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Oct. 13, 2011 1 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP Human-inspired Vehicle Navigation Through Fields of Safe Travel Karl Iagnemma (PI), Sterling.

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Presentation on theme: "Oct. 13, 2011 1 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP Human-inspired Vehicle Navigation Through Fields of Safe Travel Karl Iagnemma (PI), Sterling."— Presentation transcript:

1 Oct. 13, 2011 1 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP Human-inspired Vehicle Navigation Through Fields of Safe Travel Karl Iagnemma (PI), Sterling Anderson, Steve Peters

2 Oct. 12, 2011 2 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP MURI Research Program Context Outline ARO MURI (Iagnemma) DARPA M3 Program High Speed Teleoperation Semi-Autonomous Control High Speed Autonomous Control Terrain Estimation ARO MURI (Frazzoli) Efficient Kinodynamic Planning High Speed Autonomous Control ARO MURI (Tsiotras)

3 Oct. 12, 2011 3 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP Outline Introduction to Semi-Autonomous Control Need Problem Statement Constraint Planning Algorithm Development Objectives Constraint Planning Constraint Enforcement Experimental Implementation (DARPA M3 leverage) Setup Results Conclusions Outline

4 Oct. 12, 2011 4 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP The Need for Semi-Autonomous Planning and Control Passenger Vehicles (2008) 1 37,261 deaths 2.3 million injuries $230 Billion economic cost Manned Military Vehicles 2 248 deaths caused by ground vehicle crashes during Operation Iraqi Freedom Unmanned Military Vehicles 3 6-20 hour mean time between failure Industrial vehicles (forklifts, cranes, etc.) 4 900 deaths and 94,000 injuries from forklift- related accidents in USA (1990) Driver error the primary cause of accidents 5 Sole factor in 60% Contributing factor in 95% [1] National Highway Traffic Safety Administration, 2008 Traffic Safety Annual Assessment [2] Defense Manpower Data Center, Statistical Information Analysis Division, Military Casualty Summary: Global War on Terrorism, 2010 [3] J. Carlson and R. Murphy, “How UGVs physically fail in the field,” Robotics, IEEE Transactions on, vol. 21, 2005, pp. 423-437 [4] Industrial Forklift Truck Fatalities – A Summary, Report from Office of Data Analysis, OSHA, June 1990 [5] L. Evans, "The dominant role of driver behavior in traffic safety," American Journal of Public Health, v. 86, n. 6, pp. 784-786, 1996. Introduction Algorithm Development Experimental Implementation Conclusion Need Context

5 Oct. 12, 2011 5 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP The Need for Semi-Autonomous Planning and Control Forklift operation (in USA) 1 One in six of all workplace fatalities in US are forklift related Collisions with pedestrians 100 deaths, >20,000 injuries Other forklift-related accidents 900 deaths, >94,000 injuries [1] United States Department of Labor Occupational Safety & Health Administration, “Section 10 - X. Summary of the Final Economic Analysis, including the Regulatory Flexibility Analysis.” [2] Industrial Forklift Truck Fatalities – A Summary, Report from Office of Data Analysis, OSHA, June 1990 Introduction Algorithm Development Experimental Implementation Conclusion Need Context

6 Oct. 12, 2011 6 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP Problems With Manned Vehicle Control Cause# Reports Operator inattention59 Overturn53 Unstable load45 Operator struck by load37 Elevated employees26 No training19 Overload, improper use15 Improper equipment10 Obstructed view10 Carrying excess passenger9 Falling from platform or curb9 Other employee struck by load8 Falling from trailer6 Vehicle left in gear6 Speeding5 [1] Office of Electrical/Electronic and Mechanical Engineering Safety Standards, Directorate of Safety Standards Programs OSHA, 1990 Introduction Algorithm Development Experimental Implementation Conclusion Need Context

7 Oct. 12, 2011 7 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP Problems With Manned Vehicle Control Cause# Reports Operator inattention59 Overturn53 Unstable load45 Operator struck by load37 Elevated employees26 No training19 Overload, improper use15 Improper equipment10 Obstructed view10 Carrying excess passenger9 Falling from platform or curb9 Other employee struck by load8 Falling from trailer6 Vehicle left in gear6 Speeding5 [1] Office of Electrical/Electronic and Mechanical Engineering Safety Standards, Directorate of Safety Standards Programs OSHA, 1990 Introduction Algorithm Development Experimental Implementation Conclusion Need Context

