控制、引导和导航的综述文献一种视觉导航的里程碑文献，描述了场景匹配的评价方式。Kendoul: Su

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详细说明：一种视觉导航的里程碑文献，描述了场景匹配的评价方式。Kendoul: Survey of advances in GNC of ruAs 317 for launch and recovery. These three categories are domi- Finally, discussions and conclusions about published pa- nated by conventional helicopter configurations with a sin- pers and developed systems are given in Section 7 gle main rotor and a tail rotor. On the other hand,most mini and micro UAS of categories IV and V are multirotor 2. RESEARCH GROUPS INVOLVED IN RUAS platforms(e.g, quad rotors and coaxial vehicles)that can fly RESEARCH AND DEVELOPMENT outdoors as well as indoors and can be launched by hand or from small and narrow spaces A number of research groups are working on the develop ment of autonomy technologies for RUAS. Figures 2 lists 12,M。 otivation for and sc。 pe of This survey some 27 research groups that are involved in research and development of autonomous RUAS. The list is not exhaus Nonmilitary research in RUAs only began in the carly tive and excludes military and industrial research groups 1990s, and they have now become a popular research area Over the past 20 years, an enormous amount of research 3. AUTONOMY LEVELS FOR UNMANNED has gone into guidance, navigation, and control(GNC) for ROTORCRAFT SYSTEMS RUAS, resulting in veried techniques and a large number of published papers. Although some survey papers have tried Before review of recent advances in research and devel- to review small subsets of methods in a particular area- opment of autonomous RUAS, it is important to develop Ollero and merino(2004) for flight controllers, Chao, Cao a framework that provides standard definitions and met and Chen(2010) for autopilots, Goerzen, Kong, and mcttlo rics characterizing and measuring the autonomy level of (2010)for path planning algorithms, and Valavanis(2007) a RUAS. In this section the autonomy levels for un- for UAS in general--there is a real need for a comprehen manned rotorcraft systems(ALFURS) framework is pro- sive survey to report and organize the large variety of GNC Posed, which is based on the generic NIST ALFUS frame methods, providing a context for viewing and comparing work (Huang, Messina, Albus, 2007)with some mod autonomy technologies developed for RUAS. This work hica tions and extensions to make it specific to RUAs and was mainly motivated by the fact that we have not found research-oriented. Another of the main objectives of this a single survey specializing on GNC systems for UAS in ection is to identify the key components that constitute the general and RUAS in particular, despite the need for a such autonomy architecture onboard a RUAS. This section is in- work after two decades of active research in these arcas tended to facilitate the understanding of this survey paper, This paper provides an overview of GNC systems devel but it can also serve as a common reference for the uas oped to date to increase the autonomous capabilities of un- communuty manned rotorcraft systems. The approaches that have been reported are organized into three main categories: control, 31. Terminology and Key Definitions navigation, and guidance. For each category, methods are In this section, definitions of terms that are most relevant to grouped at the highest level based on the autonomy level they provide, and then according to the algorithmic ap RUAS autonomy are proposed. As such, consistency with the nist alFus (Huang 2008) definitions is assumed proach used, which in most cases is closely associated with the type of sensors used. The central objective of this sur- However, for certain terms, the NIST generic definitions have been modified to better suit RUAS. For some other vey paper is to serve the UAS research community by pro- terms, new definitions are proposed based on information iding an overview of the state of the art, major milestones, from various sources(ICAO, FAA, UAS Roadmap 2010 and unsolved problems in the areas of GNC for RUAS. This 2035, NIST, ctc) will help researchers to reduce reinvention and enable them to better identify the key critical gaps that prevent advance Definition 1. Rotorcraft: A heavier-thar-air aircrafts 5 that is in the field The rest of the paper is organized as follows: The main supported in flight by the dynmamic reaction of the air against its rescarch groups involved in rescarch and devclopment of power-driven rotors on a substantially vertical axi autonomous RUAS are presented in Section 2. Section 3 in troduces autonomy aspects onboard RUAS, including au Definition 2. Rotorcraft Unmanned Aerial Vehicle(ruav) A powered rotorcraft that does not require an onboard crew, can tonomy definition from the UAS Perspective, autonom levels and metrics, and the main components of a typical autonomous system. Sections 4, 5, and 6 provide a com- National Institute of Standards