Papers
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2024
- Multi-Agent Clarity-Aware Dynamic Coverage with Gaussian ProcessesThis paper presents two algorithms for multi-agent dynamic coverage in spatiotemporal environments, where the coverage algorithms are informed by the method of data assimilation. In particular, we show that by explicitly modeling the environment using a Gaussian Process (GP) model, and considering the sensing capabilities and the dynamics of a team of robots, we can design an estimation algorithm and multi-agent coverage controller that explores and estimates the state of the spatiotemporal environment. The uncertainty of the estimate is quantified using clarity, an information-theoretic metric, where higher clarity corresponds to lower uncertainty. By exploiting the relationship between GPs and Stochastic Differential Equations (SDEs) we quantify the increase in clarity of the estimated state at any position due to a measurement taken from any other position. We use this relationship to design two new coverage controllers, both of which scale well with the number of agents exploring the domain, assuming the robots can share the map of the clarity over the spatial domain via communication. We demonstrate the algorithms through a realistic simulation of a team of robots collecting wind data over a region in Austria.
- Eclares: Energy-Aware Clarity-Driven Ergodic SearchPlanning informative trajectories while considering the spatial distribution of the information over the environment, as well as constraints such as the robot’s limited battery capacity, makes the long-time horizon persistent coverage problem complex. Ergodic search methods consider the spatial distribution of environmental information while optimizing robot trajectories; however, current methods lack the ability to construct the target information spatial distribution for environments that vary stochastically across space and time. Moreover, current coverage methods dealing with battery capacity constraints either assume simple robot and battery models or are computationally expensive. To address these problems, we propose a framework called Eclares, in which our contribution is two-fold. 1) First, we propose a method to construct the target information spatial distribution for ergodic trajectory optimization using clarity, an information measure bounded between [0, 1]. The clarity dynamics allow us to capture information decay due to a lack of measurements and to quantify the maximum attainable information in stochastic spatiotemporal environments. 2) Second, instead of directly tracking the ergodic trajectory, we introduce the energy-aware (eware) filter, which iteratively validates the ergodic trajectory to ensure that the robot has enough energy to return to the charging station when needed. The proposed eware filter is applicable to nonlinear robot models and is computationally lightweight. We demonstrate the working of the framework through a simulation case study.
@inproceedings{naveed2024eclares, title={Eclares: Energy-aware clarity-driven ergodic search}, author={Naveed, Kaleb Ben and Agrawal, Devansh and Vermillion, Christopher and Panagou, Dimitra}, booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)}, pages={14326--14332}, year={2024}, organization={IEEE} } - Advances in the Theory of Control Barrier Functions: Addressing practical challenges in safe control synthesis for autonomous and robotic systemsThis tutorial paper presents recent work of the authors that extends the theory of Control Barrier Functions (CBFs) to address practical challenges in the synthesis of safe controllers for autonomous systems and robots. We present novel CBFs and methods that handle safety constraints (i) with time and input constraints under disturbances, (ii) with high-relative degree under disturbances and input constraints, and (iii) that are affected by adversarial inputs and sampled-data effects. We then present novel CBFs and adaptation methods that prevent loss of validity of the CBF, as well as methods to tune the parameters of the CBF online to reduce conservatism in the system response. We also address the pointwise-only optimal character of CBF-induced control inputs by introducing a CBF formulation that accounts for future trajectories, as well as implementation challenges such as how to preserve safety when using output feedback control and zero-order-hold control. Finally we consider how to synthesize non-smooth CBFs when discontinuous inputs and multiple constraints are present.
@article{garg2024advances, title={Advances in the Theory of Control Barrier Functions: Addressing practical challenges in safe control synthesis for autonomous and robotic systems}, author={Garg, Kunal and Usevitch, James and Breeden, Joseph and Black, Mitchell and Agrawal, Devansh and Parwana, Hardik and Panagou, Dimitra}, journal={Annual Reviews in Control}, volume={57}, pages={100945}, year={2024}, publisher={Elsevier} }
2023
- gatekeeper: Online safety verification and control for nonlinear systems in dynamic environmentsThis paper presents the gatekeeper algorithm, a real-time and computationally-lightweight method to ensure that nonlinear systems can operate safely in dynamic environments despite limited perception. gatekeeper integrates with existing path planners and feedback controllers by introducing an additional verification step that ensures that proposed trajectories can be executed safely, despite nonlinear dynamics subject to bounded disturbances, input constraints and partial knowledge of the environment. Our key contribution is that (A) we propose an algorithm to recursively construct committed trajectories, and (B) we prove that tracking the committed trajectory ensures the system is safe for all time into the future. The method is demonstrated on a complicated firefighting mission in a dynamic environment, and compares against the state-of-the-art techniques for similar problems.
