A Path Planning Algorithm to Enable Well-Clear Low Altitude UAS Operation Beyond Visual Line of Sight

Transcription

1 A Path Planning Algorithm to Enable Well-Clear Low Altitude UAS Operation Beyond Visual Line of Sight Swee Balachandran National Institute of Aerospace, Hampton, VA Anthony Narkawicz, César Muñoz, María Consiglio NASA Langley Research Center, Hampton, VA

2 Outline Motivation Background Integrated system Results Conclusions 2

3 Motivation Off-the-shelf autopilot systems are highly capable, e.g., waypoint flight plan following, station keeping, geofence containment. Emerging applications require Unmanned Aerial Systems (UAS) to fly beyond visual line of sight missions. Require technologies to maintain separation between UAS while also enabling mission progress and satisfying geofence constraints. Two complementary approaches: UTM (off-board) vs onboard autonomy. 3

4 Related work - Separation assurance Several decades of research interest in airborne separation assurance. Original focus: prevent loss of separation between manned aircraft. Pilot plays pivotal role in the timely execution of maneuvers in accordance with suggested resolutions. Examples: TCAS-II, ACAS-X, DAIDALUS. Image: Wikipedia: 4

5 Related work - Geofencing Related work has mainly focused on preventing an autopilot or a remote pilot from violating fence boundaries. Typically involve a simple return to home maneuver without considerations on mission constraints. Examples: Safeguard: An Assured Safety Net Technology for UAS, Dill et al. Multi-Mode Guidance for an Independent Multi-Copter Geofencing System, Stevens et al. 5

6 Related work Path planning Flight planning: Optimal control formulation, e.g., Pontryagin s minimum principle, Dynamic programming. Geometric approach, e.g., Dubin s path. Discrete search methods, e.g., A*, Dijkstra. Probabilistic search methods, e.g., PRM, RRT. Complexity increases with dynamic environments. 6

7 Objective Requirements for autonomous operation: Avoid other air traffic in the airspace. Satisfy geofences and obstacle constraints. Decision making capability to return to mission or initiate replanning if necessary. Emphasis on formal verification. Computation speed. The primary contribution of this work is the integration of several previously developed formally verified tools to achieve the above functionality. 7

8 DAIDALUS Detect and Avoid Alerting Logic for Unmanned Aircraft. DAA reference implementation established by RTCA DO-365. Muñoz et al., DAIDALUS: Detect and Avoid Alerting Logic for Unmanned Systems, Proceedings of the 34th Digital Avionics Systems Conference (DASC 2015). (Figure is notional) 8

9 Detection Logic Detection logic determines the time interval of loss of well-clear (LoWC). (Figure is notional) 9

10 Maneuver Guidance Logic Maneuver guidance logic allows the pilot in control to maintain or recover well-clear status: Separation assurance bands, i.e., ranges of maneuvers that lead to intrusion in hazard volumes. Recovery bands, i.e., ranges of maneuvers that lead to well-clear recovery without intruding a protected volume. (a) Separation assurance bands (b) Recovery bands 10

11 PolyCARP PolyCARP is a library containing functions for polygon related computations. Formally verified using Prototype Verification System (PVS). Uses ray casting to determine if a given point is inside/outside a geofence: Outside, when even crossing. Inside, when odd crossing. Ray casting Narkawicz, A. and Hagen, G. E., Algorithms for collision detection between a point and a moving polygon, with applications to aircraft weather avoidance, 16th AIAA Aviation Technology, Integration, and Operations Conference,

12 Path planning Rapidly Exploring Random Trees (RRT). Build a tree of feasible paths. Sample the search space randomly. Grow tree towards the sampled node. Discard branches that lead to conflicts. RRT exploration 12

13 Problem Description 13

14 Data structures Aircraft state information Aircraft position in R 3. Aircraft velocity in R 3. List of traffic state information Traffic position in R 3. Traffic velocity in R 3. Parent node. List of children nodes. Each node is a snapshot of what the environment looks like if a branch was taken. 14

15 Problem setup Problem dynamics: X n+1 = f(x n, U n ). X = [o p, o v, t p, t v ]. U = v ref. 15

16 Constraint satisfaction Kinematic bands used to eliminate tree expansion in directions that lead to conflicts. The current node and the projected node are checked for traffic conflicts. Branches leading to conflict are discarded. 16

17 Early termination heuristic At each step, the algorithm checks to see if the direct path to goal is free from traffic and geofence constraints. Avoids unnecessary tree expansion. 17

18 Decision making Limited assumption. Uncertainty in traffic state measurement. 18

19 Results Encounter scenario 19

20 Computation time comparison Capability to dynamically construct flight plans to maneuver around other traffic and geofence. Computation on embedded devices that can be used by UAS. (a) Computation time on a beagle bone (1GHz ARM Cortex-A8) Iterations used Nodes explored Time taken (s) (b) Computation time on a Jetson TK1 (2.32GHz ARM quad-core Cortex-A15) Iterations used Nodes explored Time taken (s) Encounter Encounter Encounter Encounter Encounter Encounter

21 ICAROUS Implementation available in Java/C++ on Github under NASA s Open Source Agreement. Current version integrates with the ArduPilot flight stack. Provides ground station support for visualizing kinematic bands. ICAROUS is a high level decision making framework enabling autonomy. 21

22 Conclusions and future work Extended detect and avoid capability for low altitude UAS to account for other traffic and geofences. A local planner to quickly navigate around other UAS and geofences to continue with mission. Some parameters require tuning based on area of operation, mission speed, traffic speed, etc. Explore different sampling strategy to further speed up computation. Incorporate hover and wait maneuvers to let other traffic pass by before proceeding. Coordinating resolution among multiple aircraft. 22