cc } For each vehicle 8 Get Current Position 5 Fer each action seq uence n For each Predicted Vehicle Trajectory Accelaabor /Decelambor € Probable Vehicle Collision > Changing la
Trang 1
(cc } For each vehicle
(8) Get Current Position
(5) Fer each action seq uence (n) For each Predicted Vehicle Trajectory
Accelaabor /Decelambor € Probable Vehicle Collision >
Changing lames
yes
`
Cost Collision
Cost Static Obstacle
(c) Probabilities Acton lá
Fig 27 Moving object prediction process
The algorithms are used to predict the future location of moving objects in the environment at longer time planning horizons on the order of tens of seconds into the future with plan steps at about one second intervals
At
The steps within the algorithm shown in Fig 27 are:
For each vehicle on the road (a), the algorithm gets the current position and velocity of the vehicle by querying external programs/sensors (/3)
For each set of possible future actions (6), the algorithm creates a set of next possible positions and assigns an overall cost to each action based upon the cost incurred by performing the action and the cost incurred based upon the vehicle’s proximity to static objects An underlying cost model is developed to represent these costs
Based upon the costs determined in Step 2, the algorithm computes the probability for each action the vehicle may perform (e)
Predicted Vehicle Trajectories (PVT) (€) are built for each vehicle which will be used to evaluate the possibility of collision with other vehicles in the environment PVTs are a vector that indicates the possible paths that a vehicle will take within a predetermined number of time steps into the future For each pair of PVTs (7), the algorithm checks if a possible collision will occur (where PVTs intersect) and assigns a cost if collision is expected In this step, the probabilities of the individual actions (@) are recalculated, incorporating the risk of collision with other moving objects
the end of the main loop, the future positions with the highest probabilities for each vehicle represent the most likely location of where the vehicles will be in the future More information about the cost-based probabilistic prediction algorithms can be found in [35]
4.3 Industrial Automated Guided Vehicles
Study of Next Generation Manufacturing Vehicles
This effort, called the Industrial Autonomous Vehicles (IAV) Project, aims to provide industries with stan- dards, performance measurements, and infrastructure technology needs for the material handling industry
Trang 2The NIST ISD have been working with the material handling industry, specifically on automated guided vehicles (AGVs), to develop next generation vehicles A few example accomplishments in this area include: determining the high impact areas according to the AGV industry, partnering with an AGV vendor to demonstrate pallets visuali2 ation using LADAR towards autonomous truck unloading, and demonstrating autonomous vehicle navigation through unstructured facilities Here, we briefly explain each of these points
Generation After Next AGV
to enhance current AGY systems and reduce costs are being addressed by AGV vendors, the study looks beyond today’s issues to identify needed technology breakthroughs that could open new markets and improve
US manufacturing productivity Results of this study are described in [36]
Within the survey and high on the list, AGV vendors look to the future
navigation in unstructured environments, onboard vehicle processing, 3D imaging sensors, and transfer of
for: reduced vehicle costs, advanced technology developed for Department of Defense Current AGVs are “guided” by wire, laser or other means, operate in structured environments tailored to the vehicle, have virtually no 3D sensing and operate from a host computer with limited onboard-vehicle control
Visualizing Pallets
Targeting the high impact area of using 3D imaging sensors on AGYV, NIST ISD teamed with Transbotics, an AGY vendor, to visualize pallets using panned line-scan LADAR, towards autonomous truck unloading [37]
A cooperative agreement between NIST and Transbotics allowed NIST to: (1) set up mock pallets, conveyer and truck loading on a loading deck, (2) to develop software to visualize pallets, the conveyer and the truck
