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  1. Free, publicly-accessible full text available May 24, 2024
  2. We introduce Spatial Predictive Control (SPC), a technique for solving the following problem: given a collection of robotic agents with black-box positional low-level controllers (PLLCs) and a mission-specific distributed cost function, how can a distributed controller achieve and maintain cost-function minimization without a plant model and only positional observations of the environment? Our fully distributed SPC controller is based strictly on the position of the agent itself and on those of its neighboring agents. This information is used in every time step to compute the gradient of the cost function and to perform a spatial look-ahead to predict the best next target position for the PLLC. Using a simulation environment, we show that SPC outperforms Potential Field Controllers, a related class of controllers, on the drone flocking problem. We also show that SPC works on real hardware, and is therefore able to cope with the potential sim-to-real transfer gap. We demonstrate its performance using as many as 16 Crazyflie 2.1 drones in a number of scenarios, including obstacle avoidance. 
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    Free, publicly-accessible full text available May 29, 2024
  3. Abstract Complex reasoning problems are most clearly and easily specified using logical rules, but require recursive rules with aggregation such as count and sum for practical applications. Unfortunately, the meaning of such rules has been a significant challenge, leading to many disagreeing semantics. This paper describes a unified semantics for recursive rules with aggregation, extending the unified founded semantics and constraint semantics for recursive rules with negation. The key idea is to support simple expression of the different assumptions underlying different semantics, and orthogonally interpret aggregation operations using their simple usual meaning. We present a formal definition of the semantics, prove important properties of the semantics and compare with prior semantics. In particular, we present an efficient inference over aggregation that gives precise answers to all examples we have studied from the literature. We also apply our semantics to a wide range of challenging examples, and show that our semantics is simple and matches the desired results in all cases. Finally, we describe experiments on the most challenging examples, exhibiting unexpectedly superior performance over well-known systems when they can compute correct answers. 
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  4. he Simplex Architecture is a runtime assurance framework where control authority may switch from an unverified and potentially unsafe advanced controller to a backup baseline controller in order to maintain the safety of an autonomous cyber-physical system. In this work, we show that runtime checks can replace the requirement to statically verify safety of the baseline controller. This is important as there are many powerful control techniques, such as model-predictive control and neural network controllers, that work well in practice but are difficult to statically verify. Since the method does not use internal information about the advanced or baseline controller, we call the approach the Black-Box Simplex Architecture. We prove the architecture is safe and present two case studies where (i) model-predictive control provides safe multi-robot coordination, and (ii) neural networks provably prevent collisions in groups of F-16 aircraft, despite the controllers occasionally outputting unsafe commands. 
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  5. Artemov, Sergei ; Nerode, Anil (Ed.)
    Complex reasoning problems are most clearly and easily specified using logical rules, but require recursive rules with aggregation such as counts and sums for practical applications. Unfortunately, the meaning of such rules has been a significant challenge, leading to many disagreeing semantics. This paper describes a unified semantics for recursive rules with aggregation, extending the unified founded semantics and constraint semantics for recursive rules with negation. The key idea is to support simple expression of the different assumptions underlying different semantics, and orthogonally interpret aggregation operations using their simple usual meaning. We present formal definition of the semantics, prove important properties of the semantics, and compare with prior semantics. In particular, we present an efficient inference over aggregation that gives precise answers to all examples we have studied from the literature. We also applied our semantics to a wide range of challenging examples, and performed experiments on the most challenging ones, all confirming our analyzed results. 
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  6. Dang, Thao ; Stolz, Volker (Ed.)
    We present Barrier-based Simplex (Bb-Simplex), a new, provably correct design for runtime assurance of continuous dynamical systems. Bb-Simplex is centered around the Simplex Control Architecture, which consists of a high-performance advanced controller which is not guaranteed to maintain safety of the plant, a verified-safe baseline controller, and a decision module that switches control of the plant between the two controllers to ensure safety without sacrificing performance. In Bb-Simplex, Barrier certificates are used to prove that the baseline controller ensures safety. Furthermore, Bb-Simplex features a new automated method for deriving, from the barrier certificate, the conditions for switching between the controllers. Our method is based on the Taylor expansion of the barrier certificate and yields computationally inexpensive switching conditions. We consider a significant application of Bb-Simplex to a microgrid featuring an advanced controller in the form of a neural network trained using reinforcement learning. The microgrid is modeled in RTDS, an industry-standard high-fidelity, real-time power systems simulator. Our results demonstrate that Bb-Simplex can automatically derive switching conditions for complex systems, the switching conditions are not overly conservative, and Bb-Simplex ensures safety even in the presence of adversarial attacks on the neural controller. 
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  7. We present the Distributed Simplex Architecture (DSA), a new runtime assurance technique that provides safety guarantees for multi-agent systems (MASs). DSA is inspired by the Simplex control architecture of Sha et al., but with some significant differences. The traditional Simplex approach is limited to single-agent systems or a MAS with a centralized control scheme. DSA addresses this limitation by extending the scope of Simplex to include MASs under distributed control. In DSA, each agent runs a local instance of traditional Simplex such that the preservation of safety in the local instances implies safety for the entire MAS. Control Barrier Functions play a critical role. They are used to define DSA’s core components (the baseline controller and the decision module’s switching logic between advanced and baseline controllers) and to verify the safety of a DSA instance in a distributed manner. We provide a general proof of safety for DSA, and present experimental results for several case studies, including flocking with collision avoidance, safe navigation of ground rovers through way-points, and the safe operation of a microgrid. 
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  8. We introduce the concept of Distributed Model Predictive Control (DMPC) with Acceleration-Weighted Neighborhooding (AWN) in order to synthesize a distributed and symmetric controller for high-speed flocking maneuvers (angular turns in general). Acceleration-Weighted Neighborhooding exploits the imbalance in agent accelerations during a turning maneuver to ensure that actively turning agents are prioritized. We show that with our approach, a flocking maneuver can be achieved without it being a global objective. Only a small subset of the agents, called initiators, need to be aware of the maneuver objective. Our AWN-DMPC controller ensures this local information is propagated throughout the flock in a scale-free manner with linear delays. Our experimental evaluation conclusively demonstrates the maneuvering capabilities of a distributed flocking controller based on AWN-DMPC. 
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  9. null (Ed.)
    Abstract Programming with logic for sophisticated applications must deal with recursion and negation, which together have created significant challenges in logic, leading to many different, conflicting semantics of rules. This paper describes a unified language, DA logic, for design and analysis logic, based on the unifying founded semantics and constraint semantics, that supports the power and ease of programming with different intended semantics. The key idea is to provide meta-constraints, support the use of uncertain information in the form of either undefined values or possible combinations of values and promote the use of knowledge units that can be instantiated by any new predicates, including predicates with additional arguments. 
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