8 Oct. 12, 2011 8 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP The Need for Semi-Autonomous Planning and Control Compelling reasons to keep humans “in the loop” Superior judgment and reasoning capabilities 1,2 High automation costs Significant socioeconomic pressures Technical challenges How to define safe operating region for systems? Constraint definition in Cartesian space, state space, input space How to safely share control? Threat assessment to determine when, how much to intervene [1] Fitts, P.M., et al. “Human engineering for an effective air navigation and traffic control system”. Washington, DC: National Research Council, 1951. [2] Sheridan, T. “Computer control and human alienation”. Technology Review, 1980, pp. 10,61-73. Introduction Algorithm Development Experimental Implementation Conclusion Need Context

9 Oct. 12, 2011 9 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP Traditional Approaches Traditional vehicle navigation approaches are path-based Plan path around hazards Cost-based Track path with low-level controller Reasonable for autonomous control Path-based approaches are not well suited for semi-autonomous operation Restricting motion to specific paths can result in overly restrictive intervention Humans do not follow paths Operate within “field of safe travel” Paths within field can have equal “goodness” [1] Leonard et al., 2008 Besselmann and Morari, 2008 Keviczky, Falcone, et al., 2007 Borrelli, Falcone, et al., 2005 [2] Keifer et al., 2005 Brunson et al., 2002 Engelman et al., 2006 Introduction Algorithm Development Experimental Implementation Conclusion Need Context f(s2)=k2 f(s1)=k1 f(s3)=k3

10 Oct. 12, 2011 10 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP Traditional Approaches Introduction Algorithm Development Experimental Implementation Conclusion Need Context [1] J. J. Gibson and L. E. Crooks, “A Theoretical Field-Analysis of Automobile-Driving,” The American Journal of Psychology, vol. 51, no. 3, pp. 453-471, Jul. 1938.

11 Oct. 12, 2011 11 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP Constraint-Based Semi-Autonomous Control Objective Guaranteed safety in high-speed vehicle operation in challenging environments through semi-autonomous planning and control Key Insight Human operators tend to navigate within fields of safe travel Paths within field can have equal “goodness” Approach Manage driver/machine interaction through the planning and selective application of constraints Define constraint set over some horizon Identify field(s) of safe travel Characterize goodness of constraint set Selectively enforce constraints based on threat Allow driver freedom to choose specific path Requirements Constraint planner Threat assessor Semi-autonomous intervention mechanism Objectives Constraint Planning Constraint Enforcement Introduction Algorithm Development Experimental Implementation Conclusion

12 Oct. 12, 2011 12 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP Constraint Planning Constraint planning objective: identify, characterize, and select a single “best” constrained region Spatial constraints (from environment) Velocity constraints (due to speed limits, power limitations, etc.) Stability constraints (from vehicle dynamics and environmental interaction) Input constraints (from actuator limits) Challenges Planning in “constraint space” requires a fundamentally new set of tools Path-centric metrics such as length, curvature, etc. ill-defined in the context of a constraint space Candidate constraint sets may contain an infinite set of feasible paths Characterizing a given constraint space may require a bulk evaluation of these paths Objectives Constraint Planning Constraint Enforcement Introduction Algorithm Development Experimental Implementation Conclusion

13 Oct. 12, 2011 13 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP Constraint Planning “Minimally-restrictive” semi-autonomous avoidance of both static and dynamic hazards: State constraints (from vehicle dynamics and environmental interaction) Input constraints (from actuator limits) Objectives Constraint Planning Constraint Enforcement s.t. Introduction Algorithm Development Experimental Implementation Conclusion

14 Oct. 12, 2011 14 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP Naïve Approach: Path-Based Constraint Planning Method Plan a feasible path through environment Expand path to encompass the homotopic class to which it belongs Homotopic Class: Set of paths that can be continuously deformed into one another Advantages Computationally feasible Constraints admit at least one feasible path Problems “Best” homotopy class may not contain optimal path Implications for both semi-autonomous and autonomous control Homotopic class goodness difficult to assess Relationship between class properties and vehicle dynamics difficult to define Robustness/feasibility of homotopic class difficult to quantify Objectives Constraint Planning Constraint Enforcement Introduction Algorithm Development Experimental Implementation Conclusion

15 Oct. 12, 2011 15 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP Challenge Efficient identification and characterization of available homotopic classes Approach Partition environment into cells Constrained Delaunay Triangulation Identify canonical path linking contiguous cells Define homotopic class constraints from sequences of contiguous cells Characterize “goodness” of each cell “Length” and “width” from canonical path Dynamic reachability across adjacent edges Constraint Planning Based on Homotopic Classes Objectives Constraint Planning Constraint Enforcement Obstacles Generalized Voronoi Diagram Delaunay Triangulation Canonical path Chosen Corridor Introduction Algorithm Development Experimental Implementation Conclusion

