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This paper explores the use of autonomous underwater vehicles (AUVs) equipped with sensors to construct water quality models to aid in the assessment of important environmental hazards, for instance related to point‐source pollutants or localized hypoxic regions. Our focus is on problems requiring the autonomous discovery and dense sampling of critical areas of interest in real‐time, for which standard (e.g., grid‐based) strategies are not practical due to AUV power and computing constraints that limit mission duration. To this end, we consider adaptive sampling strategies on Gaussian process (GP) stochastic models of the measured scalar field to focus sampling on the most promising and informative regions. Specifically, this study employs the GP upper confidence bound as the optimization criteria to adaptively plan sampling paths that balance a trade‐off between exploration and exploitation. Two informative path planning algorithms based on (i) branch‐and‐bound techniques and (ii) cross‐entropy optimization are presented for choosing future sampling locations while considering the motion constraints of the sampling platform. The effectiveness of the proposed methods are explored in simulated scalar fields for identifying multiple regions of interest within a three‐dimensional environment. Field experiments with an AUV using both virtual measurements on a known scalar field and in situ dissolved oxygen measurements for studying hypoxic zones validate the approach's capability to quickly explore the given area, and then subsequently increase the sampling density around regions of interest without sacrificing model fidelity of the full sampling area.

 

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. 

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. 

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. 

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 

The current state-of-the-art for testing and evaluation of autonomous surface vehicle (ASV) decision-making is currently limited to one-versus-one vessel interactions by determining compliance with the International Regulations for Prevention of Collisions at Sea, referred to as COLREGS. Strict measurement of COLREGS compliance, however, loses value in multi-vessel encounters, as there can be conflicting rules which make determining compliance extremely subjective. This work proposes several performance metrics to evaluate ASV decision-making based on the concept of "good seamanship," a practice which generalizes to multi-vessel encounters. Methodology for quantifying good seamanship is presented based on the criteria of reducing the overall collision risk of the situation and taking early, appropriate actions. Case study simulation results are presented to showcase the seamanship performance criteria against different ASV planning strategies.