Tolerating Broken Robots

Transcription

1 Tolerating Broken Robots Lachlan Murray, Jon Timmis and Andy Tyrrell Intelligent Systems Group Department of Electronics University of York, UK March 24, 2011 People say my broken friend is useless, but I say his mind is free. There s a lot of things my mangled robot friend could be... - Bender Bending Rodríguez (3003)

2 Outline SYMBRION IR anomaly detection Future...

3 Symbiotic Evolutionary Robot Organisms Swarm Robotic System Re-configurable Robotic System Collective Robotic System Heterogeneous, re-configurable, collective robotic system Robots share energy and computational resources Terminology: Individual/Module - a single independent robot Swarm - multiple cooperating individuals Organism - multiple connected individuals Collective - combination of individuals, swarms and organisms

4 SYMBRION Robots I Three specialised modules: I I I Active Wheel Scout Robot Backbone Robot I Common docking interface I 15 working robots by May! (sort of)

5 SYMBRION Grand Challenges GC1-100 Robots 100 Days Long-term survival in a changing environment GC2 - The Emergence of Multi-cellularity Online, on-board evolution

6 IR Anomaly Detection Immune-inspired anomaly detection using the modified Dendritic Cell Algorithm (mdca) Detecting anomalies in the infrared sensors of simulated SYMBRION-style robots Optimised using NSGA-II Compared with Support Vector Machines (SVM) Performance measured in terms of: Classification accuracy Execution time Long-term survival

7 Simulator Extended version of Stage (in collab. UWE) SYMBRION-style robots Realistic IR data Energy sharing capabilities

8 Behavioural Controller Wait Wander Reverse Un-dock Approach Recharge Provide Recover Avoid Align Dock Default behaviour: random wandering with obstacle avoidance Every state transitions to Recover when motors stall

9 Energy Sharing Strategy Wait Wander Reverse Un-dock Approach Recharge Provide Recover Avoid Align Dock If moderately in need of energy - recharge If desperately in need of energy - wait If another robot is in need - provide

10 Recovery Strategy If sensor is returning anomalous data take value of neighbouring sensor If neighbour is returning anomalous data panic assume a small value If either of the front two sensor are returning anomalous data prevent controller from entering Approach state Important - this strategy is not perfect

11 (Video not included)

12 Anomalies Anomalies in IR sensor data may originate from: Faulty sensors Interference from other robots Other environmental factors Here we consider transient faults in the sensors Three types of fault are simulated: 1. Stuck-at-value - sensor always returns the same value, regardless of the state of the environment being measured 2. Sensor noise - the value returned by the sensor is a random amount away from the ideal value 3. Sensor bias - the value returned by the sensor is consistently a fixed amount away from the value it should be

13 mdca Mai s algorithm Immune-inspired anomaly detection algorithm Simplified version of the DCA - for resource constrained systems Inspired by danger theory Behaviour determined by five parameters

14 mdca Mai s algorithm Immune-inspired anomaly detection algorithm Simplified version of the DCA - for resource constrained systems σ i (t) = waa i (t)+w b B i (t)+w cc i (t) w a+w b +w c φ i (t) = { 0 σ i (t) < τ 1 σ i (t) τ Inspired by danger theory c i (t) = 1 ω φi (k) Behaviour determined by five parameters

15 mdca Mai s algorithm Immune-inspired anomaly detection algorithm Simplified version of the DCA - for resource constrained systems σ i (t) = waa i (t)+w b B i (t)+w cc i (t) w a+w b +w c φ i (t) = { 0 σ i (t) < τ 1 σ i (t) τ Inspired by danger theory c i (t) = 1 ω φi (k) Behaviour determined by five parameters

16 mdca Mai s algorithm Immune-inspired anomaly detection algorithm Simplified version of the DCA - for resource constrained systems σ i (t) = waa i (t)+w b B i (t)+w cc i (t) w a+w b +w c φ i (t) = { 0 σ i (t) < τ 1 σ i (t) τ Inspired by danger theory c i (t) = 1 ω φi (k) Behaviour determined by five parameters

17 mdca Mai s algorithm Immune-inspired anomaly detection algorithm Simplified version of the DCA - for resource constrained systems σ i (t) = waa i (t)+w b B i (t)+w cc i (t) w a+w b +w c φ i (t) = { 0 σ i (t) < τ 1 σ i (t) τ Inspired by danger theory c i (t) = 1 ω φi (k) Behaviour determined by five parameters

18 mdca Mai s algorithm Immune-inspired anomaly detection algorithm Simplified version of the DCA - for resource constrained systems σ i (t) = waa i (t)+w b B i (t)+w cc i (t) w a+w b +w c φ i (t) = { 0 σ i (t) < τ 1 σ i (t) τ Inspired by danger theory c i (t) = 1 ω φi (k) Behaviour determined by five parameters

19 Feature Extraction σ i (t) = waa i (t)+w b B i (t)+w cc i (t) w a+w b +w c A i (t), B i (t) and C i (t) are features extracted from sensor data Example features include: Distance between neighbouring sensor values Mean of recent sensor values Standard deviation Skewness Six more parameters: fa, f b, f c - types of feature ta, t b, t c - associated time windows

