EE4308 Advances in Intelligent Systems & Robotics

Transcription

1 EE4308 Advance in Intelligent Sytem & Robotic Part 2: Unmanned Aerial Vehicle Ben M. Chen Profeor & Provot Chair Department of Electrical & Computer Engineering Office: E , Phone:

2 Coure content & continuou aement Thi module i to be aeed totally baed on project. For the 2nd part, there will be introductory lecture about 6 hour in the firt 2 week on ome baic topic related to unmanned aerial vehicle UAV, i.e., Introduction to unmanned aerial vehicle ytem and application; UAV hardware and oftware tructure; UAV platform and avionic ytem deign; UAV dynamic modeling and flight control ytem deign, path planning, trajectory generation, etc. After the introductory part, there will be tutorial eion conducted either in the cla room or in the VICON room in T Lab Building Level 4 actual location will be announced before hand for project imulation and actual flight experiment. The project for thi part i on how to plan path for drone to fly in actual complicated environment with obtacle. Simulation in ROS i required together with real experiment to be conducted in the T Lab VICON room. Project will be aeed baed on imulation and experimental reult, report and preentation. EE4308 ~ 2

3 Outline of the note Introduction to drone and application o Type of drone o Application o NUS UAV reearch highlight Overall tructure of unmanned ytem o Communication unit o Ground control ytem Internal tructure of unmanned ytem o Avionic ytem o Dynamic modeling o Flight control ytem o Meaurement ytem o Path planning o Trajectory generation o Miion management EE4308 ~ 3

4 What i a drone or UAV? An unmanned aerial vehicle UAV or drone i an aircraft that i equipped with neceary data proceing unit, enor, automatic control, and communication ytem and i capable of performing autonomou flight miion without the interference of a human pilot A drone? EE4308 ~ 4

5 Drone type Fixed wing UAV EE4308 ~ 5

6 Drone type Flapping wing UAV EE4308 ~ 6

7 Drone type Rotorcraft UAV EE4308 ~ 7

8 Drone type Hybrid UAV EE4308 ~ 8

9 Drone application GPS baed environment Search & recue Surveillance & patrolling Foretation & agriculture Aerial mapping EE4308 ~ 9

10 Drone application GPS baed environment Mining Oil indutry Policing Aerial filming EE4308 ~ 10

11 Drone application GPS denied environment Urban canyon & inide foret Indoor Tunnel Under bridge EE4308 ~ 11

12 Drone indutry Market ector Senor Autopilot OEM Propulion Operator & Service Navigation Data Handling Data Link Military Reearch/education Mining Foretry Power & pipeline Media Real etate Agriculture Policing Film indutry Oil & ga indutry Port Entertainment EE4308 ~ 12

13 NUS reearch team & unmanned ytem platform EE4308 ~ 13

14 NUS unmanned ytem reearch highlight Indoor Navigation & Control Firefighting Inide Foret Navigation NUS ALL Hit and Run in UAV Flight Formation ONE Micro UAV hit & run UAV Hybrid UAV Cargo Tranportation UAV Calligraphy EE4308 ~ 14

15 Award won by NUS reearch team DARPA UAVForge Finalit 2012 WCICA Bet Paper Award CCC Guan Zhaozhi Prize IMAV nd Place IMAV 2014 Champion EE4308 ~ 15

16 Overall tructure of unmanned ytem UAV UGV Onboard Sytem Onboard Sytem USV Ground Control Sytem UUV EE4308 ~ 16

17 Ground control ytem Online touch creen path planning on the map Command window to iue real time command EE4308 ~ 17

18 Communication unit The communication unit in the UAV ytem framework are deployed a interface between the UAV entity itelf and external entitie. The external entity can be the GCS for the ground operator, or another UAV entity for information exchange. With UAV to GCS communication, the operator can remotely control and monitor UAV in operation. With inter UAV communication, the UAV team can multiply their capability and effectivene in cooperative tak. Decentralized Control Centralized Control EE4308 ~ 18

