Robot System of DRC‐HUBO+ and Control Strategy of Team KAIST in DARPA Robotics Challenge Finals

This paper summarizes how Team KAIST prepared for the DARPA Robotics Challenge (DRC) Finals, especially in terms of the robot system and control strategy. To imitate the Fukushima nuclear disaster situation, the DRC performed eight tasks and degraded communication conditions. This competition demanded various robotic technologies, such as manipulation, mobility, telemetry, autonomy, and localization. Their systematic integration and the overall system robustness were also important issues in completing the challenge. In this sense, this paper presents a hardware and software system for the DRC‐HUBO+, a humanoid robot that was used for the DRC; it also presents control methods, such as inverse kinematics, compliance control, a walking algorithm, and a vision algorithm, all of which were implemented to accomplish the tasks. The strategies and operations for each task are briefly explained with vision algorithms. This paper summarizes what we learned from the DRC before the conclusion. In the competition, 25 international teams participated with their various robot platforms. We competed in this challenge using the DRC‐HUBO+ and won first place in the competition.     

An Architecture for Human‐Guided Autonomy: Team TROOPER at the DARPA Robotics Challenge Finals

Recent robotics efforts have automated simple, repetitive tasks to increase execution speed and lessen an operator's cognitive load, allowing them to focus on higher‐level objectives. However, an autonomous system will eventually encounter something unexpected, and if this exceeds the tolerance of automated solutions, there must be a way to fall back to teleoperation. Our solution is a largely autonomous system with the ability to determine when it is necessary to ask a human operator for guidance. We call this approach human‐guided autonomy. Our design emphasizes human‐on‐the‐loop control where an operator expresses a desired high‐level goal for which the reasoning component assembles an appropriate chain of subtasks. We introduce our work in the context of the DARPA Robotics Challenge (DRC) Finals. We describe the software architecture Team TROOPER developed and used to control an Atlas humanoid robot. We employ perception, planning, and control automation for execution of subtasks. If subtasks fail, or if changing environmental conditions invalidate the planned subtasks, the system automatically generates a new task chain. The operator is able to intervene at any stage of execution, to provide input and adjustment to any control layer, enabling operator involvement to increase as confidence in automation decreases. We present our performance at the DRC Finals and a discussion about lessons learned.     

Technical Overview of Team DRC‐Hubo@UNLV's Approach to the 2015 DARPA Robotics Challenge Finals

This paper presents a technical overview of Team DRC‐Hubo@UNLV's 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 paper. 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.     

Amphibious Pattern Design of a Robotic Fish with Wheel‐propeller‐fin Mechanisms

This paper is devoted to the underwater and terrestrial locomotion aspects of an amphibious robotic fish propelled by modular fish‐like propelling units and a pair of hybrid wheel‐propeller‐fin mechanisms. According to the mechanical structure and locomotion characteristics of the robot, a central pattern generator (CPG) network comprising coupled oscillators is employed to produce signals for swimming, crawling, as well as transitions between them. Specifically, a set of four key parameters including a tonic input drive, a direction factor, and two pitch factors is introduced to serve as input to the CPG network. Meanwhile, a finite state machine is built to trigger locomotor pattern transitions. Field tests on the amphibious patterns and autonomous water‐land transition demonstrate the effectiveness of the adopted CPG‐based control architecture. The latest results show that the robot attained a maximum advancing speed of 1.16 m/s (corresponding to 1.66 body lengths per second), a minimal turning radius of approximately 0.55 m (corresponding to 0.79 body lengths) on land, as well as an average rolling speed of 204 degrees per second in an alligator‐like roll maneuver. It is also found that the dolphin‐like dorsoventral swimming could provide an increase of 10.3% in speed compared to the fish‐like carangiform swimming on the same propulsion platform.     

A Non-Time Based Controller for Load Swing Damping and Path-Tracking in Robotic Cranes

This paper proposes the use of the non-time based control strategy named Delayed Reference Control (DRC) to the control of industrial robotic cranes. Such a control scheme has been developed to achieve two relevant objectives in the control of autonomous operated cranes: the active damping of undesired load swing, and the accurate tracking of the planned path through space, with the preservation of the coordinated Cartesian motion of the crane. A paramount advantage of the proposed scheme over traditional ones is its ease of implementation on industrial devices: it can be implemented by just adding an outer control loop (incorporating path planning) to standard position controllers. Experimental performance assessment of the proposed control strategy is provided by applying the DRC to the control of the oscillation of a cable-suspended load moved by a parallel robot mimicking a robotic crane. In order to implement the DRC scheme on such an industrial robot it has been just necessary to manage path planning and the DRC algorithm on a separate real-time hardware computing the delay in the execution of the desired trajectory suitable to reduce load swing. Load swing has been detected by processing the images from two off-the-shelf cameras with a dedicated vision system. No customization of the robot industrial controller has been necessary.     

