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Scalable pursuer coordination for reach-avoid games against a fast evader are developed leveraging coverage control over manifolds. The maintenance of a manifold, termed defense surface, prevents the evader and its target from occupying the same half-space and shown sufficient as a cooperative capture strategy. Nonlinear control synthesis continually reconfigures the pursuers to enable a defense surface via coverage. Simulation results empirically validate that the proposed condition 

Approaches for stochastic nonlinear model predictive control (SNMPC) typically make restrictive assumptions about the system dynamics and rely on approximations to characterize the evolution of the underlying uncertainty distributions. For this reason, they are often unable to capture more complex distributions (e.g., non-Gaussian or multi-modal) and cannot provide accurate guarantees of performance. In this letter, we present a sampling-based SNMPC approach that leverages recently derived sample complexity bounds to certify the performance of a feedback policy without making assumptions about the system dynamics or underlying uncertainty distributions. By parallelizing our approach, we are able to demonstrate real-time receding-horizon SNMPC with statistical safety guarantees in simulation and on hardware using a 1/10th scale rally car and a 24-inch wingspan fixed-wing unmanned aerial vehicle (UAV). 

We present an implementation of a formally verified safety fallback controller for improved collision avoidance in an autonomous vehicle research platform. Our approach uses a primary trajectory planning system that aims for collision-free navigation in the presence of pedestrians and other vehicles, and a fallback controller that guards its behavior. The safety fallback controller excludes the possibility of collisions by accounting for nondeterministic uncertainty in the dynamics of the vehicle and moving obstacles, and takes over the primary controller as necessary. We demonstrate the system in an experimental set-up that includes simulations and real-world tests with a 1/5-scale vehicle. In stressing simulation scenarios, the safety fallback controller significantly reduces the number of collisions. 

This work provides a decentralized approach to safety by combining tools from control barrier functions (CBF) and nonlinear model predictive control (NMPC). It is shown how leveraging backup safety controllers allows for the robust implementation of CBF over the NMPC computation horizon, ensuring safety in nonlinear systems with actuation constraints. A leader-follower approach to control barrier functions (LFCBF) enforcement will be introduced as a strategy to enable a robot leader, in a multi-robot interactions, to complete its task in minimum time, hence aggressively maneuvering. An algorithmic implementation of the proposed solution is provided and safety is verified via simulation. 

Motion planning methods for autonomous systems based on nonlinear programming offer great flexibility in incorporating various dynamics, objectives, and constraints. One limitation of such tools is the difficulty of efficiently representing obstacle avoidance conditions for non-trivial shapes. For example, it is possible to define collision avoidance constraints suitable for nonlinear programming solvers in the canonical setting of a circular robot navigating around M convex polytopes over N time steps. However, it requires introducing (2+L)MN additional constraints and LMN additional variables, with L being the number of halfplanes per polytope, leading to larger nonlinear programs with slower and less reliable solving time. In this paper, we overcome this issue by building closed-form representations of the collision avoidance conditions by outerapproximating the Minkowski sum conditions for collision. Our solution requires only MN constraints (and no additional variables), leading to a smaller nonlinear program. On motion planning problems for an autonomous car and quadcopter in cluttered environments, we achieve speedups of 4.0x and 10x respectively with significantly less variance in solve times and negligible impact on performance arising from the use of outer approximations. 

Motion planning methods for autonomous systems based on nonlinear programming offer great flexibility in incorporating various dynamics, objectives, and constraints. One limitation of such tools is the difficulty of efficiently representing obstacle avoidance conditions for non-trivial shapes. For example, it is possible to define collision avoidance constraints suitable for nonlinear programming solvers in the canonical setting of a circular robot navigating around $M$ convex polytopes over $N$ time steps. However, it requires introducing $(2+L)MN$ additional constraints and $LMN$ additional variables, with $L$ being the number of halfplanes per polytope, leading to larger nonlinear programs with slower and less reliable solving time. In this paper, we overcome this issue by building closed-form representations of the collision avoidance conditions by outer-approximating the Minkowski sum conditions for collision. Our solution requires only $MN$ constraints (and no additional variables), leading to a smaller nonlinear program. On motion planning problems for an autonomous car and quadcopter in cluttered environments, we achieve speedups of 4.0x and 10x respectively with significantly less variance in solve times and negligible impact on performance arising from the use of outer approximations. 

This paper addresses the problem of identifying whether/how a black-box autonomous system has regressed in performance when compared to previous versions. The approach analyzes performance datasets (typically gathered through simulation-based testing) and automatically extracts test parameter clusters of predicted performance regression. First, surrogate modeling with quantile random forests is used to predict regions of performance regression with high confidence. The predicted regression landscape is then clustered in both the output space and input space to produce groupings of test conditions ranked by performance regression severity. This approach is analyzed using randomized test functions as well as through a case study to detect performance regression in autonomous surface vessel software. 

This paper offers a multi-layer planning approachfor autonomous surface vessels (ASVs) that must adhere togood seamanship practices and the International Regulationsfor Prevention of Collisions at Sea (COLREGS) [1]. The ap-proach combines novel situational awareness logic with motionprimitive-based planners in a receding horizon framework.Further, ship domain and ship arena concepts are used todevelop risk metrics that capture COLREGS compliance andthe notion of good seamanship. By relying on metrics-drivenmotion planning as opposed to rule-based conditions, theproposed framework scales naturally to non-trivial single-vessel and multi-vessel situations. The planner is evaluatedusing adaptive, simulation-based testing to statistically comparethe performance to other standard methods. Finally, proof-of-concept field experiments are presented on a subscale platform