20 Support Vector Machines Standard classification technique Given a set of labelled training data (features) In a training phase SVMs construct a hyperplane that maximises distance between classes New data instances are classified according to which side of the plane they are situated

21 Multi-objective Parameter Optimisation Objectives of a resource constrained anomaly detector: Maximise true positives whilst minimising false positives Maximise the speed of detection without loss of accuracy Minimise the computational cost of the system Three experiments: 1. mdca with fixed features 2. mdca with evolvable features 3. SVM with evolvable features NSGA-II used for all three

22 mdca with Fixed Features Fixed features: fa - distance between neighbouring sensor values fb - standard deviation of recent history fc - binary distance between current and recent mean t a fixed to 1, t b and t c evolved as one parameter: t bc All other mdca parameters evolved Objective 1 - minimise the distance between ideal and actual output when data is anomalous Objective 2 - minimise the distance between ideal and actual output when data is normal Parameter t bc ω w a w b w c τ Range (1, 1000) (-100, 100)

23 mdca with Fixed Features - results Bad FPR (relatively) Good TPR (Video not included) Measure TPR FPR PPV NPV ACC Value

24 mdca with Fixed Features - example output mdca output Ideal output Raw sensor value mdca output Ideal output Raw sensor value Time x 10 4

25 (Video not included)

26 mdca with Evolvable Features All feature types and window sizes evolved All other mdca parameters evolved Objective 1 - minimise the distance between ideal and actual output when data is anomalous Objective 2 - minimise the distance between ideal and actual output when data is normal Parameter f a f b f c t a t b t c ω w a w b w c τ Range (0, 9) (1, 500) (-100, 100)

27 mdca with Evolvable Features - results Good FPR Reasonable TPR (Video not included) Measure TPR FPR PPV NPV ACC Value

28 mdca with Evolvable Features - example output mdca output Ideal output Raw sensor value Time x 10 4 mdca output Ideal output Raw sensor value

29 SVM with Evolvable Features All feature types and window sizes evolved Objective 1 - Minimise one minus the true positive rate Objective 2 - Minimise the false positive rate Parameter f a f b f c t a t b t c Range (0, 9) (1, 500)

30 SVM with Evolvable Features - results Reasonable FPR Good TPR (Video not included) Measure TPR FPR PPV NPV ACC Value

31 SVM with Evolvable Features - example output mdca output Ideal output Raw sensor value SVM output Ideal output Time x 10 4 Raw sensor value

32 Comparisons Experiment mdca-i mdca-ii SVM Mean Sim. Time (s)

33 Comparisons Summary mdca-i/mdca-ii mdca-i/svm mdca-ii/svm TPR mdca-i - SVM FPR mdca-ii SVM mdca-ii PPV mdca-ii SVM mdca-ii NPV mdca-i - SVM ACC mdca-ii SVM - Speed mdca-i mdca-i mdca-ii No single approach outperforms the others for all measures In terms of classification SVM is the most balanced but execution and optimisation time was slow

34 Long-term survival 50 robots operating over a 10 hour period Robots assigned random starting positions and energy levels 12 anomalies injected into each robot Six different approaches investigated: No anomalies - control No anomaly detection - baseline Ideal anomaly detection - gold standard? mdca with fixed features mdca with evolvable features SVM with evolvable features

35 Long-term survival - mean stored energy 1.8 x Energy No anom aly detection No anom alies m DCA I Idealanom aly detection m DCA II SVM Time SVM performed best but not significantly better than the rest Why is the ideal detector not better? Deficiencies in the recovery strategy mean that in certain situations the best response to an anomaly is to ignore it

36 Long-term survival - surviving number of robots Num berofrobots No anom aly detection No anom alies m DCA I Idealanom aly detection m DCA II SVM Time SVM performed best but not significantly better than the rest

37 Summary The parameters of three mdca and SVM based anomaly detection systems were optimised using NSGA-II Comparisons were made in terms of accuracy, speed of execution and long-term survival No system outperformed all others in terms of accuracy - though the SVM-based system was the most balanced Both mdca systems significantly outperformed the SVM in terms of speed, and informally in terms of evolvability The importance of a good recovery strategy was highlighted by the long-term survival experiments

38 (Video not included)

39 Thanks!

40 True Positive Rate (TPR) True Negative Rate (TNR) False Positive Rate (FPR) Positive Predictive Value (PPV) Negative Predictive Value (NPV) Accuracy (ACC) TP (TP+FN) TN (TN+FP) 1 TNR TP (TP+FP) TN (TN+FN) TP+TN (TP+FP+TN+FN)

41 Standard Deviation σ = 1 (N 1) (xi x) 2 Average distance distave = x µ Binary average distance bdistave = { 0 distave τ 1 distave > τ Distance from neighbours distn = µ a (µ b + µ c )/2 Skew skew = 1 N ((xi µ)/σ) 3 Mean µ = 1 N xi StdDev distance diststddev = σ a σ b Range range = max(x ) min(x ) Pair distance pairdist = 1 N xi+1 x i