in 3D space, and (3) verify if the pallet, conveyor and truck are in their expected location with respect to the AGV The project was successful on mock components used at NIST and software was transferred to Transbotics for implementation on their AGV towards use in a production favility
Navigation Through Unstructured Facilities
Also targeting a high impact AGY industry requested area, the (OMS Program has been transferring tech- nology from defense mobility projects through its [AV Project to the AGV industry By focusing on AGV mee related challenges, for example autonomous vehicle navigation through unstructured facilities 138],
he TAV project attempts to provide improved AGV capabilities to do more than point-to-point, pa art pick- up/delivery operations For example, AGV could avoid obstacles and people in the vehicle path, adapt
to facilities instead of vice versa, navigate both indoors and outdoors using the same adaptable absolute vehicie position software modules - all towards doing more with end users’ vehicle capital investments and developing niche markets
outdoor vehicle application to an indoor industrial setting Two REID sensors, batteries, laptop, and network hub were added, Active RFID sensors were integrated into the venicle position estimate Also, a passive RFID system was used including tags that provide 8, more accurate vehicle position to within a few centimeters
ot system sof
RFID systems updates replaced the outdoor GPS positioning system updates in the controller
The control system also needed to be |
indoors, negotiate tighter corners than are typicaily encountered outdoors, display facility maps and expected
ess aggressive for safety of people and equipment, use stereo vision paths (see Fig 28), and many other modifications detailed in [38] The demonstration was successful and
navigate
Fiture research will include integration of a 2D safety sensor to eliminate false positives on obstacles near ground level caused by low stereo disparity Demonstration of controlling more than one oteliwont ‘vehicle
at a time in the same unstructured environment along with other moving obstacles is also planned
Trang 3
Ga NẴY close 10 Ons tAcLE to WFO PASSIVE om [Coker Leman | Emacs | Planner | Left | Pag File View Settings Map
OCU ght #1411 teh #1479
lí
Fig 28 LAGR AGV Graphical Displays — right and left stereo images (upper left); images overlaid with stereo
obstacle (red) and floor (green) detection and 2D scanner obstacle detection (purple) (middle left); right and left cost
maps (lower left); low level map (upper right); and high level map (lower right)
5 Conclusions and Continuing Work
The field of autonomous vehicles has grown tremendously over the past few years This is perhaps most evi- dent by the performance of these vehicles in the DARPA-sponsored Grand Challenge events which occurred
in 2004, 2005 and most recently in 2007 [39] The purpose of the DARPA Grand Challenge was to develop autonomous vehicle technologies that can be applied to military tasks, notably robotic “mules” or troop supply vehicles The Grand Challenge courses gradually got harder, with the most recent event incorporat- ing moving on-road objects in the urban environment The 2007 Challenge turned out to have more civilian focus than military’s, with the DARPA officials and many teams emphasizing safe robotic driving as a very important objective The performance of the vehicles improved tremendously from 2004 to 2007, even as the environment got more difficult This is in part due to the advancement of technologies that are being explored
as part of the ICMS program The ICMS Program and its development of 4D/RCS has been ongoing for nearly 30 years with the goal to provide architectures and interface standards, performance test methods and data, and infrastructure technology needed by US manufacturing industry and government agencies in developing and applying intelligent control technology to mobility systems to reduce cost, improve safety, and save lives
