CaRINA Intelligent Robotic Car: Architectural design and applications

This paper presents the development of two outdoor intelligent vehicles platforms named CaRINA I and CaRINA II, their system architecture, simulation tools, and control modules. It also describes the development of the intelligent control system modules allowing the mobile robots and vehicles to navigate autonomously in controlled urban environments. Research work has been carried out on tele-operation, driver assistance systems, and autonomous navigation using the vehicles as platforms to experiments and validation. Our robotic platforms include mechanical adaptations and the development of an embedded software architecture. This paper addresses the design, sensing, decision making, and acting infrastructure and several experimental tests that have been carried out to evaluate both platforms and proposed algorithms. The main contributions of this work is the proposed architecture, that is modular and flexible, allowing it to be instantiated into different robotic platforms and applications. The communication and security aspects are also investigated.     

基于深度强化学习的AGV智能导航系统设计

针对现有的AGV在大规模未知复杂环境中进行自主导航配送的问题,基于深度强化学习完成了AGV智能导航系统设计。首先,结合传感器对周围的障碍物进行探测感知,利用DDPG（deep deterministic policy gradient）算法实现AGV小车从环境的感知输入到动作的直接输出控制,帮助AGV完成自主导航和避障任务。此外,针对训练样本易受环境干扰的问题,提出了一种新颖的DL（disturb learning）- DDPG算法,通过对学习样本中相关数据进行高斯噪声预处理,帮助智能体适应噪声状态下的训练环境,提升了AGV在真实环境中的鲁棒性。仿真实验表明,经改进后的DL-DDPG 算法能够为AGV导航系统提供更高效的在线决策能力,使AGV小车完成自主导航与智能控制。     

一种基于模糊控制的智能车转向控制算法研究

针对飞思卡尔电磁车模型,探讨了从交变电流产生的电磁场中进行路径检测的问题,提出了一种基于模糊控制的舵机转向控制算法,目的是提高智能车通过复杂电磁赛道特别是S弯道时的稳定性,并进行了仿真与试验验证。结果表明,设计的电路能很好的进行路径检测,提出的转向控制算法能使小车的舵机输出以较高精度跟随期望输入信号,在复杂赛道上得到较为平稳的转向控制。     

A 3D simulator for autonomous robotic fish

This paper presents a 3D simulator used for studying the motion control and autonomous navigation of robotic fish. The simulator's system structure and computation flow are presented. Simplified kinematics and hydrodynamics models for a virtual robotic fish are proposed. Many other object models are created for water, obstacles, sonar sensors and a swimming pool. Experimental results show that the simulator provides a, realistic and convenient way to develop autonomous navigation algorithms for robotic fish.     

OTTER: The design and development of an intelligent underwater robot

Recent advances in sensing and intelligent control technologies open a whole new dimension in underwater autonomy. However, before truly-capable, autonomous underwater robots can be created for subsea intervention and exploration, many research issues must be first investigated and developed experimentally on testbed platforms.OTTER is an underwater robot designed to be used as a testbed for autonomous technologies. Both OTTER's hardware and software systems are configured to support simultaneous development and testing of different concepts for underwater robotic by independent researchers. A general control-software framework enables common access to all subsystems and avoids the duplication of basic robotic functionality jointly required by all projects. Additionally, the new autonomous technologies enabled by the results of individual research are mutually compatible and can be easily integrated into a single robotic system. Examples of new technologies demonstrated on the OTTER underwater robot include control from a real-time vision-sensing system, coordinated arm/vehicle control, and control from 3D graphical user interfaces.     

多传感器数据融合的智能电动车自主导航

以四轮驱动电动车为研究对象,将智能电动车上多种传感器所采集的信息进行处理与融合,实现校园环境里的自主行驶与导航.提出了在空旷路段和沿墙导航的控制决策,着重研究了沿墙算法和入弯算法,使智能电动车在不同的环境下可以有效地完成自主导航.实验结果表明:该导航控制策略和算法具有较高的可靠性.     

Learned Action SLAM: Sharing SLAM through learned path planning information between heterogeneous robotic platforms

Currently when path planning is used in SLAM it is to benefit SLAM only, with no mutual benefit for path planning. Furthermore, SLAM algorithms are generally implemented and modified for individual heterogeneous robotic platforms without autonomous means of sharing navigation information. This limits the ability for robot platforms to share navigation information and can require heterogeneous robot platforms to generate individual maps within the same environment. This paper introduces Learned Action SLAM, which for the first time autonomously combines path-planning with SLAM such that heterogeneous robots can share learnt knowledge through Learning Classifier Systems (LCS). This is in contrast to Active SLAM, where path-planning is used to benefit SLAM only. Results from testing LA-SLAM on robots in the real world have shown; promise for use on teams of robots with various sensor morphologies, implications for scaling to associated domains, and ability to share maps taken from less capable to more advanced robots.     