@inproceedings{agrawal2023gatekeeper, title={gatekeeper: Online safety verification and control for nonlinear systems in dynamic environments}, author={Agrawal, Devansh and Chen, Ruichang and Panagou, Dimitra}, booktitle={2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, pages={259--266}, year={2023}, organization={IEEE} } - Predictive Velocity Trajectory Control for a Persistently Operating Solar-Powered Autonomous Surface VesselThe Gulf Stream represents a major potential resource for renewable energy but is presently only sparsely characterized via radar, buoys, gliders, and intermittently op- erating human-operated research vessels. Dramatically greater resolution is possible through the use of persistently operating autonomous surface vessels (ASVs), which can be powered by wind, wave, or solar resources. Optimizing the control of these ASVs, taking into account the device and environmental properties, is crucial to obtaining good data. An ASV’s path and velocity profile along that path both significantly influence the amount of a mission domain that can be covered and, ultimately, the scientific quality of the mission. While our previous work focused on optimizing the path of a solar-powered ASV with fixed speed, the present work represents the complement: optimizing the speed for a given path, accounting for the ASV dynamics, flow resource, and solar resource. We perform this optimization through a model predictive controller that maxi- mizes the projected distance traversed, with a terminal incentive that captures the estimated additional long-duration range that is achievable from a given terminal battery state of charge. We present simulation results based on the SeaTrac SP-48 ASV, Mid-Atlantic Bight/South-Atlantic Bight Regional Ocean Model, and European Centre for Medium-Range Weather Forecasts solar model. Our results show improved performance relative to simpler heuristic controllers that aim to maintain constant speed or constant state of charge. However, we also show that the design of the MPC terminal incentive and design of the heuristic comparison controller can significantly impact the achieved performance; by examining underlying simulation results for different designs, we are able to identify likely causes of performance discrepancies.
- Sensor-based Planning and Control for Robotic Systems: Introducing Clarity and PerceivabilityIn this letter, we first introduce an information measure, termed clarity , motivated by information entropy, and show that it has intuitive properties relevant to dynamic coverage control and informative path planning. Clarity defines on a scale of [0,1] the quality of the information that we have about a variable of interest in an environment. Clarity lower bounds the expected estimation error of any estimator, and is used as the information metric in the notion of perceivability , which is defined later on and is the primary contribution of this letter. Perceivability captures whether a given robotic (or more generally, sensing and control) system has sufficient sensing and actuation capabilities to gather desired information about an environment. We show that perceivability relates to the reachability of an augmented system, which encompasses the robot dynamics and the clarity about the environment, and we derive the corresponding Hamilton-Jacobi-Bellman equations. Thus, we provide an algorithm to measure an environment’s perceivability, and obtain optimal controllers that maximize information gain. In simulations, we demonstrate how clarity is a useful concept for planning trajectories, how perceivability can be determined using reachability analysis, and how a Control Barrier Function controller can be used to design controllers to maintain a desired level of information.
@article{agrawal2023sensor, title={Sensor-based planning and control for robotic systems: Introducing clarity and perceivability}, author={Agrawal, Devansh R and Panagou, Dimitra}, journal={IEEE Control Systems Letters}, year={2023}, publisher={IEEE} }
2022
- Safe and robust observer-controller synthesis using control barrier functionsThis letter addresses the synthesis of safety-critical controllers using estimate feedback. We propose an observer-controller interconnection to ensure that the nonlinear system remains safe despite bounded disturbances on the system dynamics and measurements that correspond to partial state information. The co-design of observers and controllers is critical, since even in undisturbed cases, observers and controllers designed independently may not render the system safe. We propose two approaches to synthesize observer-controller interconnections. The first approach utilizes Input-to-State Stable observers, and the second uses Bounded Error observers. Using these stability and boundedness properties of the observation error, we construct novel Control Barrier Functions that impose inequality constraints on the control inputs which, when satisfied, certifies safety. We propose quadratic program-based controllers to satisfy these constraints, and prove Lipschitz continuity of the derived controllers. Simulations and experiments on a quadrotor demonstrate the efficacy of the proposed methods.
