Paul Oh

A parallel manipulator for mobile manipulating UAVs

Manipulating objects using arms mounted to unmanned aerial vehicles (UAVs) is attractive because UAVs may access many locations that are otherwise inaccessible to traditional mobile manipulation platforms such as ground vehicles. Most previous efforts seeking to coordinate the combined manipulator-UAV system have focused on using a manipulator to extend the UAVs reach and assume that both the UAV and manipulator can reliably reach commanded goal poses. This work accepts the reality that state of the art UAV positioning precision is not of a high enough quality to reliably perform simple tasks such as grasping objects. A 6 degree of freedom parallel manipulator is used to robustly maintain precise end-effector positions despite host UAV perturbations. A description of a unique parallel manipulator that allows for very little moving mass, and is easily stowed below a quadrotor UAV is presented along with flight test results and an analytical comparison to a serial manipulator.

The Ach Library: A New Framework for Real-Time Communication

Correct real-time software is vital for robots in safety-critical roles such as service and disaster response. These systems depend on software for locomotion, navigation, manipulation, and even seemingly innocuous tasks such as safely regulating battery voltage. A multiprocess software design increases robustness by isolating errors to a single process, allowing the rest of the system to continue operation. This approach also assists with modularity and concurrency. For real-time tasks, such as dynamic balance and force control of manipulators, it is critical to communicate the latest data sample with minimum latency. There are many communication approaches intended for both general-purpose and real-time needs [9], [13], [15], [17], [19]. Typical methods focus on reliable communication or network transparency and accept a tradeoff of increased message latency or the potential to discard newer data. By focusing instead on the specific case of real-time communication on a single host, we reduce communication latency and guarantee access to the latest sample. We present a new interprocess communication (IPC) library, Ach which addresses this need, and discuss its application for real-time multiprocess control on three humanoid robots (Figure 1). (Ach is available at http://www.golems.org/projects/ach.html. The name Ach comes from the common abbreviation for the motor neurotransmitter Acetylcholine and the computer networking term ACK.).

Coupling virtual reality and motion platforms for snowboard training

This paper proposes a snowboard simulation system that mitigates the risks of snowboarding while allowing for realistic beginner training. The system uses a passive motion platform with tension bands that return the platform to equilibrium when the user is not applying force. This allows the user to make manual changes to board position as they would on a snowy hill. These manual changes in board position are fed into the simulator, and the user experiences visual cuing as to changes in their simulated position, velocity, and acceleration. This snowboard simulator is the first to feature manual yaw with full range of motion, allowing users to train on essential beginning techniques like “the falling leaf” and paving the way for the future development of simulation of advanced techniques necessary for navigating advanced terrain.

Dexterous Aerial Robots—Mobile Manipulation Using Unmanned Aerial Systems

In this paper, we present selected benchmark aerial manipulation tasks using an aerial vehicle endowed with multi-degree of freedom manipulators. The proposed tasks analyze environmental coupling and are broken into three general categories: momentary, loose, and strong coupling. A classical control structure is derived, tuned, and verified through experiments, conducted for benchmarking purposes to include pick-and-place, insertion, and valve-turning tasks. Although other nonlinear controllers may prove more effective, the classical control approach has been selected in order to analyze contact stability and provide benchmark results for future reference. An analysis of system stability is conducted and implemented into the controller. A vision-based high-level controller fuses motion tracking data in order to provide control of both the aircraft and the manipulators, allowing the system to become coupled to the environment and perform the required operation. We present recent results validating our framework using the proposed aircraft-arm system.

Strategies for driving and egress for the vehicle of a humanoid robot in the drc finals 2015

This paper presents various strategies for humanoid vehicle driving and egress tasks. For driving, a tele-operating system that controls a robot based on a human operator’s commands is built. In addition, an autonomous assistant module is developed for the operator. Normal position control can result in severe damage to robots when they egress from vehicles. To prevent this problem, another approach that mixes various joint control techniques is adopted in this study. Additionally, a footplate is newly designed and attached to the vehicle floor for the ground landing phase of the egress task. The attached plate enables the robot to step down onto the ground in a safe manner. For stable locomotion, a balance controller is designed for the humanoid. For the design of the controller, the robot is modeled using an inverted pendulum that consists of a spring and a damper. Then, a state feedback controller (with pole placement and a state observer) is built based on the simplified model. Many approaches that are presented in this paper were successfully applied to a full-sized humanoid, DRC-HUBO+, in the DARPA Robotics Challenge Finals, which were held in the United States in 2015.

Optimization of humanoid's motions under multiple constraints in vehicle ingress task

This paper presents an approach on whole-body motion optimization for a humanoid robot to enter a ground vehicle. Motion capture system (mocap) was used to plan an initial suboptimal motion. Reinforcement learning was then implemented to optimize the trajectories with respect to kinematic and torque limits at the both body and the joint level. The cost functions in the body level calculated a robot’s static balancing ability, collisions and validity of the end-effector movement. Balancing and collision checks were computed from kinematic models of the robot and the vehicle model. Energy consumption such as torque limit obedience was checked at the joint level. Energy cost was approximated as joint torque, measured from a dynamic model. Various penalties such as joint angle and velocity limits were also computed in the joint level. Physical limits of each joint ensured both spatial and temporal smoothness of the generated trajectories. Finally, experimental evaluations of the presented approach were demonstrated through simulation and physical platforms in a real environment.

Mechatronics Education: From Paper Design to Product Prototype Using LEGO NXT Parts

The industrial design cycle starts with design then simulation, prototyping, and testing. When the tests do not match the design requirements the design process is started over again. It is important for students to experience this process before they leave their academic institution. The high cost of the prototype phase, due to CNC/Rapid Prototype machine costs, makes hands on study of this process expensive for students and the academic institutions. This document shows that the commercially available LEGO NXT Robot kit is a viable low cost surrogate to the expensive industrial CNC/Rapid Prototype portion of the industrial design cycle.

Mission Planning and Control

Unmanned aerial vehicles have attracted significant attention for a variety of structural inspection operations, for their ability to move in unstructured environments [3]. Typical examples include bridge inspection [26], power plant inspection [4], wind farm inspection [29], and maritime surveillance [28].

Team DRC-Hubo@UNLV in 2015 DARPA Robotics Challenge Finals

This chapter presents a technical overview of Team DRC-Hubo@UNLVs approach to the 2015 DARPA Robotics Challenge Finals (DRC-Finals). The Finals required a robotic platform that was robust and reliable in both hardware and software to complete tasks in 60 min under degraded communication. With this point of view, Team DRC-Hubo@UNLV integrated methods and algorithms previously verified, validated, and widely used in the robotics community. For the communication aspect, a common shared memory approach that the team adopted to enable efficient data communication under the DARPA controlled network is described. A new perception head design (optimized for the tasks of the Finals) and its data processing are then presented. In the motion planning and control aspect, various techniques, such as wheel-driven navigation, zero-moment point (ZMP)-based locomotion, and position-based manipulation and controls, are described in this chapter. By introducing strategically critical elements and key lessons learned from DRC-Trials 2013 and the testbed of Charleston, we also illustrate how DRC-Hubo has evolved successfully toward the DRC-Finals.

Coordinate Systems and Transformations

This chapter describes the coordinate systems used in depicting the position and orientation (pose) of the aerial robot and its manipulator arm(s) in relation to itself and its environment.