Dynamic omnidirectional vision for mobile robots

Mobile robotic devices hold great promise for a variety of applications in industry. A key step in the design of a mobile robot is to determine the navigation method for mobility control. The purpose of this paper is to describe a new algorithm for omnidirectional vision navigation. A prototype omnidirectional vision system and the implementation of the navigation techniques using this modern sensor and an advanced automatic image processor is described. The significance of this work is in the development of a new and novel approach—dynamic omnidirectional vision for mobile robots and autonomous guided vehicles.     

A multirobot system for autonomous deployment and recovery of a blade crawler for operations and maintenance of offshore wind turbine blades

Offshore wind farms will play a vital role in the global ambition of net zero energy generation. Future offshore wind farms will be larger and further from the coast, meaning that traditional human-based operations and maintenance approaches will become infeasible due to safety, cost, and skills shortages. The use of remotely operated or autonomous robotic assistants to undertake these activities provides an attractive alternative solution. This paper presents an autonomous multirobot system which is able to transport, deploy and retrieve a wind turbine blade inspection robot using an unmanned aerial vehicle (UAV). The proposed solution is a fully autonomous system including a robot deployment interface for deployment, a mechatronic link-hook module (LHM) for retrieval, both installed on the underside of a UAV, a mechatronic on-load attaching module installed on the robotic payload and an intelligent global mission planner. The LHM is integrated with a 2-DOF hinge that can operate either passively or actively to reduce the swing motion of a slung load by approximately 30%. The mechatronic modules can be coupled and decoupled by special maneuvers of the UAV, and the intelligent global mission planner coordinates the operations of the UAV and the mechatronic modules for synchronous and seamless actions. For navigation in the vicinity of wind turbine blades, a visual-based localization merged with the location knowledge from Global Navigation Satellite System has been developed. A proof-of-concept system was field tested on a full-size decommissioned wind-turbine blade. The results show that the experimental system is able to deploy and retrieve a robotic payload onto and from a wind turbine blade safely and robustly without the need for human intervention. The vicinity localization and navigation system have shown an accuracy of 0.65 and 0.44 m in the horizontal and vertical directions, respectively. Furthermore, this study shows the feasibility of systems toward autonomous inspection and maintenance of offshore windfarms.     

Reinforcement learning in robotic applications: a comprehensive survey

In recent trends, artificial intelligence (AI) is used for the creation of complex automated control systems. Still, researchers are trying to make a completely autonomous system that resembles human beings. Researchers working in AI think that there is a strong connection present between the learning pattern of human and AI. They have analyzed that machine learning (ML) algorithms can effectively make self-learning systems. ML algorithms are a sub-field of AI in which reinforcement learning (RL) is the only available methodology that resembles the learning mechanism of the human brain. Therefore, RL must take a key role in the creation of autonomous robotic systems. In recent years, RL has been applied on many platforms of the robotic systems like an air-based, under-water, land-based, etc., and got a lot of success in solving complex tasks. In this paper, a brief overview of the application of reinforcement algorithms in robotic science is presented. This survey offered a comprehensive review based on segments as (1) development of RL (2) types of RL algorithm like; Actor-Critic, DeepRL, multi-agent RL and Human-centered algorithm (3) various applications of RL in robotics based on their usage platforms such as land-based, water-based and air-based, (4) RL algorithms/mechanism used in robotic applications. Finally, an open discussion is provided that potentially raises a range of future research directions in robotics. The objective of this survey is to present a guidance point for future research in a more meaningful direction.

     

Editorial of special issue on unmanned aerial vehicles (UAVs)

UAVs have witnessed unprecedented levels of growth during the last decade. Projections and predictions suggest that during the next 5-10 years growth will continue to rapidly increase, while the spectrum of UAV utilization will be dominated by civil and public domain applications, ranging from search and rescue, emergency response, disaster management, infrastructure monitoring and protection, precision agriculture, surveillance and reconnaissance, cartography, etc. This special issue on UAVs consists of six invited and peer reviewed papers. The main focus of the issue is on multi-UAV teams, a research area that has attracted attention due to the fact that a team of unmanned vehicles may accomplish, collectively, tasks that may be difficult or impossible by a single UAV to complete. The first four papers focus on: Flocking control of a fleet of UAVs; distributed output feedback stationary consensus of multi-vehicle systems in unknown environments; consensus controller for multi-UAV navigation; and ranging-aided relative navigation of multi-platforms. Collectively, these four papers offer insight to the state of the art in this important topic. The fifth paper on nonlinear robust control of a quadrotor helicopter with finite time convergence addresses challenges related to UAV navigation/control, while the last paper on experimental evaluation of a real-time GPU-based pose estimation system for autonomous landing of rotary wing UAVs introduces a comprehensive methodology that is suitable for real-time autonomous takeoff and landing from stationary and moving ground platforms.     