19 Internal tructure of unmanned ytem UAV Indoor Inpection UGV USV Powerline Inpection Miion Management An unmanned ytem Path Planning Trajectory Generation Outer Loop Control Inner Loop Control Meaured Signal EE4308 ~ 19

20 Hardware component & avionic ytem An NUS made micro quadrotor An NUS made avionic ytem Miion Management Hardware Platform Path Planning Trajectory Generation Outer Loop Control Inner Loop Control Meaured Signal EE4308 ~ 20

21 Eential hardware component of unmanned ytem GPS/AHRS Sonar Senor Avionic Primary Computer Tak Management & Flight Control Other Senor LIDAR Viion Secondary Computer Actuator Management Decode Encode Power Sytem Inter Vehicle Communication High Bandwidth Long Range RC Receiver Actuator Other UAV Ground Control Station Human Operator Control Surface Object & environment EE4308 ~ 21

22 Hierarchy of an avionic ytem EE4308 ~ 22

23 Component breakdown of a typical multi rotor UAV EE4308 ~ 23

24 Multi rotor UAV platform BlackLion 168 BlackLion 068 K Lion EE4308 ~ 24

25 Dynamic modeling of a quadrotor UAV Miion Management Aerial Vehicle Path Planning Trajectory Generation Outer Loop Control Inner Loop Control Meaured Signal EE4308 ~ 25

26 Dynamic model tructure of the aerial vehicle Force Velocity Poition Moment Euler angle Angular Velocity ail ele thr rud aileron input elevator input collective pitch input rudder input EE4308 ~ 26

27 Linearized model of the aerial vehicle With ome appropriate implification, we can obtain a implified linear model for a quadrotor UAV a follow 1, 2, 3, 4 are repectively the motor angular peed and i a contant matrix related to motor propertie. g i the gravity contant, m i the UAV ma. EE4308 ~ 27

28 Flight control ytem Due to the nature of the time cale of the aircraft dynamic, i.e., the attitude repone i much fater compared to that of the poition and velocity, it i a common practice to eparate the flight control ytem into two part, i.e., the inner loop and the outer loop control. More pecifically, Inner loop control ytem i to guarantee the tability of the aircraft attitude; and Outer loop control ytem i to control the aircraft poition and velocity. Miion Management Path Planning Trajectory Generation Outer Loop Control Inner Loop Control Meaured Signal EE4308 ~ 28

29 The objective of a control ytem Deired Performance REFERENCE Difference ERROR INPUT to the ytem Information about the ytem: OUTPUT + Controller Sytem to be controlled Objective: To tabilize the overall cloed loop ytem Stability; and to make the ytem OUTPUT and the deired REFERENCE a cloe a poible, i.e., to make the ERROR a mall a poible. EE4308 ~ 29

30 Claical control technique Claical control cheme can be depicted in the following diagram: R + E K G Y where G i the tranfer function of the ytem to be controlled, K i the controller to be deigned, R, Y and E are the Laplace tranform of the reference ignal r, the meaurement y and the tracking error e, repectively. Once again, the objective are to make the overall cloed loop ytem table and y to match r a cloe a poible. A typical repone of a 2nd order ytem EE4308 ~ 30

31 EE4308 ~ 31 Claical control technique cont. Claical control technique cont. Note that U G Y U Y G E K U Y R E Y R K G E K G U G Y 1 R K G Y K G Y K G R K G Y 1 K G K G R Y H Cloed loop tranfer function from R to Y. + R G K Y E We have

32 EE4308 ~ 32 Claical control technique cont. Claical control technique cont. Let u focu our attention on the control ytem deign of a double integrator with a proportional deferential PD control law, i.e., Thi implie Thu, the block diagram of the control ytem can be implified a 1 K G K G H R Y The whole control problem become how to chooe an appropriate K uch that the reulting H would yield deired propertie between R and Y. 2 1 G k k K d p and p d p d k k k k K G K G H 2 1 a2nd order ytem