and Technology. prehensive survey of major works focusing on flight cor trol, autonomous navigation, and guidance, respectivel The Autonomy Levels For Unmanned Systems(ALFUS)Ad Hoc Workgroup is a NiST sponsored effort that aims at formulating a logical framework for characterizing the autonomy of unmanned The book (valavanis, 2007), published in 2007, also provided some systems in general, covering issues of definitions, metrics, levels of overview of recent advances in UAS, but it is more a concatenation autonomy, etc of contributed chapters from different groups than a survey See icao and faa definitions of aircraft Journal of field robotics doi 10.1002/ rob 318.Journal of Field robotics 2012 NAME OF THE GROUP ROTORCRAFT CURRENT RESEARCH MILESTONES AND MAJOR AND NSTITUTION PLATFORMS AREAS PROJECTS ACHIEVEMENTS Field Robotics Centre, CMU, Class 1: Eurocopter FC135-Obstacles detection and avoidanceI-Successful obstacle detection and CMU-FRC-US and the Boeing unmanned Using a 3D)LIDAR wwwfrc.ri.cmu.eduprojects/ Little Bird (ULB)helicopter Landing zone detection and safe voidance with RMAX helicopter www.frc.ri.cmu.educademoandR-50 ClassI1:YamahaRMAX landing using sweeping 2D LIDAR. automatic landing with full-scale Class Iv: Otto Quadrotor Visual localization and perception. helicopter UJAV Research Facility Class l[: Yamaha rmax Georgia Institute of Techno-land Sung Woo Eng Adaptive autopilot for 3D trajectory logy, UAVRF-Glcch-US Remo-H helicopter Rotorcraft control using adaptive |tracking and flight control techniques and neural networks http://controls.ae.gatech.edu/classIv:Getpsyducted Vision-based navigation Vision-based target tracking wikiuuvrf fan), GlQ(quadrotor)and Vision-based formation flight GrAma(co-axial The Nasa Army autono 3D navigation in urban environ-|- Mapping and path planning using a mous Rotorcraft Project Class I: Yamaha RMAX ments using vision and LIDAR pinning NASA-ARP-US hulp: //ii. arc. nasa gop/projects helicopter. Safe landing area detection Vision-based state estimation PALACE Successful SLad tests using stereo apex/projectaRe nhp Mission and path planning and LIDAR Mission planning for surveillance. AR Group, Berkeley Class i[: Yamaha rmax Flight control using MPC University, BEAR-US andr-50 helicopters No current research activities on Formation flight robotics.cccs. berkeley. cdu/be Class III: Elcctric Maxi- UAS(to our knowledge) Vision-bascd landin loker helicopters LIDAR-based obstacle avoiadnce UASTcch Lab, Linkopings Class II: Yamaha RMAx Vision-based landing and localiza- Vision-based landing and localization University: UASTech-SH helicopter Path planning www.ida.liu.se/divisions/uicsclassIv:Quadrotors Mission and path planning Mission planning and execution aiicssite/uustech Artificial Intelligence monitoring. The French Acrospace Labs Vision-based navigation(target Ground target tracking using vision (ONERA); ONERA-FR Class IL: Yamaha RMAX tracking and safe landing Safe landing area detection using helicopters Mission planning and decisional stereo visi httpaction.onera.fraccueil autonomy( ReSS.AC project Mission managcment UAS cooperation USL, Univ of South Florida: Class IL: Yamaha RMAX, -Flight control JSL-USF-US Class Ill: Burgen, Raptor -Vision-bascd navigation Automatic flight www.cse.usfedu/usl 90. Maxi Joker 2 Fault dctection and isolation Trafic data collection and analysis Kenzo Lab, Chiha University: Class IL: Sky Surveyor, QTW CHIBA-U-JP Class Ill: Hirobo SST-Eaglc Autopilots desi Automatic flight of diffcrent mec 2. tm.chiba -u.jpil-nonamni Class IV: Astech quadrotors. -Formation flight control otorcraft platforms Vision-based flight mec:2. tm.chiba-u ip/uav'main/Class V: Epson micro Flyin Vision-based state estimation Formation flight of two helicopters Robot (uFr) USW(ADFA, australia Class ll: Yamaha RMaX heli siteshrpliesearch/UAP ua html/ Class l: Hirobo Eagle Flight control Optic flow-based terrain following Optic flow-based navigation using the Yamaha RMAX helicopte Ihtp:/sei. nsw, ad/a. edu. wu iacme helicopter Automatic landing on a shi researchconsult flight/index. html Shenyang Institute of Class II: ServoHeli-120 Avionics development/ integration. -Waypoint navigation with automatic Automation(SIA), SIA-CN (120 kg). ScrvoHcli-40 Flight contre htp. /av.sia.cn/en/index php(40 kg) Path planning and UAS coopcration -Vision-based ground target tracking Gierman Aerospace Centre 3D mapping and path planning using Class Ill: ARTIS helicopter-Stcrco vision-bascd mapping and stereo vision http:/www.dlr:deift/en/desk(25kg) obstacle avoidance Vision-based flight through obstacle default. aspx/tabid Mission management gates l377/905rel3350′ ARCAA(CSIRO-QUT robust control of rotorcraft ARCAA-AU Beyond visual range(BvR)flights http:/www.arcaa.aero' Class III: Vario helicopters.