A potential field approach for reactive navigation of autonomous sailboats

Navigation techniques for autonomous sailboats are faced with two inherent difficulties. The uncontrollable and partially unpredictable nature of thrust forces on one hand and the complex kinematics of sailboats on the other hand. This paper proposes a new reactive navigation approach, based on artificial potential fields, that addresses these two problems simultaneously. Environment and specific sailboat navigation constraints are represented by a local potential built around the vehicle location. Changes of wind direction and detected obstacles affect this periodically updated potential, which guarantees the real-time computation of a feasible heading for the boat. Numerical simulations are presented and validate the proposed algorithm under various wind conditions. Field trials eventually illustrate the efficiency of this navigation technique using a reduced-scale autonomous sailboat prototype.     

Intelligent Control Algorithms for Robotic-Assisted Beating Heart Surgery

This paper focuses on the development of control algorithms for intelligent robotic tools that assist off-pump coronary artery bypass graft (CABG) surgery. In the robotic-assisted CABG surgery, the surgeon operates on the beating heart using intelligent robotic instruments. Robotic tools actively cancel the relative motion between the surgical instruments and the point of interest on the beating heart, dynamically stabilizing the heart for the operation. This algorithm is called active relative motion canceling (ARMC). Here, a model-based intelligent ARMC algorithm employing biological signals, such as electrocardiogram, to achieve effective motion cancellation is proposed. Finally, experimental results of the algorithm on a 3-degree-of-freedom robotic test-bed system are reported.     

Bisimulation conversion and verification procedure for goal-based control systems

Fault tolerance and safety verification of control systems are essential for the success of autonomous robotic systems. A control architecture called Mission Data System (MDS), developed at the Jet Propulsion Laboratory, addresses these needs with a goal-based control approach. In this paper, a software algorithm for converting goal network control systems into linear hybrid systems is described. The conversion process is a bisimulation; the resulting linear hybrid system can be verified for safety in the presence of failures using existing symbolic model checkers, and thus the original goal network is verified. A moderately complex example goal network control system is converted to a linear hybrid system using the automatic conversion software that is based on the bisimulation and then is verified.     

The AutoSOAR autonomous soaring aircraft,part 1: Autonomy algorithms

Autonomous soaring has the potential to greatly improve both the range and endurance of small robotic aircraft. This paper describes an autonomous soaring system that generates a dynamic map of lift sources (thermals) in the environment and uses this map for online flight planning and decision making. Components of the autonomy algorithm include thermal mapping, explore/exploit decision making, navigation, optimal airspeed computation, thermal centering control, and energy state estimation. A finite state machine manages the aircraft behavior during flight and determines when changing behavior is appropriate. A complete system to enable autonomous soaring is described with special attention paid to practical considerations encountered during flight testing. A companion paper describes the hardware implementation of this system and the results of a flight test campaign conducted at Aberdeen Proving Ground in September 2015.     

自主轮式机器人THMR－V的混合模糊逻辑控制

轮式机器人的控制问题是控制研究的关键问题之一，对高速自主导航的轮式机器人，控制器的实时性、精确性和鲁棒性要求很高．在本文中，根据PID控制和模糊逻辑控制的各自优点，将传统的PID控制与模糊逻辑控制结合起来，提出了一种混合模糊逻辑控制算法. 经实验检验，该算法具有很高的实时性、控制精度和鲁棒性，能够满足机器人高速自主导航的需要．     

Robotic Airships for Exploration of Planetary Bodies with an Atmosphere: Autonomy Challenges

Robotic unmanned aerial vehicles have great potential as surveying and instrument deployment platforms in the exploration of planets and moons with an atmosphere. Among the various types of planetary aerovehicles proposed, lighter-than-atmosphere (LTA) systems are of particular interest because of their extended mission duration and long traverse capabilities. In this paper, we argue that the unique characteristics of robotic airships make them ideal candidates for exploration of planetary bodies with an atmosphere. Robotic airships extend the capabilities of balloons through their flight controllability, allowing (1) precise flight path execution for surveying purposes, (2) long-range as well as close-up ground observations, (3) station-keeping for long-term monitoring of high science value sites, (4) transportation and deployment of scientific instruments and in situ laboratory facilities across vast distances, and (5) opportunistic flight path replanning in response to the detection of relevant sensor signatures. Implementation of these capabilities requires achieving a high degree of vehicle autonomy across a broad spectrum of operational scenarios. The paper outlines some of the core autonomy technologies required to implement the capabilities listed above, drawing on work and results obtained in the context of AURORA (Autonomous Unmanned Remote Monitoring Robotic Airship), a research effort that focuses on the development of the technologies required for substantially autonomous robotic airships. We discuss airship modeling and control, autonomous navigation, and sensor-based flight control. We also outline an approach to airborne perception and monitoring which includes mission-specific target acquisition, discrimination and identification, and present experimental results obtained with AURORA.     

自主移动机器人智能导航研究进展

本文对当前在自主移动机器人智能导航研究中已被采用并取得的研究方法进行了１，并根据已取得的成果预测了移动机器人智能导航研究的发展趋势，指出视觉导航和传感器融合将是移动机器人智能导航的主要发展方向。