CHIMP, the CMU Highly Intelligent Mobile Platform, is a humanoid robot capable of executing complex tasks in dangerous, degraded, human‐engineered environments, such as those found in disaster response scenarios. CHIMP is uniquely designed for mobile manipulation in challenging environments, as the robot performs manipulation tasks using an upright posture, yet it uses more stable prostrate postures for mobility through difficult terrain. In this paper, we report on the improvements made to CHIMP—both in its mechanical design and its software systems—in preparation for the DARPA Robotics Challenge Finals in June 2015. These include details on CHIMP's novel mechanical design, actuation systems, robust construction, all‐terrain mobility, supervised autonomy approach, and unique user interfaces utilized for the challenge. Additionally, we provide an overview of CHIMP's performance, and we detail the various lessons learned over the course of the challenge. CHIMP was one of the winners of the DARPA Robotics Challenge, completing all tasks and finishing in 3rd place out of 23 teams. Notably, CHIMP was the only robot to stand back up after accidentally falling over, a testament to the robustness engineered into the robot and a remote operator's ability to execute complex tasks using a highly capable robot. We present CHIMP as a concrete engineering example of a successful disaster response robot.

Abstract This paper outlines a new industry initiative and system supported by a group of Operators to reduce NPT and enhance safety performance. It addresses competency of Well-Site Supervisors with a group of Operators aspiring to create industry-wide certification. Its paradigm is Well Control Certification which it will complement, addressing all other areas within both drilling and completions. Competence and experience validation requires a systematic, reliable and repeatable approach. The paper will outline the challenges and response of an Operator group to Well-Site Supervisor Testing, Certification and Training. The Vision is to set the hallmark for quality Well-Site Supervisors globally by providing regularly updated certification. The objective is to: Prevent repetitive mistakes that cost the industry $billions each yearDifferentiate reliable people and support those that need assistanceMeasure reactions to simulations of events that occurred by presenting them as they unfold (to avoid hindsight engineering)Provide tools to improve and measure the individual's developmentShare the resource of quality graded Well-Site SupervisorsIncorporate ‘Human Factors’ to test interaction The downturn represents a unique opportunity to set a new standard for an upturn when there will be shortages of quality personnel. Operators will be able to identify qualified personnel and train those that don't yet meet requirements, providing more quality personnel to the industry.

Although fully autonomous robots continue to advance in ability, all points on the spectrum of cooperative interfaces between man and machine continue to have their place. We have developed a suite of operator assist technologies for a small (1 cubic meter volume) high speed robot that is intended to improve both speed and fidelity of control. These aids include fast stability control loops that run on the robot and graphical user interface enhancements that help the operator cope with lost peripheral vision, unstable video, and latency. After implementing the driving aids, we conducted an experiment where we evaluated the relative value of each from the perspective of their capacity to improve driving performance. Over a one week period, we tested 10 drivers in each of four driving configurations for three repetitions of a difficult test course. The results demonstrate that operators of all skill levels can benefit from the aids and that stabilized video and predictive displays are among the most valuable of the features we added.

The task of teleoperating a robot over a wireless video link is known to be very difficult. Teleoperation becomes even more difficult when the robot is surrounded by dense obstacles, or speed requirements are high, or video quality is poor, or wireless links are subject to latency. Due to high-quality lidar data, and improvements in computing and video compression, virtualized reality has the capacity to dramatically improve teleoperation performance — even in high-speed situations that were formerly impossible. In this paper, we demonstrate the conversion of dense geometry and appearance data, generated on-the-move by a mobile robot, into a photorealistic rendering model that gives the user a synthetic exterior line-of-sight view of the robot, including the context of its surrounding terrain. This technique converts teleoperation into virtual line-of-sight remote control. The underlying metrically consistent environment model also introduces the capacity to remove latency and enhance video compression. Display quality is sufficiently high that the user experience is similar to a driving video game where the surfaces used are textured with live video.

The DARPA PerceptOR program has implemented a rigorous evaluative test program which fosters the development of field relevant outdoor mobile robots. Autonomous ground vehicles were deployed on diverse test courses throughout the USA and quantitatively evaluated on such factors as autonomy level, waypoint acquisition, failure rate, speed, and communications bandwidth. Our efforts over the three year program have produced new approaches in planning, perception, localization, and control which have been driven by the quest for reliable operation in challenging environments. This paper focuses on some of the most unique aspects of the systems developed by the CMU PerceptOR team, the lessons learned during the effort, and the most immediate challenges that remain to be addressed.