- Dependable autonomous UAS Class IV: Quadrotors and BVR infrastructure inspection http:/research.ictcsiroauresearch/lalOctocopters Obstacle detection and path Mapping and obstacle avoidance Syautonomous-- planning using vision and LIDAR. using LIDAR and/or stereo vision lsystems field-roboties/field-robotic Acrospacc Systcms and Class lll: Voyager gisr UAS tlight control Control Lab. KAIST 260 helicopter -Vision-hased navigation Trajectory tracking and waypoint avigation ASCL-KR Class IV: Electric T-REX -Obstacle dctection and path planning Vision-based flight hilp: //ascl kaist. ac kr: 600 helicopter. National University of Class IlL: AF25B from Nonlincar control of ruas Automatic flight control Singapore, NUS-SGi Copterworks Inc: Raptor 90 3D indoor navigation Formation flight of a ruas with a http://vlab.ee.nus.edu.sg/classiv:trex-450heli Vision-based navigation virtual leader coaxial rotorcraft Vision-based indoor flight Figure 2. Re working on RUAS Journal of field robotics doi 10 1002/rob Kendoul: Survey of advances in GNC Of ruas 319 NAME OF THE GROUP ROTORCRAFT CURRENT RESEARCH MILESTONES AND AND INSTITUTION PLATFORMS areas PRojeCts MAJOR Achievements Computcr Vision Group Class lll: bergen industrial Vision-based pose estimation. -Vision-bascd statc estimation Universidad Politecnica de Twin and Rotomotion SR20 heli. -Object tracking and ground target tracking Madrid. CVG-ES Class Iv: Octocopter from 3D mapping using vision. Visual 3D SLam http://wwwvisionauavcom Mikrokopter: quadrotor Vision-based flight Visual servoing Robotics vision and control Cuntroloflas Cooperative detection of forest Group, University of Seville, Class Ill: Heliv and HERO Vision-based navigation and fire using two RUAS GRVC-ES helicopters forest fire detection -Visual odometry and slam Cooperative perception and -Cooperative faults dctection control of multiple UAS The Center for Advanced fleet of heterogeneous UASs -Automatic flight control Aerospace Technologies (about 6 fixed-wing UASs, 6|-Navigation and sense avoid. -European COMETS and (CATEC), CATEC-ES unmanned helicopters, and 10 -Multi-vehicle coordination. AWARE projects http:/www.catec.c quadrotors) Avionics and onboard systems Automation and robotics Laboratory for Autonomous Class Il: Aero-Tec CB-5000 -Control of VTOL UAS Load transportation using 3 Flying Robots, TU Berlin, heli (12-16 kg) Collision detection& avoidance helicopter FAFR-DE Class iv: Mikado logo 14 Distributed control of multiple -Sensor nodes deployment by http:/pdvcs.tu-berlin.derlfafirself-madequadrotor(5kg).RuaS three autonomous helicopters Autonomous Vehicles Group, I Clas I: Bergen Industrial win. I - Flight control with slung load. I-Control of a helicopter slung University, A Urban UAs navigation load system http:/www.es.cu.dk/projects/700Nitro,ThunderTigerE550 Task allocation and Class Iv: IMH- 120 Corona, T-Rex coordinated night of AV Robust control 450, X-3D-BL quadrotor Collision-free path generation utonomous Helicopter project Class lll: 90-size XCell Acrobatic and aggressive flight-Autorotation-based andin Tempest, 90-size Synergy N9. control Learning-based aerobatic flight. STARMAC project hitp: /hybrid. stanford. eduiostarn Class IV: STARMAC platform [Learning-based control Bakflip control for a quadrotor Multi-agent contro -collision avoidance tlight ACFR, University of sydney, Weed monitoring and animals-Vision- based mapping and ACFR-AU Class III: UAV vision G18 tracking classification (fixed-wing hittp:/www.acfr:usvd.edu.au/reshelicopter Multi-UAV active SLAM Aquatic weed surveillance and UAS SYSter roSpace Controls Lab, MIT, Class IV: Dragonfly UAS modeling and control Indoor flight using vIcoN quadrotors, Asc lech quadrotors Multi-UAS task assignement.-Persistent mission plannin httP acl mit. edu Multi-vehicle health management/-Health management of UAS Robust robotics Group, MIT -Non-GPS indoor navigation waypoint guidance and RRG-US Planning under uncertainties. geolocation of ground target htp: /groups. csail mit. eduirg/ Class IV: ASC Tech quadrotors. -SLAM-based navigation and -Autonomous tlight in unknown Planning for target tracking Autonomous systems lab Class Iv: home-designed AIRobots project (Inspection ETH. ASL-CH I quadrotors and coaxial rotorcraft Rotorcrafts) Design of miniature platforms http://www.asl.ethz.ch/research:classv:under-developmentfor-sflyproject(swarmofMicro|-indoorhovering Heudiasyc Lab, University of Class IV: quadrotors and other -Design of multi-rotor Technology of Compiegne, home-designed multi-rotor configurations Niching. HDS-FR configurations Modeling and control of RUAS. -Vision-based hovering http://www.hds.utcfr Vision-based navigation GRASP Lab. Univcrsity of Autonomous navigation and Aggressive and accurate Pennsylvania GRASP-US Class iv: Asc tech control of quadrotors using http:/alliance.seas.upenn.edu-quadrotors. VICON control for 3d dvnamic Cooperative manipulation and tation with aerial robots Automatic flight