Backward Ladder Climbing Locomotion of Humanoid Robot with Gain Overriding Method on Position Control

We studied ladder climbing locomotion with the humanoid robot, DRC‐HUBO, under the constraints suggested by DARPA. Considering the hardware constraints of the robot platform, we planned for the robot to climb backward with four limbs moving separately. Task‐priority whole‐body inverse kinematics was used to generate and track the motion while maintaining COM inside the support polygon. As ladder climbing is a multicontact motion that generates interaction and internal forces, we resolved these issues using a gain overriding method applied to the position control of the motor controllers. This paper also provides various vision methods and posture modification strategies for the restricted conditions of the challenge. We ultimately verified our work in the DRC trials by getting a full score on the ladder task.     

WALK‐MAN: A High‐Performance Humanoid Platform for Realistic Environments

In this work, we present WALK‐MAN, a humanoid platform that has been developed to operate in realistic unstructured environment, and demonstrate new skills including powerful manipulation, robust balanced locomotion, high‐strength capabilities, and physical sturdiness. To enable these capabilities, WALK‐MAN design and actuation are based on the most recent advancements of series elastic actuator drives with unique performance features that differentiate the robot from previous state‐of‐the‐art compliant actuated robots. Physical interaction performance is benefited by both active and passive adaptation, thanks to WALK‐MAN actuation that combines customized high‐performance modules with tuned torque/velocity curves and transmission elasticity for high‐speed adaptation response and motion reactions to disturbances. WALK‐MAN design also includes innovative design optimization features that consider the selection of kinematic structure and the placement of the actuators with the body structure to maximize the robot performance. Physical robustness is ensured with the integration of elastic transmission, proprioceptive sensing, and control. The WALK‐MAN hardware was designed and built in 11 months, and the prototype of the robot was ready four months before DARPA Robotics Challenge (DRC) Finals. The motion generation of WALK‐MAN is based on the unified motion‐generation framework of whole‐body locomotion and manipulation (termed loco‐manipulation). WALK‐MAN is able to execute simple loco‐manipulation behaviors synthesized by combining different primitives defining the behavior of the center of gravity, the motion of the hands, legs, and head, the body attitude and posture, and the constrained body parts such as joint limits and contacts. The motion‐generation framework including the specific motion modules and software architecture is discussed in detail. A rich perception system allows the robot to perceive and generate 3D representations of the environment as well as detect contacts and sense physical interaction force and moments. The operator station that pilots use to control the robot provides a rich pilot interface with different control modes and a number of teleoperated or semiautonomous command features. The capability of the robot and the performance of the individual motion control and perception modules were validated during the DRC in which the robot was able to demonstrate exceptional physical resilience and execute some of the tasks during the competition.     

Dynamics and Noncollocated Model‐Free Position Control for a Space Robot with Multi‐Link Flexible Manipulators

In this paper, both the dynamics and noncollocated model‐free position control (NMPC) for a space robot with multi‐link flexible manipulators are developed. Using assumed modes approach to describe the flexible deformation, the dynamic model of the flexible space robotic system is derived with Lagrangian method to represent the system dynamic behaviors. Based on Lyapunov's direct method, the robust model‐free position control with noncollocated feedback is designed for position regulation of the space robot and vibration suppression of the flexible manipulators. The closed‐loop stability of the space robotic system can be guaranteed and the guideline of choosing noncollocated feedback is analyzed. The proposed control is easily implementable for flexible space robot with both uncertain complicated dynamic model and unknown system parameters, and all the control signals can be measured by sensors directly or obtained by a backward difference algorithm. Numerical simulations on a two‐link flexible space robot are provided to demonstrate the effectiveness of the proposed control.     

Fault‐tolerant on‐board computing for robotic space missions

This paper describes an approach to providing software fault tolerance for future deep‐space robotic National Aeronautics and Space Administration missions, which will require a high degree of autonomy supported by an enhanced on‐board computational capability. We focus on introspection‐based adaptive fault tolerance guided by the specific requirements of applications. Introspection supports monitoring of the program execution with the goal of identifying, locating, and analyzing errors. Fault tolerance assertions for the introspection system can be provided by the user, domain‐specific knowledge, or via the results of static or dynamic program analysis. This work is part of an on‐going project at the Jet Propulsion Laboratory in Pasadena, California. Copyright © 2011 John Wiley & Sons, Ltd.     