33 Claical control technique cont. Behavior of the econd order ytem with a unit tep input Again, conider the following block diagram with a tandard 2nd order ytem R = 1/ r = 1 H 2 2 n 2 n 2 n Y The behavior of the ytem i given on the right. It i fully characterized by, which i called the damping ratio, and n, which i called the natural frequency. EE4308 ~ 33

34 Claical control technique cont. Control ytem deign with time domain pecification R r = 1 H 2 2 n 2 n 2 n Y overhoot M p Overhoot v damping ratio rie time t r t 1% ettling time t t r 1.8 n t 4.6 n EE4308 ~ 34

35 Claical control technique cont. Example: Given a double integrator plant, deign a PD control law uch that the reulting cloed loop ytem i table. When it track a tep reference, it ha a ettling time le than 2 econd and overhoot le than 10%. t 4.6 n 2 n X Recall that H G K kd k p 1 G K 2 k k d p H 2 n n n k d 4.6, k 14.7 p Thi additional term might caue ome problem EE4308 ~ 35

36 Claical control technique cont. Simulation reult Step Repone Overhoot i more than 20% 1 Amplitude Settling time i right on the target Time ec Need to fine tune it! EE4308 ~ 36

37 Compoite nonlinear feedback CNF control Motivation 1.5 Repone with a mall damping ratio 1 fat rie time Magnitude Repone with a large damping ratio no overhoot 0.5 CNF Control Time EE4308 ~ 37

38 CNF control cont. CNF control conit of a linear law and a nonlinear feedback law with any witching element. The linear part i deigned to yield a cloed loop ytem with a mall damping ratio for a quick repone. On the other hand, the nonlinear part i ued to increae the damping ratio of the cloed loop ytem a the ytem output approache the target reference to reduce the overhoot caued by the linear part. For a double integrator plant characterized by The following CNF control law i capable of beating even a time optimal control TOC law in ettling time with 1% overhoot EE4308 ~ 38

39 CNF control cont. Simulation reult Overhoot i le than 1% The bet poible Settling time Output repone of the CNF control and the TOC EE4308 ~ 39

40 Detailed tructure of flight control ytem EE4308 ~ 40

41 Inner loop control ytem Controlling a quadrotor type drone i rather imple a mechanically each channel can be regarded a decoupled from one another. Exercie: Uing technique learnt from EE3331C to deign a PD controller for each channel uch that r r r EE4308 ~ 41

42 Outer loop control ytem deign etup VIRTUAL ACTUATOR EE4308 ~ 42

43 Propertie of the outer loop dynamic It can alo be verified that coupling among each channel of the outer loop dynamic i very weak and thu can be ignored. A a reult, all the x, y and z channel of the rotorcraft dynamic can be treated a decoupled and each channel can be characterized by p * 0 1 p* 0 a v 0 0 v 1 * * where p * i the poition, v * i the velocity and a * i the acceleration, which i treated a control input in our formulation. For uch a imple ytem, it can be controlled by almot all the control technique available in the literature, which include the mot popular and the implet one uch a PID control Exercie: Deign a PD control law for the above ytem to track a reference poition. * EE4308 ~ 43

44 RPT outer loop control law RPT control i capable of utilizing all poible information available in it controller tructure. Such a feature i highly deirable for flight miion involving complicated maneuver, in which not only the poition reference i ueful, but alo it velocity and even acceleration information are important or even neceary to be ued in order to achieve a good overall performance. For each x, y, z channel, it RPT control law can be characterized a follow a 2 * 2 n 2n n pr n vr ar * 2 p v * where p r, v r and a r, are repectively the poition, velocity and acceleration reference; i the damping ratio and n i the natural frequency of the cloed loop ytem, which can be elected in accordance with the phyical propertie of the pecific channel. We will notice that uch a control tructure will link moothly with the navigation block i.e., path planning and trajectory generation and give moother flight performance. EE4308 ~ 44

45 Simulation of RPT control with =0.7 & n =1 1 red: actual repone; blue: reference 0.2 Poition Poition Velocity Time ec red: actual repone; blue: reference Time ec red: actual repone; blue: reference 1 Tracking error Tracking error Time ec Velocity Time ec Acceleration 1.5 Acceleration Tracking error Time ec Time ec EE4308 ~ 45

