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Traversability estimation in rugged, unstructured environments remains a challenging problem in field robotics. Often, the need for precise, accurate traversability estimation is in direct opposition to the limited sensing and compute capability present on affordable, small-scale mobile robots. To address this issue, we present a novel method to learn [u]ncertainty-aware [n]avigation features from high-fidelity scans of [real]-world environments (UNRealNet). This network can be deployed on-robot to predict these high-fidelity features using input from lower-quality sensors. UNRealNet predicts dense, metric-space features directly from single-frame lidar scans, thus reducing the effects of occlusion and odometry error. Our approach is label-free, and is able to produce traversability estimates that are robot-agnostic. Additionally, we can leverage UNRealNet's predictive uncertainty to both produce risk-aware traversability estimates, and refine our feature predictions over time. We find that our method outperforms traditional local mapping and inpainting baselines by up to 40%, and demonstrate its efficacy on multiple legged platforms.

This paper addresses the problem of object-goal navigation in autonomous inspections in real-world environments. Object-goal navigation is crucial to enable effective inspections in various settings, often requiring the robot to identify the target object within a large search space. Current object inspection methods fall short of human efficiency because they typically cannot bootstrap prior and common sense knowledge as humans do. In this paper, we introduce a framework that enables robots to use semantic knowledge from prior spatial configurations of the environment and semantic common sense knowledge. We propose SEEK (Semantic Reasoning for Object Inspection Tasks) that combines semantic prior knowledge with the robot's observations to search for and navigate toward target objects more efficiently. SEEK maintains two representations: a Dynamic Scene Graph (DSG) and a Relational Semantic Network (RSN). The RSN is a compact and practical model that estimates the probability of finding the target object across spatial elements in the DSG. We propose a novel probabilistic planning framework to search for the object using relational semantic knowledge. Our simulation analyses demonstrate that SEEK outperforms the classical planning and Large Language Models (LLMs)-based methods that are examined in this study in terms of efficiency for object-goal inspection tasks. We validated our approach on a physical legged robot in urban environments, showcasing its practicality and effectiveness in real-world inspection scenarios.

Sampling-based model-predictive controllers have become a powerful optimization tool for planning and control problems in various challenging environments. In this paper, we show how the default choice of uncorrelated Gaussian distributions can be improved upon with the use of a colored noise distribution. Our choice of distribution allows for the emphasis on low frequency control signals, which can result in smoother and more exploratory samples. We use this frequency-based sampling distribution with Model Predictive Path Integral (MPPI) in both hardware and simulation experiments to show better or equal performance on systems with various speeds of input response., Comment: Published to RA-L

This paper addresses the problem of autonomous robotic inspection in complex and unknown environments. This capability is crucial for efficient and precise inspections in various real-world scenarios, even when faced with perceptual uncertainty and lack of prior knowledge of the environment. Existing methods for real-world autonomous inspections typically rely on predefined targets and waypoints and often fail to adapt to dynamic or unknown settings. In this work, we introduce the Semantic Belief Behavior Graph (SB2G) framework as a novel approach to semantic-aware autonomous robot inspection. SB2G generates a control policy for the robot, featuring behavior nodes that encapsulate various semantic-based policies designed for inspecting different classes of objects. We design an active semantic search behavior to guide the robot in locating objects for inspection while reducing semantic information uncertainty. The edges in the SB2G encode transitions between these behaviors. We validate our approach through simulation and real-world urban inspections using a legged robotic platform. Our results show that SB2G enables a more efficient inspection policy, exhibiting performance comparable to human-operated inspections.

Although autonomy has gained widespread usage in structured and controlled environments, robotic autonomy in unknown and off-road terrain remains a difficult problem. Extreme, off-road, and unstructured environments such as undeveloped wilderness, caves, rubble, and other post-disaster sites pose unique and challenging problems for autonomous navigation. Based on our participation in the DARPA Subterranean Challenge, we propose an approach to improve autonomous traversal of robots in subterranean environments that are perceptually degraded and completely unknown through a traversability and planning framework called STEP (Stochastic Traversability Evaluation and Planning). We present 1) rapid uncertainty-aware mapping and traversability evaluation, 2) tail risk assessment using the Conditional Value-at-Risk (CVaR), 3) efficient risk and constraint-aware kinodynamic motion planning using sequential quadratic programming-based (SQP) model predictive control (MPC), 4) fast recovery behaviors to account for unexpected scenarios that may cause failure, and 5) risk-based gait adaptation for quadrupedal robots. We illustrate and validate extensive results from our experiments on wheeled and legged robotic platforms in field studies at the Valentine Cave, CA (cave environment), Kentucky Underground, KY (mine environment), and Louisville Mega Cavern, KY (final competition site for the DARPA Subterranean Challenge with tunnel, urban, and cave environments)., Comment: arXiv admin note: substantial text overlap with arXiv:2103.02828

One of the main challenges in autonomous robotic exploration and navigation in unknown and unstructured environments is determining where the robot can or cannot safely move. A significant source of difficulty in this determination arises from stochasticity and uncertainty, coming from localization error, sensor sparsity and noise, difficult-to-model robot-ground interactions, and disturbances to the motion of the vehicle. Classical approaches to this problem rely on geometric analysis of the surrounding terrain, which can be prone to modeling errors and can be computationally expensive. Moreover, modeling the distribution of uncertain traversability costs is a difficult task, compounded by the various error sources mentioned above. In this work, we take a principled learning approach to this problem. We introduce a neural network architecture for robustly learning the distribution of traversability costs. Because we are motivated by preserving the life of the robot, we tackle this learning problem from the perspective of learning tail-risks, i.e. the Conditional Value-at-Risk (CVaR). We show that this approach reliably learns the expected tail risk given a desired probability risk threshold between 0 and 1, producing a traversability costmap which is more robust to outliers, more accurately captures tail risks, and is more computationally efficient, when compared against baselines. We validate our method on data collected a legged robot navigating challenging, unstructured environments including an abandoned subway, limestone caves, and lava tube caves., Comment: Published in RA-L with ICRA presentation option (IEEE International Conference on Robotics and Automation, 2022)