The 4D/RCS has been the standard intelligent control architecture on many of the Defense, Learning, and Industry Projects providing application to respective real world issues The Transportation Project provides performance analysis of the latest mobile system sensor advancements And the Research and Engineering Projects allow autonomy capabilities to be defined along with simulation and prediction efforts for mobile robots
Trang 4Future [CMS eHorts will focus deeper into these projects with even more autonomous capabilities Broader applications to robots supporting humans in manufacturing, construction, and farming are expected once major key intelli
ent mobility elements in perception and control are solved
References
it Albus, J.S., Huang, H-M., Messina, E., Murphy, K., Juberts, M., Lacaze, A., Balakirsky, 5., Shneier, M.O.,
10
Ti ay
14
3
Hong, T., Scott, H., Horst, J., Proctor, F , Shackle ‘ford, W., Szabo, S., and Finkelstein, R., 4 4D/RCS S Version 2.0:
A Reference Model Architecture for Unmanned Vet stems, NIST, Gaithersburg, MD, NISTIR 6912, 2002 Balakirsky, $., Messina, E., Albus, J.S., Architecting a Simulation and Development Environment for Multi-Rebot Teams, Proceed tings of the International Workshop on Multi Robct Systems, 2002
Balakirsky, 3.B., Chang, T., Hong, T.H., Messina, E., Shneier, M.O., A Hierarchical World Model for an Autonomous Scout Vehicle, Proceedings of the SPTE 16th Annual International Symp on Aerospace /Defense Sensing, Simulation, and Controls, Orlando, PL, April 1-5, 2062
Albus, J.S., Juberts, M., Szabo, $., RCS: A Reference Model Architecture for Intelligent Vehicle and High- way Systems, Proceedings of the 25th Silver Jubilee International Symposium on Automotive Technclogy and Automation, Florence, Italy, June 1-5, 1992
Bostelman, R.V., Jacoff, A., Dagalakis, N.G., Albus, JS » ROS-Based RoboCrane Integration, Proceedings of the International Conference on Intelligent Systems: A Se Perspective, Gaithersburg MD, October 20-23, 1996 Madhavan, R., Messina, E., and Afbus, J Guditors), Low-Level Autonomous Mobility Implementation part of Chapter 3: Behavior Generation in the book, Intelligent Vehicle Systems: A 4D/RCS Approach, 2007 Jackel, Larry, LAGR, Mission, http://www-.darpa.mil /ipto/ programs /lagr /index.htm, DARPA Information Pro- cessing Technology Office
Albus, J., Bostelman, R., Chang, T., Hong, T., Shackleford, W., and Shneier, M., 2006 Learning in a Hierarchical Control System: [4D/RCS in the DARPA LAGR Pro ›gram Journal of Field Robotics, Special Issue on Learning
in Unstructured Environments, 23(11/12): 975-1003
Konolige, K., 5RI Stereo Engine, http: / /www.ai.sri.com/-~konolige/svs/
Tan, C., Hong, T., Shneier, M., and Chang, T., Color Model-Based Real-Time Learning for Road Following, in Proceedings of the {ERE Intelligent Transportation Systems Conference (Submitted) Toronto, Canada, 2006 Shneier, M., Chang, T., Hong, T., Shackleford, W., Bostelman, R., and Albus, J S Learning traversability models for autonomous mobile vehicles Autonomous Robots 24, 1, January 2008, 69-86
Oskard, D., Hong, T., Shaffer, C., Real-time Algorithms and Data Structures for Underwater Mapping, Proceedings of the SPITE Advances in {intelligent Robotics Systems Conference, Boston, MA, November, 1988
NMLepp html
Beyes-Jones, J., A* algorithm tutorial, http://us.geocities.com/jheyesjones/astar btm
Tan, C., Hong, T., Shneier, M., Chang, T., Color Model-Based Real-Time Learning for Road Following, Proceedings of the TERE Intelligent ° Transportation Systems Conference, 2006
He, Y., Wang, H., Zhang, B., Color-based road detection in urban traffic scenes, IEEE: Transactions on Intelligent
Kristensen, D., Autonomous Road Following PhD thesis, KTH Royal Institute of Technology, Stockholm, Sweden, 2004
Lin, X., Chen, S., Color image segmentation using modified ASI system for road following, TERE Intemational Conference on Robotics and Automation, 1991, pp 1998-2003
Ulrich, 1, Nourbakhsh, 1., Appearance-Based Obstacle Detection with Monocular Color Vision, Proceedings of the AAAT National Conference on Artificial Intelligence, 2000