46 A brief view on the development of control technique Claical control PID control, developed in 1940 and ued heavily for in indutrial procee. Optimal control Linear quadratic regulator control, Kalman filter, H 2 control, developed in 1960 to achieve certain optimal performance. Robut control H control, developed in 1980 & 90 to handle ytem with uncertaintie and diturbance and with high performance. Nonlinear control Still on going reearch topic, developed to handle nonlinear ytem with high performance. Intelligent control Knowledge baed control, adaptive control, neural and fuzzy control, etc., developed to handle ytem with unknown model. EE4308 ~ 46

47 Meaurement ignal Meaurement come from inertial enor gyrocope, accelerometer, magnetometer and External poitioning ytem GPS Beidou VICON UWB or Internal enor ytem Viion LiDAR Radar Sonar Miion Management Path Planning Trajectory Generation Outer Loop Control Inner Loop Control Meaured Signal EE4308 ~ 47

48 Gyrocope A gyrocope i a pinning wheel or dic in which the axi of rotation i free to aume any orientation by itelf. When rotating, the orientation of thi axi i unaffected by tilting or rotation of the mounting, according to the conervation of angular momentum. Becaue of thi, gyrocope are ueful for meauring or maintaining orientation. Inexpenive vibrating tructure gyrocope manufactured with MEMS technology have become widely available nowaday. Thee are packaged imilarly to other integrated circuit and may provide either analog or digital output. In many cae, a ingle part include gyrocopic enor for multiple axe. Some part incorporate multiple gyrocope and accelerometer to achieve output that ha ix full degree of freedom. MEMS Gyrocope EE4308 ~ 48

49 Accelerometer & magnetometer An accelerometer i a device that meaure proper acceleration; proper acceleration i not the ame a coordinate acceleration rate of change of velocity. For example, an accelerometer at ret on the urface of the Earth will meaure an acceleration due to Earth' gravity, traight upward by definition of g 9.81 m/ 2. By contrat, accelerometer in free fall will meaure zero. A magnetometer i an intrument that meaure magnetim either magnetization of magnetic material like a ferromagnet, or the direction, trength, or the relative change of a magnetic field at a particular location. A compa i a imple example of a magnetometer, one that meaure the direction of an ambient magnetic field. EE4308 ~ 49

50 Inertial navigation ytem INS An inertial navigation ytem INS i a navigation aid that ue a computer, motion enor accelerometer and rotation enor gyrocope to continuouly calculate via dead reckoning the poition, orientation, and velocity direction and peed of movement of a moving object without the need for external reference. It i ued on vehicle uch a hip, aircraft, ubmarine, guided miile, and pacecraft. Other term ued to refer to inertial navigation ytem or cloely related device include inertial guidance ytem, inertial intrument, inertial meaurement unit IMU. An INS i capable of providing the following information of unmanned vehicle: Acceleration Velocitie Rotating angle Heading angle EE4308 ~ 50

51 Global poitioning ytem GPS The Global Poitioning Sytem GPS i a pace baed radio navigation ytem owned by the US Government and operated by the US Air Force. It i a global navigation atellite ytem that provide geolocation and time information to a GPS receiver in all weather condition, anywhere on or near the Earth where there i an unobtructed line of ight to four or more GPS atellite. The GPS ytem provide critical poitioning capabilitie to all uer around the world. GPS i widely ued by drone for outdoor application nowaday. The incorporation of GPS receiver in drone allow for waypoint navigation. It allow a drone to autonomouly fly to pre programmed point, can intruct the drone how fat, how high, and where to fly. EE4308 ~ 51

52 Global poitioning ytem cont. Space Segment NAVSTAR: 30 atellite with atomic clock At leat 5 alway viible uually km altitude km/h peed Control Segment Mater control for atellite Update atellite poition Kalman Filter and atomic clock Uer Segment General GPS receiver with antenna and precie clock crytal Geoid model for height etimation EE4308 ~ 52