Team VALOR's ESCHER: A Novel Electromechanical Biped for the DARPA Robotics Challenge

The Electric Series Compliant Humanoid for Emergency Response (ESCHER) platform represents the culmination of four years of development at Virginia Tech to produce a full‐sized force‐controlled humanoid robot capable of operating in unstructured environments. ESCHER's locomotion capability was demonstrated at the DARPA Robotics Challenge (DRC) Finals when it successfully navigated the 61 m loose dirt course. Team VALOR, a Track A team, developed ESCHER leveraging and improving upon bipedal humanoid technologies implemented in previous research efforts, specifically for traversing uneven terrain and sustained untethered operation. This paper presents the hardware platform, software, and control systems developed to field ESCHER at the DRC Finals. ESCHER's unique features include custom linear series elastic actuators in both single and dual actuator configurations and a whole‐body control framework supporting compliant locomotion across variable and shifting terrain. A high‐level software system designed using the robot operating system integrated various open‐source packages and interfaced with the existing whole‐body motion controller. The paper discusses a detailed analysis of challenges encountered during the competition, along with lessons learned that are critical for transitioning research contributions to a fielded robot. Empirical data collected before, during, and after the DRC Finals validate ESCHER's performance in fielded environments.     

空间机器人控制系统硬件仿真平台的研究

建立了空间机器人控制系统的硬件仿真平台。研究了空间机器人基于手眼视觉的控制问题,建立了系统关键部件的模拟设备。仿真平台由中央控制器、关节模拟器、手眼模拟器、动力学/运动学仿真计算机和三维动画显示计算机组成。基于该平台,对空间机器人控制特性和仿真过程中的延时环节进行了研究。系统自主捕获仿真试验结果表明,所采用的运动控制算法能够稳定收敛于目标,仿真平台能够较好地完成对实际机器人系统控制过程的模拟测试及系统控制算法的验证。     

Collision‐free control of robotic manipulators in the task space

This paper addresses the problem of position control of robotic manipulators in the task space with obstacles. A computationally simple class of task space regulators consisting of a transpose Jacobian controller plus an integral term including the task error and the gradient of a penalty function generated by obstacles is proposed. The Lyapunov stability theory is used to derive the control scheme. Through the use of the exterior penalty function approach, collision avoidance of the robot with obstacles is ensured. The performance of the proposed control strategy is illustrated through computer simulations for a direct‐drive arm of a SCARA type manipulator operating in both an obstacle‐free task space and a task space including obstacles. © 2005 Wiley Periodicals, Inc.     

A new 6‐DOF parallel robotic structure actuated by wires: The WiRo‐6.3

This paper describes a novel parallel robotic structure with six degrees of freedom, whose end effector is driven by nine wires operated by motors: the WiRo‐6.3. The ideas that led to the conception of the robot are thoroughly discussed and analyzed. The workspace of WiRo‐6.3 has been numerically analyzed, and it is significantly larger than the one of other analogous seven‐wire structures. The forward and inverse kinematics are both solved in closed form. © 2004 Wiley Periodicals, Inc.     

Cloud‐based Real‐time Outsourcing Localization for a Ground Mobile Robot in Large‐scale Outdoor Environments

Cloud robotics is the application of cloud computing concepts to robotic systems. It utilizes modern cloud computing infrastructure to distribute computing resources and datasets. Cloud‐based real‐time outsourcing localization architecture is proposed in this paper to allow a ground mobile robot to identify its location relative to a road network map and reference images in the cloud. An update of the road network map is executed in the cloud, as is the extraction of the robot‐terrain inclination (RTI) model as well as reference image matching. A particle filter with a network‐delay‐compensation localization algorithm is executed on the mobile robot based on the local RTI model and the recognized location both of which are sent from the cloud. The proposed methods are tested in different challenging outdoor scenarios with a ground mobile robot equipped with minimal onboard hardware, where the longest trajectory was 13.1 km. Experimental results show that this method could be applicable to large‐scale outdoor environments for autonomous robots in real time.     

