A Path Planning Algorithm to Enable Well-Clear Low...
Transcript of A Path Planning Algorithm to Enable Well-Clear Low...
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
Outline
• Motivation
• Background
• Integrated system
• Results
• Conclusions
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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.
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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.
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Image: Wikipedia: https://en.wikipedia.org/wiki/Traffic_collision_avoidance_system
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.
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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.
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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.
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DAIDALUS
• Detect and Avoid Alerting
Logic for Unmanned Aircraft.
• DAA reference
implementation established
by RTCA DO-365.
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(Figure is notional)
Muñoz et al., DAIDALUS: Detect and Avoid Alerting Logic for Unmanned Systems, Proceedings of the 34th Digital Avionics Systems Conference (DASC 2015).
Detection Logic
Detection logic determines the time interval of loss
of well-clear (LoWC).
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(Figure is notional)
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.
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(a) Separation assurance bands (b) Recovery bands
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.
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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, 2016.
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.
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RRT exploration
Problem Description
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Data structures
• Aircraft state information
– Aircraft position in ℝ3.
– Aircraft velocity in ℝ3.
• List of traffic state information
– Traffic position in ℝ3.
– Traffic velocity in ℝ3.
• Parent node.
• List of children nodes.
• Each node is a snapshot of what the environment looks
like if a branch was taken.
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Problem setup
Problem dynamics:
• 𝑋𝑛+1 = 𝑓(𝑋𝑛, 𝑈𝑛).
• 𝑋 = [𝑜𝑝, 𝑜𝑣, 𝑡𝑝, 𝑡𝑣].
• 𝑈 = 𝑣𝑟𝑒𝑓.
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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.
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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.
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Decision making
• Limited assumption.
• Uncertainty in traffic state measurement.
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Results
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Encounter scenario
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.
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Iterations used Nodes explored Time taken (s)
Encounter 1 5 5 1.5572
Encounter 2 7 6 1.5802
Encounter 3 16 13 2.6758
(a) Computation time on a beagle bone (1GHz ARM® Cortex-A8)
Iterations used Nodes explored Time taken (s)
Encounter 1 7 5 0.1324
Encounter 2 19 14 0.2822
Encounter 3 10 7 0.1671
(b) Computation time on a Jetson TK1 (2.32GHz ARM quad-core Cortex-A15)
https://beagleboard.org/blackhttp://www.nvidia.com/object/jetson-tk1-embedded-dev-kit.html
ICAROUS
• Implementation available in Java/C++ on Githubunder 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.
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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.
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