53 Global poitioning ytem cont. Navigation Equation The receiver ue meage received from atellite to determine the atellite poition and time ent. For n atellite, the equation to atify are: ~ 2 x x y y z z t b c, i 1,, n where x i, y i, z i : poition of atellite i; x, y, z: receiver poition; c: peed of light; b: receiver clock bia; : on board receiver clock; : atellite time. Leat Square Solution where i i i i xˆ, yˆ, zˆ, bˆ x x y y z z bc p arg pi ~ ti i min x, y, z, b i i i i c i peudorange. For 3D localization, we need at leat 4 atellite! i i 2 EE4308 ~ 53

54 VICON: a motion capture poitioning ytem Here i a link to YouTube for a tutorial on VICON EE4308 ~ 54

55 Senor baed poitioning ytem SLAM Viion baed 3D Dene SLAM LiDAR baed 2D SLAM EE4308 ~ 55

56 Topological SLAM technique Lit of topological SLAM method and their feature/aociated extraction algorithm Line extractor LiDAR Split & merge, expectation maximization, Hough tranform, RANdom Sample Conenu Haar wavelet Viion Fourier tranform imilar approach Edge baed detector Viion Keypoint detector Blob Detector: Scale Invariant Feature Tranform SIFT, Speeded Up Robut Feature SURF, Center Surround Extrema SenSurE Other Detector: corner KLT Fingerprint of place FACT, DP FACT Affine covariant region detector Harri affine, Maximally Stable Extremal Region MSER Bayeian urprie EE4308 ~ 56

57 Path planning Miion Management Path Planning Trajectory Generation Outer Loop Control Inner Loop Control Meaured Signal EE4308 ~ 57

58 Motion planning Motion planning alo known a the navigation problem i a term ued in robotic or unmanned ytem in general for the proce of breaking down a deired movement tak into dicrete motion that atify movement contraint and poibly optimize ome apect of the movement, uch a time and/or energy. For example, conider navigating a UAV inide the T Lab Level 4 VICON room to a ditant waypoint a in the project for the 2nd part of EE4308. It hould execute thi tak while avoiding wall and not hit the pole placed in the room. A motion planning algorithm would take a decription of thee tak a input, and produce the peed and turning command ent to the drone. It might addre iue related to multiple UAV, more complex tak, different contraint e.g., velocity, acceleration, heading, and uncertainty e.g. imperfect model of the environment or UAV. Motion planning algorithm are widely ued for ground robotic ytem a covered in Part 1. EE4308 ~ 58

59 Motion planning Motion planning i to earch an appropriate path for unmanned vehicle that would allow the vehicle to travel afely and efficiently among obtacle. It conit of two part: the geometrical path planning and the trajectory generation or optimization. The path planning i done in the configuration pace with all dynamic of the vehicle being ignored. Thi cover a lot of claical problem uch a the piano mover problem. The trajectory generation i the proce of deigning a trajectory that minimize or maximize ome meaure of performance while atifying a et of contraint of the dynamic model of the unmanned vehicle. Generally peaking, trajectory generation or optimization i a technique for computing an open loop olution to an optimal control problem. It i often ued for ytem where computing the full cloed loop olution i either impoible or impractical. EE4308 ~ 59

60 Search baed motion planning Dicretized tate pace The o called tate of a vehicle varie quite a lot. Depending on the vehicle we are talking about, it might include it poition, body angle and their correponding derivative. For example, a mall omnidirectional robot moving at low peed, one might only need to concern i poition on a 2D flat urface, i.e., the work pace. Therefore, it tate conit of only x, y. For quadrotor, the tate conit of x, y, z, roll, pitch, yaw. If the drone i moving in an agile way, it dynamic property might alo be conidered which enlarge it tate pace. We call the et of all poible tate of the vehicle a the tate pace. EE4308 ~ 60