Team THOR's Entry in the DARPA Robotics Challenge Finals 2015

This paper describes Team THOR's approach to human‐in‐the‐loop disaster response robotics for the 2015 DARPA Robotics Challenge (DRC) Finals. Under the duress of unpredictable networking and terrain, fluid operator interactions and dynamic disturbance rejection become major concerns for effective teleoperation. We present a humanoid robot designed to effectively traverse a disaster environment while allowing for a wide range of manipulation abilities. To complement the robot hardware, a hierarchical software foundation implements network strategies that provide real‐time feedback to an operator under restricted bandwidth using layered user interfaces. Our strategy for humanoid locomotion includes a backward‐facing knee configuration paired with specialized toe and heel lifting strategies that allow the robot to traverse difficult surfaces while rejecting external perturbations. With an upper body planner that encodes operator preferences, predictable motion plans are executed in unforeseen circumstances. These plans are critical for manipulation in unknown environments. Our approach was validated during the DRC Finals competition, where Team THOR scored three points in 18 min of operation time, and the results are presented along with an analysis of each task.     

Adaptive continuous‐space informative path planning for online environmental monitoring

Autonomous mobile robots are increasingly employed to take measurements for environmental monitoring, but planning informative, measurement‐rich paths through large three‐dimensional environments is still challenging. Designing such paths, known as the informative path planning (IPP) problem, has been shown to be NP‐hard. Existing algorithms focus on providing guarantees on suboptimal solutions, but do not scale well to large problems. In this paper, we introduce a novel IPP algorithm that uses an evolutionary strategy to optimize a parameterized path in continuous space, which is subject to various constraints regarding path budgets and motion capabilities of an autonomous mobile robot. Moreover, we introduce a replanning scheme to adapt the planned paths according to the measurements taken in situ during data collection. When compared to two state‐of‐the‐art solutions, our method provides competitive results at significantly lower computation times and memory requirements. The proposed replanning scheme enables to build models with up to 25% lower uncertainty within an initially unknown area of interest. Besides presenting theoretical results, we tailored the proposed algorithms for data collection using an autonomous surface vessel for an ecological study, during which the method was validated through three field deployments on Lake Zurich, Switzerland. Spatiotemporal variations are shown over a period of three months and in an area of 350 m × 350 m × 13 m. Whereas our theoretical solution can be applied to multiple applications, our field results specifically highlight the effectiveness of our planner for monitoring toxic microorganisms in a pre‐alpine lake, and for identifying hot‐spots within their distribution.     

Direct Virtual-hand Interface in Robot Assembly Programming

For a long time, robot assembly programming has been produced in two environments: on-line and off-line. On-line robot programming uses the actual robot for the experiments performing a given task; off-line robot programming develops a robot program in either an autonomous system with a high-level task planner and simulation or a 2D graphical user interface linked to other system components. This paper presents a whole hand interface for more easily performing robotic assembly tasks in the virtual tenvironment. The interface is composed of both static hand shapes (states) and continuous hand motions (modes). Hand shapes are recognized as discrete states that trigger the control signals and commands, and hand motions are mapped to the movements of a selected instance in real-time assembly. Hand postures are also used for specifying the alignment constraints and axis mapping of the hand-part coordinates. The basic virtual-hand functions are constructed through the states and modes developing the robotic assembly program. The assembling motion of the object is guided by the user immersed in the environment to a path such that no collisions will occur. The fine motion in controlling the contact and ending position/orientation is handled automatically by the system using prior knowledge of the parts and assembly reasoning. One assembly programming case using this interface is described in detail in the paper.     

Control Architecture for Human–Robot Integration: Application to a Robotic Wheelchair

Completely autonomous performance of a mobile robot within noncontrolled and dynamic environments is not possible yet due to different reasons including environment uncertainty, sensor/software robustness, limited robotic abilities, etc. But in assistant applications in which a human is always present, she/he can make up for the lack of robot autonomy by helping it when needed. In this paper, the authors propose human–robot integration as a mechanism to augment/improve the robot autonomy in daily scenarios. Through the human–robot-integration concept, the authors take a further step in the typical human–robot relation, since they consider her/him as a constituent part of the human–robot system, which takes full advantage of the sum of their abilities. In order to materialize this human integration into the system, they present a control architecture, called architecture for human–robot integration, which enables her/him from a high decisional level, i.e., deliberating a plan, to a physical low level, i.e., opening a door. The presented control architecture has been implemented to test the human–robot integration on a real robotic application. In particular, several real experiences have been conducted on a robotic wheelchair aimed to provide mobility to elderly people.