Shneier, M., Bostelman, R., Albus, J.S., Shackleford, W., Chang, T., Hong, T., A Common Operator Control Unit Color Scheme for Mobile Robots, National Institute of Standards and Technology, Gaithersburg, MD, August, 2007
Huang, H-M., The Autonomy Levels for Unmanned Systems (ALFUS) Framework-Interim Results, in Pestormance Metrics for fatelligent Systems (PerMIS) Workshop, Caithersbure, Maryland, 2006
Huang, H-M et al., Characterizing Unmanned System Autonomy: Contextual Autonomous Capability and Level
of Autonomy Analyses, in Proceedings of the SPTE Defense and Security Symposium, April, 2007
Huang, H-M ed., Autonomy Levels for Unmanned Systems (ALFUS) Framework, Volume I: Terminology, NIST Special Publication 1011, Gaithersburg: National Institute of Standards and Technology, 2004
€
Trang 5
341 The OWL Services Coalition, “OWL-S 1.0 Release,” http://www.darmlLorg/services/owl-s/1.0/owl-s.pdf, 2003
25 Schienoff, C., Washington, R., and Barbera, T., Experiences in Developing an Intelligent Ground Vehicle IGV} Ontology in Protege, Proceedings of the 7th International Protege Conference, Bethesda, MD, 2004
26 http: //wuw.cts dot gov/iobss
27 Szabo, $., Wilson, B., Application of a Crash Prevention Boundary Metric to a Road Departure Warning System Proceedings of the Performance Metrics for Intelligent Systems (PerMIS) Workshop, NIST, Gaithersburg, MD, August, 24-26, 2004 http: //www.isd.mel nist.gov /documents/szabo/PerMIS04.pdf
28 Aitp: //wuw.isd.mel nist gou/projects /autonomy_levels/
29 Balakirsky, 5., Scrapper, ©., Carpin, S., and Lewis, M., USARSim: Providing a Framework for Multi robot Performance Evaluation, Proceedings of the Performance Metrics for Intelligent Systems (PerM{S) Workshop, 2006
30 Scrapper, C., Balakirsky, $., and Messina, E., MOAST and USARSim - A Combined Framework for the
Systems, S SPIE 2006 Defense and Security Symposium, 2006
1 USARSim Homepage htip://usarsim sourceforge.net/, 2007
32 MOAST Homepage hitp:///sourceforge.net/projects/moast/, 2007
33 Dickmanns, E.D., A General Dynamic Vision Architecture for UGV and UAV, Journal of Applied Intelligence,
3, 251, 1992
34 Schienoff, C., Ajot, J., and Madhavan, R., PRIDE: A Framework for Performance Evahiation of Intelligent
Vehicles in Dynamic, On-Road Environments, Proceedings of the Performance Metrics tor Intelligent Systems
(PerMIS) 2004 Works shop, 2004
35 Madhavan, R and Schlenoff, C., The Effect of Process Models on Short-term Prediction of Moving Objects for Autonomous Driving, International Journal of Control, Automation and Systems, 3, 509-523, 2005
36 Bishop, R., Industrial Autonomous Vehicles: Results of a Vendor Survey of Technology Needs, Bishop Consulting, February 16, 2006
37 Bostelman, R., Hong, T., Chang, 'T., Visualization of Pallets, Proceedings of SPME Optics East 2006 Conference, Boston, MA, USA, October 1-4, 2006
38 Bostetman, R., Hong, T., Chang, T., Shackletord, W., Shneier, M., Unstructured Facility Navigation by Applying the NIST 4D/RCS Architecture, CITSA 06 Conference Proceedings, July 20-22, 2006
39 Tagnemma, K., Buehler, M., Special Issues on the DARPA Grand Challenge, Journal of Field Robotics, Volime
23, Issues 8 & 9, Pages 461-835, Aug/Sept 2006
Development and Testing of Autonomous
Trang 6Index
4D/RCS, 241, 256, 258, 261, 269
Active Appearance Models, 33
Adaptive boosting (AdaBoost), 9, 10, 59, 68, 70, 210
Adaptive DWE, 15
APR Control, 146, 148, 149, i51, 155, 160, 164, 165
AFR Prediction, 149, 155, 159, 162
Agent architecture, 239
Agent control module, 239, 241, 243
Air intake subsystem (AES), 194, 197
Aiv-fuel ratio (AFR), 145, 165
Air-intake system, 196, 198
Aivpath
control, 131, 132
in-cylinder air mass obse ; 135
manifold pressure, 137
recirculated gas mass, 1 133, 134
residual gases, 138
volumetric efficiency, 134
ALOPEX, 114
Analysis of variance, 10
Annotation tool, 40, 42, 43, 47, 56
Anti-lock braking system (ABS), 194, 212
Automotive suspension system, 194, 204
Autonomous agent, 239
Backpropagation through time (BPTT)
truncated, 111
Bayesian network, 80, 84
Blink frequency, 21, 26, 27, 28
Branch and bound, 59, 90, 94, 97-99
Charafer matching, 61, 63
Classifier fusion techniques, 208
Cognitive workload, 1
Computer vision, 21, 33
Constrained Local Models, 34