Collaborative rescarch bctwccn Class IV: Homc-built Optic flow-based terrain vision-based hovering France(CEA and USI) and quadrotor follow Terrain following in a Australia(ANU) Visual servo structured indoor en Figure 2. Continued Journal of field robotics doi 10.1002/ rob 320. Journal of field Robotics-2012 GUIDANCE NAVIGATION igh-level decision-making Situational awareness mission planning and execution monitoring Perception path planning le estimation low-level decision-makin Inl se Sensing trajectory generation RUas state FLIGHT CONTROL erence 3D position/velocity control attitude control, etc. RUAy rotorcraft with avionics, communication equipment, mission payload, etc. lowest links other modules and functions visualizati telemetry and data logging RUAS GCS Human-Robot Interface(HRT) Unmanned Other Systems Autonomous Systems Human (LAS, UGV, etc.) (GPS, etc. Figure 3. The overall structure of guidance, navigation, and control systems onboard a RUAS operate with some degree of autonomy, and can be expendable Definition 4. Autonomy: The condition or quality of being or reusable. Most RUAVs include integrated equipment such as self-governing. When applied to RUAS, autonomy can be de avionics, data links, payload, and various algorithms needed for fined as RUAs's own abilities of integrated sensing, perceiving, flight analyzing, communicating planning, decision-making and act ing/executing, to achieve its goals as assigned by its human op Definition 3. Rotorcraft unmanned Aerial or Aircraft System erator(s)through a designed human-robot interface (Hrd)or by (RUIAS): A RUIAS is a plysical system that includes a ruia another system that the ruas communicates with communicalion architecture, and a ground control station with no human element'aboard any component. The ruAs acts on Definition 5. Autonomous RUAS: A RUAS is defined to be the physical world for the purpose of achieving an assigned mis- autonomous relative to a given mission (relational notion)when sion. Contrary to the uas definition proposed in the us dod it accomplishes its assigned mission successfully, within a defined UAS Roadmap 2010-2035(Roadmap, 2010), here the human el- cope, with or without further interaclion with human or other ement is not part of the ruas but rather an external system that external systems. A RUAS is fully autonomous if it accomplishes interacts with the ruas; see Figure 3 its assigned mission successfully without any intervention from a bThe plural of ruas will be also denoted RUAS ee the UAS Roadmap(Roadmap, 2010) for the definition of hu- BOwn"implies independence from human or any other external man element systems Journal of field robotics doi 10 1002/rob Kendoul: Survey of Advances in GNC of RUAs .321 human or any other external system while adapting to operational mation analysis, decision and action selection, and action and environmental condition implementation Other relevant concepts and results have been developed by academia, especially from the human machine interaction and artificial intelligence areas( Castel- Definition 6. Autonomy Level(AL): The term"autonomy franchi & Falcone, 2003; Zeigler, 1990), as well as by NASA level"is used in different contexts in the research community. In and the military, using mainly the ooDA(Observe, ori- Huang (2008)and Huang, Messina, 8 Albus (2007) for exam- ent, Decide, and Act)loop. A more fully developed frame ple, AL is equivalent to human independence (Hl). In this paper, work for defining autonomy levels for unmanned systems AL is defined as a set of progressive indices, typically numbers (ALFUS) has been proposed by an NIST-sponsored ad hoc and/or names, identifying a RUAS capability for performing al- workgroup(Huang, Messina, Albus, 2007). In the AL tonomously assigned missions. A RULAS'S AL can be character FUS framework, the autonomy level, later renamed contex- ized by the missions that the ruAs is capable of performing(mis- tual autonomous capability(CAC), is measured by weight- sion complexity or MC), the environments within which the mi ing the score of various metrics for three aspects, or axes sions are performed (environment complexity or EC), and inde- which are human independence(Hi), mission complexity pendence from any external system including any human element (MC), and environmental complexity(EC). In 2002, the U.S external system independence or ESI). Note that this al defini- Air Force Research Laboratory (AFRl) presented the re tion is similar to the contextual autonomous capability (CAc) sults of a rescarch study on how to measure the auton- definition in the NIST aLFuS framework(Huang, 2008), except omy level of a UAV(Clough, 2002). The result of this study for Hl, which is replaced here by esI is the autonomous control levels(ACL)chart, where 11 autonomy levels have been identified and described. The As in Clough(2002)and Merz(2004), we make