1. Evaluating Hardware Platforms and Path
Re-Planning Strategies for the UAV Emergency
Landing Problem
Jesimar S. Arantes, Márcio S. Arantes, André B. Missaglia,
Eduardo V. Simões and Claudio F. M. Toledo
November – 2017
Jesimar S. Arantes, Márcio S. Arantes, André B. Missaglia, Eduardo V. Simões and Claudio F. M. Toledo (USP)ICTAI 2017 November – 2017 1 / 22
2. Outline
1 Introduction
2 Problem Description
3 Methods
4 Computational Results
5 Conclusions
J. S. Arantes et al. (USP) ICTAI 2017 November – 2017 2 / 22
3. Introduction
This work addresses
Path replanning to land the UAV under critical situation:
Greedy Heuristic (GH) (Arantes et al. 2015).
Genetic Algorithm (GA) (Arantes et al. 2015).
Ensemble GA-GH.
Ensemble GA-GA.
Critical situations considered:
Motor failure.
Battery failure.
Supervision of safety systems
IFA: In-Flight Awareness (Mattei et al. 2013).
Performance evaluation:
Personal computer: Intel i5
Embedded computer: Intel Edison
J. S. Arantes et al. (USP) ICTAI 2017 November – 2017 3 / 22
4. Problem Description
dc
critical
situation
no-fly region
penalty region
bonus region
Legend:
mission route
emergencial route
aborted route critical distance
Figure 1: Illustrative scenario of emergency path re-planning.
J. S. Arantes et al. (USP) ICTAI 2017 November – 2017 4 / 22
5. Problem Description
Problem: Path-replanning problem
Minimize Pr
j
xT ∈ Op
j − Pr
i
xT ∈ Ob
j (1)
subject to:
xt+1 = FΨ(xt, ut) + ωt ∀(t) (2)
x0 ∼ N(ˆx0, Σx0 ), ωt ∼ N(0, Σωt ) ∀(t) (3)
Pr
j t
xt /∈ On
j
≥ 1 − ∆ (4)
J. S. Arantes et al. (USP) ICTAI 2017 November – 2017 5 / 22
6. Problem Description
Legend:
map boundary
mission routeno-fly region boundary
penalty region boundary
bonus region boundary takeoff
landing
Figure 2: Real world case study for aerial imaging application.
J. S. Arantes et al. (USP) ICTAI 2017 November – 2017 6 / 22
12. Computational Results
Settings used in the GA method.
Parameter Value Parameter Value
population size 39 crossover rate 0.5
tournament size 3 mutation rate 0.7
stopping criterion time elitism yes
Computer settings used.
PC i5 Intel Edison
Frequency 1.8 GHz 500 MHz
Memory RAM 4 GB 1 GB
Operating System Linux - Ubuntu Linux - Yocto
J. S. Arantes et al. (USP) ICTAI 2017 November – 2017 12 / 22
13. Computational Results
Technical specifications of the Ararinha.
Component Value Component Value
Wingspan 1.90m Weight 2.83kg
Length 1.15m Payload 0.60kg
Electric Power 740W Endurance 15 minutes
J. S. Arantes et al. (USP) ICTAI 2017 November – 2017 13 / 22
14. Computational Results
Time analysis for convergence of the GA.
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avg global
Time (ms)
Fitness
ε < 1%time = 890
ε < 2%time = 690
ε < 4%time = 460
J. S. Arantes et al. (USP) ICTAI 2017 November – 2017 14 / 22
15. Computational Results
GH
We run 1 time over each map
We evaluated 30 artificial maps in total
We evaluated 2 critical situations (motor and battery)
GA, GA-GH, GA-GA
We run 10 times over each map
We evaluated 30 artificial maps in total
We evaluated 2 critical situations (motor and battery)
We evaluated 3 stopping criterion based on time
J. S. Arantes et al. (USP) ICTAI 2017 November – 2017 15 / 22
18. Computational Results
F3
F2
F4
F1
Legend:
route motor failure
route battery failure
critical situation detected
start emergencial route
critical distance
Figure 7: Results of case study in a real world scenario using a GA method.
J. S. Arantes et al. (USP) ICTAI 2017 November – 2017 18 / 22
20. Computational Results
Figure 8: Complete route obtained in the SITL experiment.
https://youtu.be/k38GBjM5jXU
J. S. Arantes et al. (USP) ICTAI 2017 November – 2017 20 / 22
21. Conclusions
As expected, the personal computer leverages the strategies
performance.
GA-GA presents a slightly better performance.
This indicates the robustness of GAs to find good solutions despite
hardware limitations.
GA can be embedded and aid the decision making during fully
autonomous flights.
J. S. Arantes et al. (USP) ICTAI 2017 November – 2017 21 / 22