16 Oct. 12, 2011 16 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP Coarse method for constraint set characterization Key idea: Maximum speed and input freedom within a given corridor related to: Corridor width Required heading changes (  path curvature) Constraint set goodness also driven by approximate “length” of corridor Result: Dijkstra on cost function: Triangulation-Based Homotopy Characterization – Coarse Method w2w2 w3w3 w1w1 L2L2 L1L1 L3L3 ϕ1ϕ1 ϕ2ϕ2 ϕ3ϕ3 L0L0 L4L4 Objectives Constraint Planning Constraint Enforcement Introduction Algorithm Development Experimental Implementation Conclusion

17 Oct. 12, 2011 17 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP Coarse Constraint Characterization in Semi-Autonomous Application Objectives Constraint Planning Constraint Enforcement Introduction Algorithm Development Experimental Implementation Conclusion

18 Oct. 12, 2011 18 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP Fine method for constraint set characterization Key idea: Employ reachability analysis for homotopic class characterization Reachability analysis yields metrics on available inputs Two candidate metrics: “Restrictiveness” of input constraints at time t 0 Restrictiveness of input constraints “in aggregate” through homotopy At time t 0, ΔSteer 1 (t 0 ) > ΔSteer 2 (t 0 ) Homotopy 1 contains paths that require less aggressive steering commands Homotopy 2 in aggregate provides greater control freedom Triangulation-Based Homotopy Characterization – Fine Method ΔSteer 2 (t 0 ) ΔSteer 1 (t 0 ) Objectives Constraint Planning Constraint Enforcement Introduction Algorithm Development Experimental Implementation Conclusion

19 Oct. 12, 2011 19 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP Goal x3x3 ψ a b ψ max ψ min 180 90 Constraint Characterization: Transition Feasibility & Reachable Sets Objectives Constraint Planning Constraint Enforcement Introduction Algorithm Development Experimental Implementation Conclusion Transition feasibility Any path existing within a given homotopy described by a sequence of triangular cells must traverse from one unconstrained edge to the other without colliding with the constrained edge Any homotopic path that successfully reaches the goal from the vehicle’s current position must be (forward) reachable from the vehicle, and (backward) reachable from the goal Characterizing the “restrictiveness” of a given homotopy may be possible via an aggregate assessment of admissible transitions between triangles

20 Oct. 12, 2011 20 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP Constraint Characterization: Transition Feasibility & Reachable Sets Goal x3x3 ψ a b ψ max ψ min 180 90 Reachable sets a function of triangle dimensions and adjacency relations  Efficient set propagation  Close approximation of state and control “restrictiveness” of a given homotopic class Homotopy “restrictiveness” metric may be used in constraint planning objective Objectives Constraint Planning Constraint Enforcement Introduction Algorithm Development Experimental Implementation Conclusion

21 Oct. 12, 2011 21 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP Control Input Constraints and Threat Assessment Identify constrained input range from homotopic class constraints Identify current optimal control input from with constrained MPC Associated with optimal trajectory “Threat level” computed from optimal trajectory Describes severity of best-case trajectory in given homotopy Various potential metrics computed over optimal trajectory Maximum vehicle roll angle, RMS of tire forces, etc. Objectives Constraint Planning Constraint Enforcement Introduction Algorithm Development Experimental Implementation Conclusion

22 Oct. 12, 2011 22 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP Steering Position ksks Optimal Input Physical Limits Restoring Torque (T) Semi-Autonomous Constraint Enforcement Objectives Constraint Planning Constraint Enforcement Introduction Algorithm Development Experimental Implementation Conclusion Wheel resistance a function of predicted threat  Torque constraints on operator tighten as threat (i.e. need for intervention) increases

23 Oct. 12, 2011 23 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP Semi-Autonomous Constraint Enforcement Steering Position Objectives Constraint Planning Constraint Enforcement Introduction Algorithm Development Experimental Implementation Conclusion ΔSteer 2 (t 0 ) ΔSteer 1 (t 0 ) Road Edge Lane Edge Obstacle Avoidance Stability Limit Restoring Torque (T) Physical Limits Wheel resistance enforces steering constraints required to remain within safe homotopy Input constraints (Δ min and Δ max ) on operator tighten to ensure vehicle remains within safe set Overlapping region allows driver to bias toward desired homotopy