Control hierarchy, 241, 242
ition, LO
Cross valic
, 8, 10-14, 44-46, 210
Decision tree © aning, 8, 15
Decision tree
Diagnostic matrix (D-raatrix), 199, 200
Diagnostic tree, 200 Direct Control System, 151
Direct Inverse Model (DIM), 151, 15%
Distraction, 19 cognitive distraction, 19, 26, 28, 32
visual distraction, 19, 26
Driver assistance, 39, 40, 50, 56, 57, 68 Driver inatter tion, 19, 20, 26, 40, 50-52, 56, 57 detection, 50, 52, 56
dviver inattentiveness level, 29 Driver support, 59
Driver workload estimation (DWE), 1-3, 10 Driving activity, 48
Driving Patterns, 169, 173, 184
vity, 39, 42, 56 Dynamic fusion, 210
Driving /driver ac
Dynamic Programraing, 169, 171, 174, 176, 188 Dynamic resistance approach, 222
Dynamic resistance protile, 223, 224, 226, 227, 234
Embodied agent, 267 Engine
actuators, 125 control, 125, 131 common features, 125 development cycle, 127 downsizing, 131 Spark Ignition (SD —, 18 turbocharging, 131 Error-correcting output codes (ACOC},
Extended kalman filter (EEF), 196
Bye closure duration, 29
210
Face pose, 21, 26, 27, 29, 36 Fatigue, 20, 21, 26-28, 30, 31 Fault detection and diagnosis (PDD), 191 Fixed gaze, 26, 28-30, 32, 36
Four Dimensional (3D + time) /Real-time Control System (4D /RCS), 237
Fuzzy logic, 174, 175, 220, 227, 229, 234
Trang 7"ugazy rules, 174, 175, 180, 182, 229, 230
Fuzzy system, 22, 29
Graphical models, 79, 80, 88
Grey box approach, 127
Hardware-in-the-loop, 191
Hessian matrix, 113
HIL5S, 191, 196
Hybrid Vehicle, 169, 173, 176
Hypervariate, 39
Image acquisition, 21
Indirect Control Systera, 151, 153
Intelligent constant current control, 220, 226, 227,
230-234
Tutelligent embodied agents, 267
Intelligent vehicle, 239, 271
Internal Model Control UMC), 154
K-Nearest Neighbor (INN), 207
Kalman filter, 21, 25, 28, 32
extended CHKF), 118, 114, 116
multi-stream, 114
non-ditferential or nonlinear, 115
Kernel function, 130
Knowledge discovery, 89
LAGR, 256, 259, 261, 274
Lane tracking, 36
Learning Applied to Ground Robots (LAGR}, 238, 255
Learning machines, 125
Learning rate, 114
Learning vector quantization (LYQ), 185, 220, 223-226,
228, 234
Learning-based DWE design process, 4
Linear parameter varying (LPV) system, 136
Maneuver, i, 4, 39, 40, 42, 46, 47, 52, 56, 72
classification, 46, 47
detection, 42, 46, 47
Manufacturing process optimization, 89, 92, 94, 99
Markov network, 80, 81
Micro-camera, 21
Multi-agent simulation environment, 268
Multi-agent system, 264
Multi-way partial least squares (MPLS), 206, 207
Multilayer perceptron (MLP}, 102, 103, 126, 128
Near-IR, 21, 22, 24, 33
Neural network, 69, 178, 179, 185
controfier, 106, 108, 110, 112
in engine control, 126
models, 103, 116, 128
Neuro-fuzzy inference system, 222 Nodding, 20, 29
Observer, 103, 127, 132-185, 187, 147, 149, 195 polytopic, 125, 134, 136
Output error (prediction-error) method, 195
Partial least squares (PLS), 211 Particle swarm optimization (PSO), 115 PERCLOS, 21, 26, 27, 29, 30, 32, 33, 36
Prior knowledge, 127, 139
Process capability index, 89, 90, 53, 96, 99 Prognostic model, 202
Prognostics, 201 Pupil detection, 24
Racial basis function (RBF)
kernel, 130, 139 networks, 1038, 104, 126, 129, 130 Random Forest, 45-48, 54-56 Real-rime recurrent learning (RTRL), 111 Recurrent Neural Network (RNN), 101, 162, 105, 109,
111, 116, 146, 149-151, 153, 153, 165 Remaining useful life, 193
Residual, 199-201, 203 Resistance spot welding, 219, 220, 222, 234 Root cause analysis, 89, 90, 94, 96 Rule extraction, 89, 90, 92, 99
Sensor selection, 40, 47, 48, 50 Simultaneous Perturbation Stochastic Approximation (SPSA), 115
Soft (indirect) sensor, 227 Soft sensing, 222, 228, 230, 231, 234 Soft sensor, 103, 127, 220, 227, 234
Stochastic Meta-Descent (SMD), 114, 115
Support vector machine regression (SVMR}, 61, 68, 69,
129, 138, 207, 211
Traffic accidents, 19
Variable camshaft timing (VCT), 131, 132, 138 Vehicle Power Managernent, 169, 171, 180, 184, 185, 188
Virtual or soft (indirect) sensor, 220
Virtual sensing, 149, 155 Virtual sensor, 101, 1023-106, 164, 166 Visual behaviors, 20, 21, 26-28, 32 Visualization, 80, 84-86
Weight update method, 111, 112 first-order, 112
second-order, 113 Workload management, 1, 39, 40