a autonomy level is determincd using the OODA concept, distinction between automatic, autonomous, and intelli- namely, perception/situationalawareness(observe),anal- gent systems. An automatic system will do exactly as ysis/coordination(orient), decision-making (decide), and programmed because it has no capabilities of reasoning, capability(act). A few other papers also briefly discussed decision-making, or planning. An autonomous system has the UAS autonomy(Fabiani, Fuertes, Piquercau, Mampey the capability to make decisions and to plan its tasks and &Tcichteil-Knigsbuch, 2007; Lacroix, Alami, Lemaire, Hat path in order to achieve its assigned mission. An intelligent tenberger, Gancet, 2007; Merz, 2004; Mettler et al.,2003 system has the capabilities of an autonomous system plus Roadmap, 2010), but to our knowledge there have been no the ability to generate its own goals from inside by moti- other papers published about metrics and autonomy levels vations and without any instruction or influence from out- for UAS in general and rUas in particular side. In this paper, we are interested in automatic and au Although the NIST ALFUS and AFRL ACL frame tonomous systems; intelligent systems are out of the scope works provide significant insight and progress in the field of this paper because such systems do not vet exist for UAS. of unmanned system autonomy characterization and eval- Generally, we do not want an intelligent system, but an au- uation, they are difficult to apply directly to RUAS,es tonomous system that does the job assigned pecially from the research perspective. Indeed, the AFRL ACL chart is most useful and applicable to relatively large UAS operating at high altitudes in obstacle-free environ- 3.2. Autonomy Levels and Metrics ments.Furthermore, the used metrics are military scenario- From reviewing the ruas litcrature, it became evident that oriented and are based on the OODA loop, originally de there is an overall need for a comprehensive framework veloped by the military to illustrate how to take advantage of an enemy. On the other hand, ALFUS is a generic frame that allows RUAS Practitioners, particularly researchers, work covering all unmanned systems, and its application to evaluate and characterize the autonomous capabili to Ruas is not straightforward. It is also important to note ties of RUAs. The next paragraph gives a brief overview that autonomy metrics and taxonomies have evolved and of autonomy-related works, which are also some of the sources that were consulted for developing the ALFURs xpanded in theory and practice since then. In this section, framework we attempt to address the challenge of ruas autonomy Many of the autonomy articles use Sheridans work characterization by proposing the autonomy levels for un- Sheridan, 1992)as a reference for initial understanding of manned rotorcraft systems(AL FURS) framework Based on utonomy and human-computer interaction. In his book research, including NIST ALFUS and AFRL ACL studies, Sheridan, 1992), Sheridan proposed a 10-level scale of de- and the desire to have a research-oriented autonomy frame grees of autonomy based on who makes the decision(ma work that better suits RUAS operating at low altitudes and chine or human)and on how those decisions are executed in cluttered environments, the alfurs framework was In 2000, Parasuraman, Sheridan, and wickens(2000)in- troduced a revised model of autonomy levels based on Future combat systems(FCS)Program, autonomous collaborative four classes of functions: information acquisition, infor- operations(ACO)program, etc Journal of field robotics doi 10.1002/ rob 322. Journal of field robotics -2012 developed ALFURS is based on the RUAS onboard func- Perception: RUAS perception is the ability to use inputs tions that enable its autonomy. These autonomy enabling from sensors to build an internal model of the environment functions(AEF) can be regrouped into three main cate- within which the vehicle is operating, and to assign entities, gories: guidance, navigation, and control (GNC). Before events and situations perceived in the environment to classes elaborating this concept, let us first define GNC systems The classification(or recognition) process involves comparing and their relevant components or aef when related to what is observed with the Rlias's a priori knowoledge(huang, RUAS, as well as their interaction in a typical RUAS au- 2008). Perception can be further divided into various func tonomy software implementation; sce Figure 3. Indeed, we tions on different levels such as mapping, obstacle and target found in our literature review that gnc terms are com- detection, and object recognition monly used in UAS research, but they are rarely defined Situational Awareness(SA): The notion of Sa is commonly and are sometimes mistakenly use used in aviation systems, and numerous definitions of Sa have been