24 Oct. 12, 2011 24 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP DARPA M3 Program Collaboration DARPA M3 Program (DSO, PM: Gill Pratt) Focus: High speed vehicle teleoperation Collaboration with Quantum Signal, LLC 9+ acre property in Saline, MI Various terrain types Off road: scrub grass, tall grass, meadow, some culverts Semi-prepared roads: smooth gravel, rough gravel Prepared roads Varying slopes None, moderate (2-10 deg), severe (10-45 deg) Varying roughness Obstacles - trees, mud, sloped terrain, barrels, structures, etc Setup Results Introduction Algorithm Development Experimental Implementation Conclusion

25 Oct. 12, 2011 25 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP UGV Platform Hardware (Kawasaki Mule) Sensing: omnidirectional video head, NavCom 2050 GPS, Velodyne LIDAR Onboard Linux PC and processor and control unit run sensing, constraint planning, and controller code NLOS teleoperation via forward-looking imagery with significant comms latency Setup Results Introduction Algorithm Development Experimental Implementation Conclusion

26 Oct. 12, 2011 26 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP Test Setup Teleoperation in a field of obstacles Off-road (scrub grass, tall grass, meadow) Gravel roads (smooth & rough) Paved roads & parking lot Varying slopes and roughness Obstacles field Shrubs & plastic barrels start goal Setup Results Introduction Algorithm Development Experimental Implementation Conclusion

27 Oct. 12, 2011 27 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP Experimental Results Constraint planner designs constraints to avoid sensed obstacles & vehicle instability Semi-autonomous controller ensures vehicle trajectory satisfies constraints Optimal maneuver identified to characterize scenario threat Position constraints designed to avoid hazards Setup Results Introduction Algorithm Development Experimental Implementation Conclusion

28 Oct. 12, 2011 28 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP Experimental Results Setup Results Introduction Algorithm Development Experimental Implementation Conclusion

29 Oct. 12, 2011 29 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP Conclusions Introduction Algorithm Development Experimental Implementation Conclusion

30 Oct. 12, 2011 30 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP Conclusions Algorithmic Advances Homotopy-based constraint planning Model-based threat assessment Constraint-based semi-autonomy Experimental Achievements Seamless integration of operator commands with controller corrections 100% safe obstacle avoidance and stability maintenance in the presence of: Operator error Loss of communications Loss of operator attention High communication latency Plans going forward Bring new constraint planning tools to bear Explore alternative force-feedback-based constraint enforcement mechanisms Introduction Algorithm Development Experimental Implementation Conclusion

31 Oct. 12, 2011 31 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP Conclusions Conference Papers—Submitted, published, and in preparation Anderson, S.J, Peters, S.C., Overholt, J., Iagnemma, K.D., “Semi-Autonomous Stability Control and Hazard Avoidance for Manned and Unmanned Ground Vehicles", Proc. 27th Army Science Conference, 2010. Peters, S., Frazzoli, E., and Iagnemma, K., “Differential Flatness of a Vehicle with Tire Force Control,” Proceedings of the IEEE International Conference on Robots and Systems, September 2011, pp. 298-304 Journal Papers—Submitted, published, and in preparation Anderson, S.J, Peters, S.C., Pilutti, T.P., Iagnemma, K.D., “An Optimal-Control-Based Framework for Trajectory Planning, Threat Assessment, and Semi-Autonomous Control of Passenger Vehicles in Hazard Avoidance Scenarios,” Intl Journal of Vehicle Autonomous Systems, Vol. 8, Nos. 2/3/4, pp.190-216. Arndt, D., Bobrow, J., Peters, S., Iagnemma, K., and Dubowsky, S., “Two-Wheel Self-Balancing of a Four- Wheeled Vehicle,” IEEE Control Systems Magazine, Vol. 31, No. 2, pp. 29-37, April 2011 Peters, S., Frazzoli, E., and Iagnemma, K., “Differential Flatness of a Front-Steered Vehicle with Tire Force Control,” Automatica, in preparation Peters, S., Frazzoli, E., and Iagnemma, K., “Yaw stability analysis for vehicle control with front, rear, and all-wheel-drive configurations,” Vehicle System Dynamics, in preparation Peters, S., and Iagnemma, K., “Optimal avoidance maneuvers for a point mass with acceleration circle constraint,” Automatica, in preparation PhD Theses—Completed and in preparation Peters, S., “Steve’s Award-Winning PhD Thesis,” MIT, expected completion 2011 Anderson, S., “Sterling’s Award-Winning PhD Thesis,” MIT, expected completion 2012 Introduction Algorithm Development Experimental Implementation Conclusion

32 Oct. 12, 2011 32 Iagnemma, Anderson, Peters ROBOTIC MOBILITY GROUP Flatness-based Control of High Speed Vehicles


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