61 Search pace dicretization Return to the cae of omnidirectional robot, where it tate pace i x, y. We cut a part of the tate pace and dicretize it a hown in the following figure. Each grid repreent a unique dicrete tate a pair of x, y and i denoted a S1, S2, S3 Since it i an omnidirectional vehicle, it can travel toward to it 8 neighbour, i.e., left, right, bottom, top, top left, top right, bottom left, bottom right. By uch connection among all the dicrete tate, a graph emerge with all the dicrete tate a node and the connection to it 8 neighbour a edge. On the ame graph, we can et unreachable tate/node to repreent the obtacle. And a path on the graph i a erie of interconnected node/tate like S1 S2 S5. EE4308 ~ 61

62 Colliion checking A depicted in the figure, there are tate that cannot be reached by the robot black area, being either wall or other obtacle. Entering thoe tate will caue colliion. Thi i the cae of a 2D tate pace. For other type of vehicle uually of higher dimenional tate pace, there might be other contraint other than thee phyical obtacle. For example, an airplane might have it maximum reachable velocity a well a the minimum velocity to keep it in the air. One of the mot baic method of performing colliion checking i the ray cating method. Many other more advanced method are alo baed on it. The baic idea i to expre the vehicle path a a erie of connected line egment. Then a ray i cat from the tart point to the end point of each line egment. If the ray pae through any obtacle area, a colliion i detected. EE4308 ~ 62

63 A* earch algorithm A* i a bet firt earch algorithm, meaning that it olve problem by earching among all poible path to the olution goal for the one that incur the mallet cot leat ditance travelled, hortet time, etc., and among thee path it firt conider the one that appear to lead mot quickly to the olution. It i formulated in term of weighted graph: tarting from a pecific node of a graph, it contruct a tree of path tarting from that node, expanding path one tep at a time, until one of it path end at the predetermined goal node. At each iteration of it main loop, A* need to determine which of it partial path to expand into one or more longer path. It doe o baed on an etimate of the cot total weight till to go to the goal node. Specifically, A* elect the path that minimize f n = gn + hn where n i the lat node on the path, gn i the cot of the path from the tart node to n, and hn i a heuritic that etimate the cot of the cheapet path from n to the goal. EE4308 ~ 63

64 A* earch example The following example i an A* earch algorithm in action where node are citie connected with road and hx i the traight line ditance to target point: Key: green: tart; blue: goal; orange: viited EE4308 ~ 64

65 A* earch illutration Illutration of A* earch for finding path from a tart node to a goal node in a robot motion planning problem. The empty circle repreent the node in the open et, i.e., thoe that remain to be explored, and the filled one are in the cloed et. Color on each cloed node indicate the ditance from the tart: the greener, the farther. One can firt ee the A* moving in a traight line in the direction of the goal, then when hitting the obtacle, it explore alternative route through the node from the open et. EE4308 ~ 65

66 Other path planning earch technique Dijktra algorithm: Claic graph earching algorithm originated from dynamic programming R* algorithm: Optimized baed on A* which depend le on the heuritic function D* lite algorithm: An incremental planning algorithm, build to handle a dynamic changing and unknown environment JPS algorithm: Jumping point earch algorithm an improved verion of A*, created to handle the unneceary zig zag created by tandard A* algorithm PRM: Probability roadmap, uing random ampling to created connected graph, then ue traditional graph earch method to generate the map RRT: Rapidly exploring random tree, combine the ampling and earching in the ame algorithm, however it doe not guarantee an optimal olution RRT*: An improved verion of RRT, guarantee an optimal olution BIT*: Batch informed tree, a combination of random ampling algorithm with the A* algorithm EE4308 ~ 66

67 Trajectory generation Miion Management Path Planning Trajectory Generation Outer Loop Control Inner Loop Control Meaured Signal EE4308 ~ 67

68 Trajectory generation for quadrotor drone With the A* algorithm, we are able to obtain a erie of connected line egment hown a in the figure below The next quetion one would ak: How can thee line egment be realized or ued to guide in a quadrotor drone? EE4308 ~ 68

