GARL: Genetic Algorithm-Augmented Reinforcement Learning to Detect Violations in Marker-Based Autonomous Landing Systems

• URL: http://arxiv.org/abs/2310.07378v3
• Date: Tue, 26 Nov 2024 13:08:26 GMT
• Title: GARL: Genetic Algorithm-Augmented Reinforcement Learning to Detect Violations in Marker-Based Autonomous Landing Systems
• Authors: Linfeng Liang, Yao Deng, Kye Morton, Valtteri Kallinen, Alice James, Avishkar Seth, Endrowednes Kuantama, Subhas Mukhopadhyay, Richard Han, Xi Zheng,
• Abstract summary: Traditional offline testing methods miss violation cases caused by dynamic objects like people and animals. Online testing methods require extensive training time, which is impractical with limited budgets. We introduce GARL, a framework combining a genetic algorithm (GA) and reinforcement learning (RL) for efficient generation of diverse and real landing system failures.
• Score: 0.7461036096470347
• License:
• Abstract: Automated Uncrewed Aerial Vehicle (UAV) landing is crucial for autonomous UAV services such as monitoring, surveying, and package delivery. It involves detecting landing targets, perceiving obstacles, planning collision-free paths, and controlling UAV movements for safe landing. Failures can lead to significant losses, necessitating rigorous simulation-based testing for safety. Traditional offline testing methods, limited to static environments and predefined trajectories, may miss violation cases caused by dynamic objects like people and animals. Conversely, online testing methods require extensive training time, which is impractical with limited budgets. To address these issues, we introduce GARL, a framework combining a genetic algorithm (GA) and reinforcement learning (RL) for efficient generation of diverse and real landing system failures within a practical budget. GARL employs GA for exploring various environment setups offline, reducing the complexity of RL's online testing in simulating challenging landing scenarios. Our approach outperforms existing methods by up to 18.35% in violation rate and 58% in diversity metric. We validate most discovered violation types with real-world UAV tests, pioneering the integration of offline and online testing strategies for autonomous systems. This method opens new research directions for online testing, with our code and supplementary material available at https://github.com/lfeng0722/drone_testing/.

Related papers

• Autonomous Vehicle Controllers From End-to-End Differentiable Simulation [60.05963742334746]
  We propose a differentiable simulator and design an analytic policy gradients (APG) approach to training AV controllers. Our proposed framework brings the differentiable simulator into an end-to-end training loop, where gradients of environment dynamics serve as a useful prior to help the agent learn a more grounded policy. We find significant improvements in performance and robustness to noise in the dynamics, as well as overall more intuitive human-like handling.
  arXiv  Detail & Related papers  (2024-09-12T11:50:06Z)
• PAFOT: A Position-Based Approach for Finding Optimal Tests of Autonomous Vehicles [4.243926243206826]
  This paper proposes PAFOT, a position-based approach testing framework. PAFOT generates adversarial driving scenarios to expose safety violations of Automated Driving Systems. Experiments show PAFOT can effectively generate safety-critical scenarios to crash ADSs and is able to find collisions in a short simulation time.
  arXiv  Detail & Related papers  (2024-05-06T10:04:40Z)
• DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control [62.24301794794304]
  Deep Adaptive Trajectory Tracking (DATT) is a learning-based approach that can precisely track arbitrary, potentially infeasible trajectories in the presence of large disturbances in the real world. DATT significantly outperforms competitive adaptive nonlinear and model predictive controllers for both feasible smooth and infeasible trajectories in unsteady wind fields. It can efficiently run online with an inference time less than 3.2 ms, less than 1/4 of the adaptive nonlinear model predictive control baseline.
  arXiv  Detail & Related papers  (2023-10-13T12:22:31Z)
• DARTH: Holistic Test-time Adaptation for Multiple Object Tracking [87.72019733473562]
  Multiple object tracking (MOT) is a fundamental component of perception systems for autonomous driving. Despite the urge of safety in driving systems, no solution to the MOT adaptation problem to domain shift in test-time conditions has ever been proposed. We introduce DARTH, a holistic test-time adaptation framework for MOT.
  arXiv  Detail & Related papers  (2023-10-03T10:10:42Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• Adaptive Failure Search Using Critical States from Domain Experts [9.93890332477992]
  Failure search may be done through logging substantial vehicle miles in either simulation or real world testing. AST is one such method that poses the problem of failure search as a Markov decision process. We show that the incorporation of critical states into the AST framework generates failure scenarios with increased safety violations.
  arXiv  Detail & Related papers  (2023-04-01T18:14:41Z)
• Unsupervised Adaptation from Repeated Traversals for Autonomous Driving [54.59577283226982]
  Self-driving cars must generalize to the end-user's environment to operate reliably. One potential solution is to leverage unlabeled data collected from the end-users' environments. There is no reliable signal in the target domain to supervise the adaptation process. We show that this simple additional assumption is sufficient to obtain a potent signal that allows us to perform iterative self-training of 3D object detectors on the target domain.
  arXiv  Detail & Related papers  (2023-03-27T15:07:55Z)
• Analyzing Robustness of the Deep Reinforcement Learning Algorithm in Ramp Metering Applications Considering False Data Injection Attack and Defense [0.0]
  Ramp metering is the act of controlling on-going vehicles to the highway mainlines. Deep Q-Learning algorithm uses only loop detectors information as inputs in this study. Model can be applied to almost any ramp metering sites regardless of the road geometries and layouts.
  arXiv  Detail & Related papers  (2023-01-28T00:40:46Z)
• DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation [68.8204255655161]
  We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE) It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase. Results show that the DAE achieves better results in both accuracy and speed of detection.
  arXiv  Detail & Related papers  (2021-09-08T14:07:55Z)
• Towards Automated Safety Coverage and Testing for Autonomous Vehicles with Reinforcement Learning [0.3683202928838613]
  Validation puts the autonomous vehicle system to the test in scenarios or situations that the system would likely encounter in everyday driving. We propose using reinforcement learning (RL) to generate failure examples and unexpected traffic situations for the AV software implementation.
  arXiv  Detail & Related papers  (2020-05-22T19:00:38Z)
• Search-based Test-Case Generation by Monitoring Responsibility Safety Rules [2.1270496914042996]
  We propose a method for screening and classifying simulation-based driving test data to be used for training and testing controllers. Our framework is distributed with the publicly available S-TALIRO and Sim-ATAV tools.
  arXiv  Detail & Related papers  (2020-04-25T10:10:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.