TAMS: development of a multipurpose three-arm aerial manipulator system

This paper describes the concept, design and realization of a novel manipulator system for a multirotor aerial robot. The proposed manipulator system consists of three robotic arms attached to a multirotor frame. To overcome the limited payload capabilities in multirotor aerial platforms, the same set of on-board manipulators are used to realize various tasks, primarily the manipulation or grasping of objects of various sizes. Furthermore, they can provide aid in complex navigation tasks by doing physical, contact-based obstacle avoidance and are able to act as adaptive landing gear in uneven terrain. The design requirements and the analysis to realize these tasks is discussed. The proposal is supported with the successful demonstration of these tasks using the aerial multi manipulator system in an outdoor environment.     

A Study on Development of a Hybrid Aerial/Terrestrial Robot System for Avoiding Ground Obstacles by Flight

To date, many studies related to robots have been performed around the world. Many of these studies have assumed operation at locations where entry is difficult, such as disaster sites, and have focused on various terrestrial robots, such as snake-like, humanoid, spider-type, and wheeled units. Another area of active research in recent years has been aerial robots with small helicopters for operation indoors and outdoors. However, less research has been performed on robots that operate both on the ground and in the air. Accordingly, in this paper, we propose a hybrid aerial/terrestrial robot system. The proposed robot system was developed by equipping a quadcopter with a mechanism for ground movement. It does not use power dedicated to ground movement, and instead uses the flight mechanism of the quadcopter to achieve ground movement as well. Furthermore, we addressed the issue of obstacle avoidance as part of studies on autonomous control. Thus, we found that autonomous control of ground movement and flight was possible for the hybrid aerial/terrestrial robot system, as was autonomous obstacle avoidance by flight when an obstacle appeared during ground movement.      

Robot-Assisted Bridge Inspection

The Center for Robot-Assisted Search and Rescue (CRASAR®) deployed a customized AEOS man-portable unmanned surface vehicle and two commercially available underwater vehicles (the autonomous YSI EcoMapper and the tethered VideoRay) for inspection of the Rollover Pass bridge in the Bolivar peninsula of Texas in the aftermath of Hurricane Ike. A preliminary domain analysis with the vehicles identified key tasks in subsurface bridge inspection (mapping of the debris field and inspecting the bridge footings for scour), control challenges (navigation under loss of GPS, underwater obstacle avoidance, and stable positioning in high currents without GPS), possible improvements to human-robot interaction (having additional display units so that mission specialists can view and operate on imagery independently of the operator control unit, incorporating 2-way audio to allow operator and field personnel to communicate while launching or recovering the vehicle, and increased state sensing for reliability), and discussed the cooperative use of surface, underwater, and aerial vehicles. The article posits seven milestones in the development of a fully functional UMV for bridge inspection: standardize mission payloads, add health monitoring, improve teleoperation through better human-robot interaction, add 3D obstacle avoidance, improve station-keeping, handle large data sets, and support cooperative sensing.     

面向斜拉桥索塔巡检的旋翼UAV避障航迹规划

针对斜拉桥索塔巡检的旋翼UAV避障航迹规划问题,提出了一种面向斜拉桥索塔巡检的旋翼UAV避障航迹规划方法。该方法以巡检过程中旋翼UAV的能量消耗为航迹优劣评价指标,利用基于信息熵理论改进后的遗传算法获取能量消耗最少航迹,并提出双圆弧避障策略对航迹上存在斜拉索障碍的局部区域进行航迹重新规划,使之能有效地避让斜拉索障碍,保障旋翼UAV的飞行安全。以咸阳渭城桥索塔的外观巡检为例进行仿真验证,仿真结果表明,所提方法规划的航迹有效地降低了巡检旋翼UAV的能量消耗,确保了巡检旋翼UAV的飞行安全,能够较好地适用于斜拉桥索塔外观巡检。     

悬挂伸缩刀具的树障清理空中机器人飞行控制方法

针对输电线路附近的树障进行清理问题,本文提出了一种新型的悬挂伸缩刀具的树障清理空中机器人并进行了仿真和实物验证.首先,对悬挂伸缩刀具的空中机器人进行了伸缩刀具重心变化下的动力学、运动学建模及接触建模.其次,为避免空中机器人接触作业时机器人倾翻的问题,设计了力估计器用于力感知和导纳控制器用于力控制.针对空中机器人非线性强耦合、伸缩刀具时参数摄动及作业时扰动的问题,设计了线性自抗扰控制(LADRC)的机器人位姿控制器.再次,数值仿真验证了导纳控制能有效避免空中机器人接触作业时产生倾翻的问题,以及基于LADRC控制器的位姿控制具有良好的稳定性和抗扰性.最后,通过实物飞行和接触作业测试,进一步验证了本文悬挂伸缩刀具的树障清理空中机器人及其控制方法的有效性.     

