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  1. Safety is a critical component in today's autonomous and robotic systems. Many modern controllers endowed with notions of guaranteed safety properties rely on accurate mathematical models of these nonlinear dynamical systems. However, model uncertainty is always a persistent challenge weakening theoretical guarantees and compromising safety. For safety-critical systems, this is an even bigger challenge. Typically, safety is ensured by constraining the system states within a safe constraint set defined a priori by relying on the model of the system. A popular approach is to use Control Barrier Functions (CBFs) that encode safety using a smooth function. However, CBFs fail in the presence of model uncertainties. Moreover, an inaccurate model can either lead to incorrect notions of safety or worse, incur system critical failures. Addressing these drawbacks, we present a novel safety formulation that leverages properties of CBFs and positive definite kernels to design Gaussian CBFs. The underlying kernels are updated online by learning the unmodeled dynamics using Gaussian Processes (GPs). While CBFs guarantee forward invariance, the hyperparameters estimated using GPs update the kernel online and thereby adjust the relative notion of safety. We demonstrate our proposed technique on a safety-critical quadrotor on SO(3) in the presence of model uncertainty inmore »simulation. With the kernel update performed online, safety is preserved for the system.« less
  2. The advent of pervasive autonomous systems such as self-driving cars and drones has raised questions about their safety and trustworthiness. This is particularly relevant in the event of on-board subsystem errors or failures. In this research, we show how encoded Extended Kalman Filter can be used to detect anomalous behaviors of critical components of nonlinear autonomous systems: sensors, actuators, state estimation algorithms and control software. As opposed to prior work that is limited to linear systems or requires the use of cumbersome machine learned checks with fixed detection thresholds, the proposed approach necessitates the use of time-varying checks with dynamically adaptive thresholds. The method is lightweight in comparison to existing methods (does not rely on machine learning paradigms) and achieves high coverage as well as low detection latency of errors. A quadcopter and an automotive steer-by-wire system are used as test vehicles for the research and simulation and hardware results indicate the overhead, coverage and error detection latency benefits of the proposed approach.
  3. In this paper we propose a framework for concurrent detection of soft computation errors in particle filters which are finding increasing use in robotics applications. The particle filter works by sampling the multi-variate probability distribution of the states of a system (samples called particles, each particle representing a vector of states) and projecting these into the future using appropriate nonlinear mappings. We propose the addition of a `check' state to the system as a linear combination of the system states for error detection. The check state produces an error signal corresponding to each particle, whose statistics are tracked across a sliding time window. Shifts in the error statistics across all particles are used to detect soft computation errors as well as anomalous sensor measurements. Simulation studies indicate that errors in particle filter computations can be detected with high coverage and low latency.
  4. The successful deployment of autonomous real-time systems is contingent on their ability to recover from performance degradation of sensors, actuators, and other electro-mechanical subsystems with low latency. In this article, we introduce ALERA, a novel framework for real-time control law adaptation in nonlinear control systems assisted by system state encodings that generate an error signal when the code properties are violated in the presence of failures. The fundamental contributions of this methodology are twofold—first, we show that the time-domain error signal contains perturbed system parameters’ diagnostic information that can be used for quick control law adaptation to failure conditions and second, this quick adaptation is performed via reinforcement learning algorithms that relearn the control law of the perturbed system from a starting condition dictated by the diagnostic information, thus achieving significantly faster recovery. The fast (up to 80X faster than traditional reinforcement learning paradigms) performance recovery enabled by ALERA is demonstrated on an inverted pendulum balancing problem, a brake-by-wire system, and a self-balancing robot.
  5. The last decade has seen tremendous advances in the transformation of ubiquitous control, computing and communication platforms that are anytime, anywhere. These platforms allow humans to interact with machines through sensing, control and actuation functions in ways not imaginable a few decades ago. While robust control techniques aim to maintain autonomous system performance in the presence of bounded modeling errors, they are not designed to manage large multiparameter variations and internal component failures that are inevitable during lengthy periods of field deployment. To address the trustworthiness of autonomous systems in the field, we propose a cross-layer error resilience approach in which errors are detected and corrected at appropriate levels of the design (hardware-through software) with the objective of minimizing the latency of error recovery while maintaining high failure coverage. At the control processor level, soft errors in the digital control processor are considered. At the system level, sensor and actuator failures are analyzed. These impairments define the health of the system. A methodology for adapting the control procedure of the autonomous system to compensate for degraded system health is proposed. It is shown how this methodology can be applied to simple linear and nonlinear control systems to maintain system performancemore »in the presence of internal component failures. Experimental results demonstrate the feasibility of the proposed methodology.« less
  6. The last decade has seen tremendous advances in the application of artificial neural networks to solving problems that mimic human intelligence. Many of these systems are implemented using traditional digital compute engines where errors can occur during memory accesses or during numerical computation. While such networks are inherently error resilient, specific errors can result in incorrect decisions. This work develops a low overhead error detection and correction approach for multilayer artificial neural networks, here the hidden layer functions are approximated using checker neurons. Experimental results show that a high coverage of injected errors can be achieved with extremely low computational overhead using consistency properties of the encoded checks. A key side benefit is that the checks can flag errors when the network is presented outlier data that do not correspond to data with which the network is trained to operate.
  7. In this research, a low cost error detection and correction approach is developed for multilayer perceptron networks, where checker neurons are used to encode hidden layer functions using independent training experiments. Error detection and correction is predicated on validating consistency properties of the encoded checks and shows that high coverage of injected errors can be achieved with extremely low computational overhead.