69 Trajectory generation for quadrotor drone For a quadrotor drone to follow cloely a pre planned path, we need to pecify a et of reference to the outer loop controller a depicted in the general unmanned ytem framework. For drone, the reference et hould conit of the vehicle 3 axi poition reference, x, y, z 3 axi velocity reference, v x, v y, v z 3 axi acceleration reference, a x, a y, a z There are extra requirement on thee reference All of the reference ignal mut be continuou The velocity and acceleration mut be limited. For the drone ued in thi module and for afety reaon, we limit v x, v, v 2 m/ and a, a, a 1.2 y z x y z m/ 2 EE4308 ~ 69

70 Trajectory generation for quadrotor drone The implet way for the vehicle to travel on the egment path i to generate velocity profile along that line egment. For example, red: velocity yellow: acceleration blue: poition It i to generate trajectorie for the vehicle to travel from A to D. For each line egment A B, B C, C D, a velocity and acceleration profile hown above ha to be generated. The problem with thi imple approach i that the drone will come to a full top at the end of each line egment, uch a point B and C. EE4308 ~ 70

71 Trajectory generation for quadrotor drone How can the drone fly from AD a fat a poible without top at the interim note? A poible olution i to witch to the next line egment before it goe into full top. Step 1: Line egment are generated, vehicle flying toward the end of the 1t line egment. Step 2: Before reaching the 1t end point to top, witch to travel on the next line egment. Step 3: By repeating the proce in Step 2, vehicle could reach it target in a moother way. The difficulty i on maintaining the continuity of the reference ignal for the entire path EE4308 ~ 71

72 How to generate mooth trajectorie? Trajectory generation technique uitable for quadrotor Acceleration limited time optimal olution Generate time minimal trajectory, under the velocity and acceleration contraint Jerk limited time optimal olution Time minimal trajectory, under the velocity, acceleration and jerk contraint Polynomial pline baed trajectory Generate high quality, energy optimized trajectory, but require the involving of numerical optimization Dynamic programming A earch baed trajectory generation method, very powerful for handling complex dynamic EE4308 ~ 72

73 Miion management State Machine Automata Deep learning? Miion Management Path Planning Trajectory Generation Outer Loop Control Inner Loop Control Meaured Signal EE4308 ~ 73

74 Framework of a miion management For a tak baed miion, we can ue a tree baed framework. The tak are organized into a tree and executed in a manner of depth firt traveral. Each leaf node tak contain only one ingle action. In other word, executing that leaf node tak i equivalent to executing the correponding action. Tak Tak Tak Tak Tak Tak Tak When an event occur, a new tak event handler i inerted to the current tree node a a ub tree. Some event require an immediate termination of miion after the event handler i Event Event Handler Tak executed uch a LAND once the battery i low. In that cae, the remaining tak Tak... Tak are removed from the tree accordingly. EE4308 ~ 74

75 EE4308 Part 2 project Navigating a drone through obtacle EE4308 ~ 75

76 Miion management for EE4308 project Miion: Navigate through obtacle and get to the goal poition. It can be decompoed into three ub tak, where each tak will correpond to one action. Tak 1: Take off TAKEOFF Tak 2: Go to goal POSITION Tak 3: Land LAND Tak name: Action: S1 Tak Take off Go to goal Land TAKEOFF POSITION LAND Action POSITION completed S2 Action TAKEOFF completed Action LAND completed S0 S3 EE4308 ~ 76

77 Project overview EE4308 ~ 77

78 Simulation and actual flight experiment Simulation for flight miion involved in the 2017 Singapore Amazing Flying Machine Competition Video taken during an actual flight demontration to BP EE4308 ~ 78

79 Acknowledgement Special thank to the whole NUS UAV Reearch Group and particularly to Lai Shupeng, Lan Menglu, Lin Feng, Li Kun, Phang Swee King, Tian Hongyu, Li Jiaxin, Wang Kangli for their help in preparing lecture material To YouTube and Wikipedia EE4308 ~ 79

80 That all, folk! Thank You! EE4308 ~ 80