@article{agrawal2022safe, title={Safe and robust observer-controller synthesis using control barrier functions}, author={Agrawal, Devansh R and Panagou, Dimitra}, journal={IEEE Control Systems Letters}, volume={7}, pages={127--132}, year={2022}, publisher={IEEE} } - Coverage-Maximizing Solar-Powered Autonomous Surface Vehicle Control for Persistent Gulf Stream ObservationThe Gulf Stream, which comes within 100 km of the United States coastline in both the Florida Straits and the vicinity of Cape Hatteras, is estimated to possess over 160 TWh/year of technical energy capacity. To better understand the behavior of the Gulf Stream, whose flow resource varies in both space and time, a relatively sparse network of fixed acoustic Doppler current profilers (ADCPs) and shore-mounted high-frequency radar units have been supplemented by more granular but infrequent boat transect runs and undersea glider deployments. Collectively, these measurements provide highly granular data with respect to either time or space, but not both. This work represents part of a comprehensive effort to evaluate use of a solar-powered autonomous surface vehicle (ASV) fleet to supplement existing observational capabilities. The proposed solar-powered ASV can provide data with high spatial and temporal granularity, but comes with the challenge of optimally planning its mission in an adaptive manner. To address this challenge in this work, we propose a multilevel controller that fuses the A* search algorithm with an upper level waypoint selector and lower level heading control. Focusing on a critically important mission domain adjacent to Cape Hatteras, and relying on a Mid-Atlantic Bight, South Atlantic Bight Regional Ocean Model (MAB-SAB-ROM), we compare the performance of our proposed algorithm against several competing strategies. We demonstrate a significant performance improvement in terms of a dynamic coverage metric, both in comparison to competing strategies and to the existing observational network.
2021
- A Constructive Method for Designing Safe Multirate Controllers for Differentially-Flat SystemsThis paper introduces the notion of an Input Constrained Control Barrier Function (ICCBF), as a method to synthesize safety-critical controllers for nonlinear control-affine systems with input constraints. The method identifies a subset of the safe set of states, and constructs a controller to render the subset forward invariant. The feedback controller is represented as the solution to a quadratic program, which can be solved efficiently for real-time implementation. Furthermore, we show that ICCBFs are a generalization of Higher Order Control Barrier Functions, and thus are applicable to systems of non- uniform relative degree. Simulation results are presented for the adaptive cruise control problem, and a spacecraft rendezvous problem.
@ARTICLE{9655322, author={Agrawal, Devansh R. and Parwana, Hardik and Cosner, Ryan K. and Rosolia, Ugo and Ames, Aaron D. and Panagou, Dimitra}, journal={IEEE Control Systems Letters}, title={A Constructive Method for Designing Safe Multirate Controllers for Differentially-Flat Systems}, year={2022}, volume={6}, number={}, pages={2138-2143}, doi={10.1109/LCSYS.2021.3136465} } - Safe Control Synthesis via Input Constrained Control Barrier FunctionsThis paper introduces the notion of an Input Constrained Control Barrier Function (ICCBF), as a method to synthesize safety-critical controllers for nonlinear control-affine systems with input constraints. The method identifies a subset of the safe set of states, and constructs a controller to render the subset forward invariant. The feedback controller is represented as the solution to a quadratic program, which can be solved efficiently for real-time implementation. Furthermore, we show that ICCBFs are a generalization of Higher Order Control Barrier Functions, and thus are applicable to systems of non- uniform relative degree. Simulation results are presented for the adaptive cruise control problem, and a spacecraft rendezvous problem.
@INPROCEEDINGS{9682938, author={Agrawal, Devansh R. and Panagou, Dimitra}, booktitle={2021 60th IEEE Conference on Decision and Control (CDC)}, title={Safe Control Synthesis via Input Constrained Control Barrier Functions}, year={2021}, volume={}, number={}, pages={6113-6118}, doi={10.1109/CDC45484.2021.9682938} }