Experiments in contact control

This article describes the implementation, experimentation, and application of contact control schemes for a 7-DOF Robotics Research arm. The contact forces and torques are measured in the sensor frame by the 6-axis force/torque sensor mounted at the wrist, are compensated for gravity, and then are transformed to the tool frame in which the contact task is defined and executed. The contact control schemes are implemented on the existing robot Cartesian position control system at 400Hz, do not require force rate information, and are extremely simple and computationally fast. Three types of contact control schemes are presented: compliance control, force control, and dual-mode control. In the compliance control scheme, the contact force is fed back through a lag-plus-feedforward compliance controller so that the end-effector behaves like a spring with adjustable stiffness; thus the contact force can be controlled by the reference position command. In the force control scheme, a force setpoint is used as the command input and a proportional-plus-integral force controller is employed to ensure that the contact force tracks the force setpoint accurately. In the dual-mode control scheme, the end-effector approaches and impacts the reaction surface in compliance mode, and the control scheme is then switched automatically to force mode after the initial contact has been established. Experimental results are presented to demonstrate contact with hard and soft surfaces under the three proposed control schemes. The article is concluded with the application of the proposed schemes to perform a contact-based eddy-current inspection task. In this task, the robot first approaches the inspection surface in compliance control until it feels that it has touched the surface, and then automatically levels the end-effector on the surface. The robot control system then transitions to force control and applies the desired force on the surface while executing a scanning motion. At the completion of the inspection task, the robot first relaxes the applied force and then retracts from the surface. © 1996 John Wiley & Sons, Inc.     

基于双旋Lyapunov矢量场的无人机避障算法

提出一种基于双旋Lyapunov矢量场的无人机避障算法.首先,建立无人机和障碍物的模型,并根据无人机有限时间是否会侵犯障碍物安全圆设计避障判定规则;然后,基于最小侧向偏移量原则选定避障机动中无人机速度旋转方向为最优避障方向,选定其反方向为矢量场旋转方向,定义成功避障的标准并进行证明;最后,通过建立的障碍物合并规则提升避障效率,使得上述方法适用于未知环境下的无人机在线避障.仿真结果表明,在无人机性能约束下,所提出的算法对动态和静态障碍都能有效避障,算法性能优于Dubins路径和人工势场法.     

用于避障研究的微型仿生机器鱼3维仿真系统

基于VC++6.0开发环境和OpenGL（open graphics library）国际图形标准,在Windows系统下开发了微型仿生机器鱼3维仿真系统。该系统可以降低用实体机器鱼进行机器鱼避障能力研究的成本和减少在研究过程中对实体机器鱼造成的损害。采用多边形建模的方法构建了虚拟微型仿生机器鱼模型,模拟了鱼类尾鳍的摆动。提出了一种模拟红外传感器探测障碍物的虚拟射线方法。并采用实时模糊决策算法设计了基于多传感器信息的复合模糊控制器,决策微型仿生机器鱼的避障行为。仿真实验表明,复合模糊控制器实时性好、效率高;无论是单个任意形状的障碍物还是多个连续障碍物,复合模糊控制器都能有效地引导仿生机器鱼避开障碍物,到达目标点。微型仿生机器鱼3维仿真系统为研究仿生机器鱼的自主避障能力提供了可靠、逼真、便利的平台。     

基于多传感器的无人机避障方法研究及应用

随着无人机巡检作业方式应用越来越广泛,巡检过程中对障碍物检测并进行避障显得愈发关键。若无人机碰到杆塔或线路不仅会造成无人机自身的损坏,还会对居民用电造成影响,给检修带来麻烦。毫米波雷达、激光雷达、双目视觉传感器在机器人避障中有广泛应用。但是基于输电线路巡检的多旋翼无人机的实际情况,传感器器件的选型、尺寸、重量,以及障碍物信息与飞控的融合,显得尤为重要。通过对多旋翼无人机搭载毫米波雷达、双目视觉传感器、差分GPS进行了研究,采用多传感器融合方法检测障碍物,利用虚拟力场法(VFF)进行航迹重规划,并实际飞行验证。测试表明该方法对杆塔避障取得了较好的应用效果。     

Robot path planning in a constrained workspace by using optimal control techniques

Robot manipulators are programmable mechanical systems designed to execute a great variety of tasks in a repetitive way. In industrial environment, while productivity increases, cost reduction associated with robotic operation and maintenance can be obtained as a result of decreasing the values of dynamic quantities such as torque and jerk, with respect to a specific task. Furthermore, this procedure allows the execution of various tasks that require maximum system performance. By including obstacle avoidance ability to the robot skills, it is possible to improve the robot versatility, i.e., the robot can be used in a variety of operating conditions. In the present contribution, a study concerning the dynamic characteristics of serial robot manipulators is presented. An optimization strategy that considers the obstacle avoidance ability together with the dynamic performance associated with the movement of the robot is proposed. It results an optimal path planning strategy for a serial manipulator over time varying constraints in the robot workspace. This is achieved by using multicriteria optimization methods and optimal control techniques. Numerical simulation results illustrate the interest of the proposed methodology and the present techniques can be useful for the design of robot controllers. Commemorative contribution.     