proposed. In this paper, we adopt Endsley,'s defi Definition7. Automatic Flight Control System(AFCS): Au- nition(Endsley, 1999)of SA as" the perception of elements in tomatic controll can be defined as the process of manipulating the environment within a desirable volume of time and space, the inputs to a dynamical system to obtain a desired effect on its the comprehension of their meaning, and the projection of their outputs without a human in the control loop. For RUAS, the de- status in the near future." SA therefore is higher than per sign of flight controllers consists of synthesizing algorithms or ception because it requires the comprehension of the situation control lazos that compute inputs for vehicle actuators(rotors, and then the extrapolation or projection of this information aileron, elevator, etc. )to produce torques and forces that act on forward in time to determine how it will affect future states of the vehicle in controlling its 3D motion(position, orientation, the operational environment and their time derivatives). AFCS, called also autopilot, is thus the integrated software and hardware that serve the control func- Definition 9. Guidance System(GS): A guidance system can tion as defined be defined as the"driver"of a ruas that exercises planning and decision-making functions to achieve assigned missions or goal Definition 8. Navigation System (NS ): In the broad sense, The role of a guidance system for RUAS is to replace the cog- navigation is the process of monitoring and controlling the move- nitive processes of a human pilot and operator. It takes inputs ment of a craft or vehicle from one place to another. For RUAS, from the navigation system and uses targeting information(mis navigation can be defined as the process of data acquisition, data sion goals)to make appropriate decisions at its high level and Lo analysis, and extraction and inference of information about the generate reference trajectories and commands for the AFCs at its vehicle's states and its surrounding environment with the objec- low level. GS decisions can also spark requests to the navigation tive of accomplishing assigned missions successfully and safely. system for new information. A guidance system comprises var- This information can be metric, such as distances, topological, ious autonomy-cnabling functions including trajectory genera such as landmarks, or any other attributes that are useful for mis- tion Path planning, mission planning, and reasoning and hig/ sion achievement. The main autonomy-enabling functions of a level decision making navigation system, from lower to higher level, are as follows Trajectory Generation: A trajectory generator has the role Sensing: A sensing system involues one or a group of devices of compuling different molion functions(reference position, (sensors) that respond to a specific physical phenomenon or reference heading, etc. that are physically possible, satisfy stimulus and generate signals that reflect some features of or RUAS dynamics and constraints, and can be directly used as informalion about an object or a physical phenomenon. Sen reference trajectories for the flight controller. Reference trajec sors such as gyroscopes, accelerometers, magnetometers, static tories can be preprogrammed, uploaded, or generated in real- and dynamic pressure sensors, cameras, and lidars are com time onboard the ruas(dynamic trajectory generation )ac- monly used onboard LAs to provide raw measurements for cording to the outputs of higher-level guidance module state estimation and perception algorithms Path Planning: The process of using accumulated navigation Stale Estimation: This concerns mainly the processing of raz data and a priori informalion to allow the ruas to find the Sensor measurements to estimate variables that are related to best and safest way to reach a goal position/configuration or the vehicle's state, particularly those related to its pose and to accomplish a specific task. Dynamic path planning refers to motion, such as attitude, position, and velocity. These esti- onboard, real-time path planning mates can be absolute or relative. Localization is a particular Mission Planning: The process of generating tactical goals, case of state estimation that is limited to position estimation route general or specific ), a commanding structure, coordina relative to some map or other locations tion, and timing for a rlAs or a team of unmanned systems (Huang, 2008). The mission plans can be generated either in l In the remainder of the paper, the term control refers to automatic This definition is also similar to the one used in the alFUS frame control work(Huang, 2008 Journal of field robotics doi 10 1002/rob Kendoul: Survey of advances in GNC Of ruas 393 advance or in real time. They can be generated by operators teraction with an external system, the rUas needs higher or by onboard