the hardware platform, the software control architecture, a human-in-the-loop computer vision algorithm, and an infrastructure to use a variety of user input devices in combination with autonomous control to compete several dexterous tasks. The effectiveness of the system was demonstrated in both laboratory and live outdoor demonstrations.

This paper introduces a new, 50g miniature robot (Figure 1 and attached video) that uses four direct-drive hybrid wheel/legs and has been clocked at speeds exceeding 30 body lengths/s. The lowest recorded cost of transport (defined as total energy to move the robot mass a given distance) is 0.90. We discuss hardware considerations for design of robots at this scale, and the benefits of a direct-drive system over coupled transmissions. We develop a simple simulation model to examine the effects of drive speed and mechanical properties of the legs, and compare the results with experimental data on efficiency and effectiveness of locomotion with various leg designs.

A dexterous manipulator (DM) with a large open lumen is presented. The manipulator is designed for surgical applications with a preliminary focus on the removal of osteolysis formed behind the acetabular shell of primary total hip arthroplasties (THAs). The manipulator is constructed from two nested superelastic nitinol tubes enabling lengthwise channels for drive cables. Notches in the nested assembly provide reliable bending under applied cable tension producing kinematics that can be effectively modeled as a series of rigid vertebrae connected using pin joints. The manipulator is controlled in plane with two independently actuated cables in a pull-pull configuration. For the purpose of the procedure, the manipulator is mounted on a Z-θ stage adding a translational and rotational degree of freedom (DOF) along the axis of the manipulator. Preliminary experimental results demonstrate the initial modeling and control of the manipulator.

A new self-reconfigurable robot is presented. The robot is a hybrid chain/lattice design with several novel features. An active mechanical docking mechanism provides inter-module connection, along with optical and electrical interface. The docking mechanisms function additionally as driven wheels. Internal slip rings provide unlimited rotary motion to the wheels, allowing the modules to move independently by driving on flat surfaces, or in assemblies negotiating more complex terrain. Modules in the system are mechanically homogeneous, with three identical docking mechanisms within a module. Each mechanical dock is driven by a high torque actuator to enable movement of large segments within a multi-module structure, as well as low-speed driving. Preliminary experimental results demonstrate locomotion, mechanical docking, and lifting of a single module.

Human hands employ characteristic patterns of actuation, or synergies, that contain much of the information required to describe an entire hand shape. In some cases, 80% or more of the total information can be described with only two scalar component values. Robotic hands, however, commonly only couple intra-finger joints, and rarely take advantage of this inter-finger coordination. In this paper, real-world data on a variety of human hand postures was collected using a data glove, and principal components analysis was used to calculate these synergies, resulting in what we call eigenpostures. A novel mechanism design is presented to combine the eigenpostures and drive a 17-degree-of-freedom 5-fingered robot hand. The hand uses only 2 DC motors to accurately recreate a wide range of hand shapes. We also present a design improvement that allows us to distinguish between high-precision and low-precision tasks, as well as greatly reduce overall error.