基于凸面体圆弧航路的无人机自主避障算法

为解决无人机飞行过程中障碍物规避问题,提出一种新的三维自主避障算法.首先,根据障碍物的若干信息利用标准凸面体对不规则障碍物进行数学建模,用一个或多个标准凸面体覆盖障碍物整体或关键部分;然后,根据障碍物模型设计圆弧规避航路算法,将避障问题转化为跟踪规避航路控制问题,并定义避障判定、避障方向判断和成功避障规则;最后,结合非线性制导律和高度通道控制,实现无人机实时三维航路跟踪与自主避障.非线性数值仿真结果表明,避障算法能够有效地规避障碍物且三维航路跟踪精度好,能够应用于无人机的避障飞行任务.     

Hybrid predictive control of a coaxial aerial robot for physical interaction through contact

This paper deals with the control of an unmanned coaxial helicopter designed towards active physical interaction with its environment. The system design is tailored to robust interaction through contact (e.g. docking and sliding on walls). Due to the rapid change of the dynamics from free-flight to the helicopter subject to the contact forces, a hybrid model is developed. This model captures all modes of the vehicle dynamics and is the basis for the design of a hybrid model predictive control strategy that ensures the stability of the hybrid dynamics and provides optimal maneuvering, docking on walls as well as sliding on them. The proposed control law is evaluated through experimental studies.     

Vision‐based navigation through urban canyons

We address the problem of navigating unmanned vehicles safely through urban canyons in two dimensions using only vision‐based techniques. Two commonly used vision‐based obstacle avoidance techniques (namely stereo vision and optic flow) are implemented on an aerial and a ground‐based robotic platform and evaluated for urban canyon navigation. Optic flow is evaluated for its ability to produce a centering response between obstacles, and stereo vision is evaluated for detecting obstacles to the front. We also evaluate a combination of these two techniques, which allows a vehicle to detect obstacles to the front while remaining centered between obstacles to the side. Through experiments on an unmanned ground vehicle and in simulation, this combination is shown to be beneficial for navigating urban canyons, including T‐junctions and 90‐deg bends. Experiments on a rotorcraft unmanned aerial vehicle, which was constrained to two‐dimensional flight, demonstrate that stereo vision allowed it to detect an obstacle to the front, and optic flow allowed it to turn away from obstacles to the side. We discuss the theory behind these techniques, our experience in implementing them on the robotic platforms, and their suitability to the urban canyon navigation problem. © 2009 Wiley Periodicals, Inc.     

Dynamic Data Driven Application System for Plume Estimation Using UAVs

In this article, a full dynamic data-driven application system (DDDAS) is proposed for dynamically estimating a concentration plume and planning optimal paths for unmanned aerial vehicles (UAVs) equipped with environmental sensors. The proposed DDDAS dynamically incorporates measured data from UAVs into an environmental simulation while simultaneously steering measurement processes. In order to assimilate incomplete and noisy state observations into this system in real-time, the proper orthogonal decomposition (POD) is used to estimate the plume concentration by matching partial observations with pre-computed dominant modes in a least-square sense. In order to maximize the information gain, UAVs are dynamically driven to hot spots chosen based on the POD modes. Smoothed particle hydrodynamics (SPH) techniques are used for UAV guidance with collision and obstacle avoidance. We demonstrate the efficacy of the data assimilation and control strategies in numerical simulations. Especially, a single UAV outperforms the ten static sensors in this scenario in terms of the mean square error over the full time interval. Additionally, the multi-vehicle data collection scenarios outperform the single vehicle scenarios for both static sensors at optimal positions and UAVs controlled by SPH.     

Adaptive compliant force–motion control of coordinated non-holonomic mobile manipulators interacting with unknown non-rigid environments

Most studies on the coordination control of multiple mobile manipulators system assume exact knowledge of system dynamics and deal only with motion control. However, actual applications may involve the tasks in which multiple coordinated mobile manipulators system interacts with rigid or non-rigid working surfaces. In this paper, we consider multiple mobile manipulators grasping a rigid object in contact with a deformable working surface, whose geometric and real physical parameters are unknown but boundedness of physical parameters is known. The contact forces are nonlinear and difficult to model. A neuro-adaptive control for coordinated mobile manipulators is proposed for robust force/motion tracking. The control law is based on the philosophy of the parallel approach, in which the control problem is divided into three subspaces and the adaptive techniques are employed to deal with the uncertain environmental constraints, disturbances, and unknown robotic and object dynamics. The proposed adaptive force–motion controller guarantees the tracking errors of motion and force trajectories converge to zero. Simulation examples are presented to verify the effectiveness of the proposed control.     