software systems in either centralized or dis- levels of GNC. One of the primary motivations for using tributed ways. The termdynamic mission planning"can also GNC as aspects or axes for characterizing the autonomy be used to refer to onboard, real-time mission plannin level of ruas is to use terms and concepts that are famil Decision Making: The RUAS's ability to select a course of ac- iar to the UAS research community. Indeed, we are inter- tions and choices among several alternative scenarios based on ested in a framework that describes the autonomy levels in available analysis and information. The decisions reached are a simple but meaningful way, so that it can easily be un- relevant to achieving assigned missions efficiently and safeli derstood and used by other researchers The intent of this Decision-making processes can differ in type and complex- GNC-based ALFURS framework is also to help categorize ity, ranging from low(e. g, fly home if the communication the RUas literature and research presented in Sections 4, link is lost) to high-level decision making. Trajectory gener- 5, and 6. Differentiating among consecutive autonomy lev ation, path planning, and mission planning also involve some els is not trivial and may even be subjective. On the other ision-making prod hand, autonomy levels need to be distinguished to be use Reasoning and Cognizance: The RUAS's ability to analyze ful for evaluation and comparison, and to be easily usable and reason using contextual associations between different by the research community. Therefore, an 11-levell3scale entities. These are the highest level aEf that a RUAS can of autonomy, shown in Figure 4, was proposed, based on perform, with varying levels of augmentation or replacement gradual increase(autonomy as a gradual property)of GNC of human cognitive process Reasoning and cognizance occur functions and capabilities. Main or key GNc functions that prior to the point of decision making. Note that transition enable each autonomy level are verbally described, along from high-level navigation(situational awareness) to high- with their correspondences with MC, EC, and ESI metrics level guidance(reasoning and cognizance) is of course quite (illustrated by color gradient). The GNC category aspect of blurry. this scale is advantageous because it helps ruas develo ers to easily and correctly determine the autonomy level of an existing algorithm or system, but also to identify the For better understanding of these GNc-related defi AEF needed to achieve a certain autonomy level during the nitions, the reader is encouraged to read the nist docu- design of a new system ment(Huang, 2008)for more details about the meaning of key terms such as"mission, ""goal, ""operator, "and environment. Figure 3 shows a simple block diagran of Observation 1. Although the ALFURS framework was key gnc functions and their interaction in a typical au- proposed for RUAS, it can be used for UAS in general tonomous RUAS. Traditionally, GNC has always been the bottom blobs of Figure 3, 1.e, flight control, state estimation In addition to autonomy characterization and eval- and trajectory generation/waypoint navigation. However, uation, two other aspects are important for comparing many of the rescarch programs today are geared toward re- and evaluating RUAS or autonomy technologies: perfor- alizai g all of the GNC functions in Figure 3 onboard the mance and dependability. Autonomy is related to what the RUAS RUAS Can do(mc, ec, esi), performance is related to how ALFURS levels are determined based on degrees of well the ruaS meets mission requirements(accuracy, time RUAS involvement and cfforts in performing AEF or GNC etc.). ama ff he vis face a gaonthms or GNC systems that functions. The general trend may be that RUAs autonomy accomplishe sion without problems(success rate level increases when the levels of GNC functions increase. failure rate, etc .In In other words, autonomy level is higher when the gnc are developed to serve the same autonomy level can still systems include high-level AEF functions, and they are per- be compared and evaluated based on performance and de formed by the rUas to a greater extent. Because the main pendability metrics. This is out of the scope of this paper, focus of this paper is not on autonomy characterization, but such a work will benefit the rescarch community and this concept will not be elaborated in detail. However, it UAS practitioners in general is important to note that there is a direct correspondence As a direct application of the ALFURS framework between GNC functions or systems and the mc, ec and RUAS-related works, reviewed in Sections 4, 5, and 6, are ESI metrics used in the ALFUS project. Therefore, it is pos- classified based on the GNC aspects and the level of auton- sible to establish GNC metrics by mapping ALFUS met omy they are addressing, starting from low-level AEF such rics to the alfurs framework. indeed to achieve a com- as automatic control to high-level functions such as cooper- plex mission in a complex environment without any in- ative mission planning 1?Metrics for measuring the level of GNC functions, and process for determining the RUAS's level of autonomy using the scores of Eleven scales, to be consistent with AFRL ACL chart and NIST these various metrics Journal of field robotics doi 10.1002/ rob 324. Journal of field robotics -2012 LEVEL EVEL DESCRIPTOR GUIDANCE NAVIGATION CoNTR。L ESI EC MC Human-level decision-making, Human-like navigation capabili accomplishment of most Same or better control ties for most missions Autonomous missions without any interven- fast SA that outper per formance as for a piloted g rforms human tion from ES(100% ESI aircrafi in the same situalion Long track awareness of very Ability to choose the appro-&/ e al SA in extremely complex cognizant of all within the environments and situations and conditions operalion range Swarm Distributed strategic group Cognizance and planning, selection of strategic complex environments and priate control architecture goals,mission execution with nol situations, inference and anticipa- based on the understanding 9 Group Decision supervisory assistance, negolial- tion of other agents intents and pf the current situation/cont- Making ing with team members and ES. strategies, high-level team SA. ext and future consequences 35 ing and higher lev bility to change or switch Awareness strategic decision-making. environments and situations, between different contro inference of self/others intent and strategic mission planning, most Cos gnizance /of supervision by RUAS, choose anticipation of near-future events understanding of the current strategic goals, cognizance and consequences(high fidelity situation/context and future consequences Collaborative mission planning Combination of capabilities in RT Collaborative and execution, evaluation and levels 5 and 6 in highly complex, same as in previous levels / Mission optimization of multi-vehicle adversarial and uncertain environ-(no-additional control Planning mission performance. allocation ment, collaborative mid fidelity capabilities are required) of tactical tasks lo each agent Reasoning, high-level decision Higher-level of perception to Dynamic making, mission driven decisions, recognize and classify detected sane as in previous levels Mission high adaptation to mission objects/events and to infere some Planning changes, tactical task allocation. of their attributes, mid fidelity SA/ capabilities are required) execution monitoring RT Cooperative Collision avoidance, cooperative Relative navigation between Navigation and path planning and execution to RUAS, cooperative perception, Distributed or centralised Path Planning meet common goals, swarm or data sharing, collision detection, flight control architectures, group optimization shared low fidelity Sa coordinated maneuvers Hazard avoidance, RT path Perception capabilities for ob Obstacle/Event planning and re-planning, event cle, risks, target and environment Accurate and robust 3D Detection and driven decisions, robust response changes detection, RI mapping trajectory tracking capability to mission changes (optional). low fidelity SA is desired Path Planning Health diagnosis. limited Robust flight controller, Fault/ Event adaptation, onboard conservative Most health and status sensing reconfigurable or adaptive by the rUAs, delection of Adaptive and low-level decisions control to compensate fo hardware and software faults RUAS execution of pre-programmed most failures, mission and environment changes All sensing and state estimation Navigation Same as in level I by the RUAS (no I GPS), all perception and situation in Level 1 (e,g, Non-GPS) awareness by the human operator re-programmed or uploaded Automatic flight plans(waypoints, reference /Most sensing and state estimation Control commands are Flight trajectories, etc. ) all by the RUAS, all perception and computed by the flight Control analyzing, planning and situational awareness by the control system(automatic decision-making by es human operator control of the RUAs 3D) pose) all guidance functions are Sensing may be performed by the 0 givenby a remoteS//S performed by external systems RUAS, all data is processed and Control commands are Remote Control(mainly human pilot or operator) analyzed by an external system (mainly human) (mainly human pilot) Figure 4. Illustration of ALFURS autonomy levels as a gradual increase of GNC capabilities and corresponding MC, EC, and ESI Acronyms: ESI (external system independence), EC (environment complexity), MC (mission complexity), ES(external system), SA (situational awareness), RT (real time Journal of field robotics doi 10 1002/rob

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