基于改进模糊算法的水面无人艇自主避障

为了提高水面无人艇（USV）在未知、复杂环境下的连续避障性能,提出一种具有速度反馈的模糊避障算法。USV利用激光扫描雷达与多路超声波传感器感知周围环境,通过对障碍物信息进行分组并设置权值的方式进行多传感器数据融合,并在模糊控制的基础上根据环境情况自动调整航速;进而提出一种考虑障碍物所有分布情况的更全面的模糊控制规则表,增强了USV对复杂环境的适应能力。实验结果表明,所提方法能通过与环境交互调整USV航速使其成功避障并优化避障路径,具有良好的可行性和有效性。     

基于深度学习的巡检机器人避障轨迹自动控制系统设计

为了提高巡检机器人在复杂环境下的避障能力,使机器人能够安全地完成巡检任务,设计基于深度学习的巡检机器人避障轨迹自动控制系统。设计由CCD传感器、信号处理芯片等设备组成的工业智能视觉CCD相机,基于FPGA和USB2.0的视频采集卡传输采集数据,完成硬件部分的设计。在软件设计中,对采集的图像实施目标分割、双目目标匹配等预处理,通过对摄像头实施双目视觉标定获取障碍物空间位置三维信息,基于深度学习中的CRNN设计机器人自主避障规划网络模型,并设计模糊轨迹控制器,实现避障中的轨迹自动控制。系统测试结果表明,设计系统最终成功避开了三个动态障碍物,最大轨迹控制误差的最大值为1.45°,最小轨迹控制误差的最大值为0.62°,动态避障巡检速度始终在3.5m/s左右,表现出了精准而稳定的轨迹控制效果。     

A Prototype of Ducted-Fan Aerial Robot with Redundant Control Surfaces

This work presents the aero-mechanical characteristics and the control design for a prototype of ducted-fan aerial robot tailored to achieve advanced robotics operations requiring physical interaction with the environment and high maneuverability. The distinguishing feature of the proposed aerial configuration is the redundant number of aerodynamic surfaces which can be employed by the controller. A control strategy is then proposed in which control allocation techniques exploit this redundancy to improve the accuracy and the efficiency of the aerodynamic forces and torques generation mechanism while simplifying the overall feedback design. The effectiveness of the proposed approach and the performances of the ducted-fan prototype have been demonstrated by means of flight experiments.     

Collision-free formation generation and tracking of unmanned aerial vehicles based on physicomimetics approach

This paper investigates the problem of collision-free leader–follower formation generation and tracking of multiple fixed-wing unmanned aerial vehicles (UAVs). A group of UAVs, described by unicycle-type models subject to velocity constraints, are required to form a desired formation, while tracking a virtual leader and achieving collision-free flight. To handle this problem, a novel control law based on physicomimetics approach is proposed, which integrates the formation generation, formation tracking, and collision avoidance together. Physical forces are imitated to design artificial forces used in control laws that drive multiple UAVs to accomplish desired collaborative behaviors. Further, the virtual repulsion is embedded in the physicomimetics-based control scheme to achieve obstacle avoidance naturally. The artificial forces have similar meaning to the physical forces because of similar forms, which facilitates the design and adjustment of the control strategy. Specially, to deal with the speed constraints of fixed-wing UAVs, a saturation function is applied to modify the control laws and the stability is proved theoretically. Finally, numerical simulations and hardware-in-the-loop experiments are provided to verify the effectiveness of the proposed control scheme.     

Hierarchical control of cooperative nonlinear dynamical systems

Cooperative control methods that are scalable with low computational cost are crucial for networked dynamical systems to respond quickly in unknown or cluttered environments. In an attempt to make the problem tractable, many existing cooperative controls are designed with oversimplified assumptions and/or without the capabilities of rapidly handling different environmental and dynamical constraints. In this article, proposed is a two-level hierarchical, cooperative control framework using a divide-and-conquer strategy so that challenges can be separately handled at different levels. It is scalable and has low computational cost. Based on a simplified homogeneous double-integrator dynamic model, the top-level planner first computes cooperative trajectories satisfying obstacle avoidance requirements. Then at the lower level, state and control constraints, nonlinear dynamics and self-collision/obstacle avoidance as related to the real system are addressed through a bio-inspired fast trajectory planning algorithm. The stability of the overall hierarchical structure is proven. Two examples, a differential-drive ground vehicle formation control and an unmanned aerial vehicle formation flight, are used to illustrate the advantages of the proposed hierarchical framework.