This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved 2nd and 1st place, respectively. We also discuss CoSTAR's demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including: (i) geometric and semantic environment mapping; (ii) a multi-modal positioning system; (iii) traversability analysis and local planning; (iv) global motion planning and exploration behavior; (i) risk-aware mission planning; (vi) networking and decentralized reasoning; and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g. wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition., Comment: For team website, see https://costar.jpl.nasa.gov/. Accepted for publication in the Journal of Field Robotics, 2021

Although ground robotic autonomy has gained widespread usage in structured and controlled environments, autonomy in unknown and off-road terrain remains a difficult problem. Extreme, off-road, and unstructured environments such as undeveloped wilderness, caves, and rubble pose unique and challenging problems for autonomous navigation. To tackle these problems we propose an approach for assessing traversability and planning a safe, feasible, and fast trajectory in real-time. Our approach, which we name STEP (Stochastic Traversability Evaluation and Planning), relies on: 1) rapid uncertainty-aware mapping and traversability evaluation, 2) tail risk assessment using the Conditional Value-at-Risk (CVaR), and 3) efficient risk and constraint-aware kinodynamic motion planning using sequential quadratic programming-based (SQP) model predictive control (MPC). We analyze our method in simulation and validate its efficacy on wheeled and legged robotic platforms exploring extreme terrains including an abandoned subway and an underground lava tube., Comment: Accepted to Robotics: Science and Systems (RSS) 2021. Video link: https://youtu.be/N97cv4eH5c8

In order for an autonomous robot to efficiently explore an unknown environment, it must account for uncertainty in sensor measurements, hazard assessment, localization, and motion execution. Making decisions for maximal reward in a stochastic setting requires value learning and policy construction over a belief space, i.e., probability distribution over all possible robot-world states. However, belief space planning in a large spatial environment over long temporal horizons suffers from severe computational challenges. Moreover, constructed policies must safely adapt to unexpected changes in the belief at runtime. This work proposes a scalable value learning framework, PLGRIM (Probabilistic Local and Global Reasoning on Information roadMaps), that bridges the gap between (i) local, risk-aware resiliency and (ii) global, reward-seeking mission objectives. Leveraging hierarchical belief space planners with information-rich graph structures, PLGRIM addresses large-scale exploration problems while providing locally near-optimal coverage plans. We validate our proposed framework with high-fidelity dynamic simulations in diverse environments and on physical robots in Martian-analog lava tubes.

We propose a framework for resilient autonomous navigation in perceptually challenging unknown environments with mobility-stressing elements such as uneven surfaces with rocks and boulders, steep slopes, negative obstacles like cliffs and holes, and narrow passages. Environments are GPS-denied and perceptually-degraded with variable lighting from dark to lit and obscurants (dust, fog, smoke). Lack of prior maps and degraded communication eliminates the possibility of prior or off-board computation or operator intervention. This necessitates real-time on-board computation using noisy sensor data. To address these challenges, we propose a resilient architecture that exploits redundancy and heterogeneity in sensing modalities. Further resilience is achieved by triggering recovery behaviors upon failure. We propose a fast settling algorithm to generate robust multi-fidelity traversability estimates in real-time. The proposed approach was deployed on multiple physical systems including skid-steer and tracked robots, a high-speed RC car and legged robots, as a part of Team CoSTAR's effort to the DARPA Subterranean Challenge, where the team won 2nd and 1st place in the Tunnel and Urban Circuits, respectively., Comment: 12 Pages, 7 Figures, 2020 International Symposium on Experimental Robotics (ISER 2020)

This paper serves as one of the first efforts to enable large-scale and long-duration autonomy using the Boston Dynamics Spot robot. Motivated by exploring extreme environments, particularly those involved in the DARPA Subterranean Challenge, this paper pushes the boundaries of the state-of-practice in enabling legged robotic systems to accomplish real-world complex missions in relevant scenarios. In particular, we discuss the behaviors and capabilities which emerge from the integration of the autonomy architecture NeBula (Networked Belief-aware Perceptual Autonomy) with next-generation mobility systems. We will discuss the hardware and software challenges, and solutions in mobility, perception, autonomy, and very briefly, wireless networking, as well as lessons learned and future directions. We demonstrate the performance of the proposed solutions on physical systems in real-world scenarios., Comment: Updated copyright statement and acknowledgements

Hybrid ground and aerial vehicles can possess distinct advantages over ground-only or flight-only designs in terms of energy savings and increased mobility. In this work we outline our unified framework for controls, planning, and autonomy of hybrid ground/air vehicles. Our contribution is three-fold: 1) We develop a control scheme for the control of passive two-wheeled hybrid ground/aerial vehicles. 2) We present a unified planner for both rolling and flying by leveraging differential flatness mappings. 3) We conduct experiments leveraging mapping and global planning for hybrid mobility in unknown environments, showing that hybrid mobility uses up to five times less energy than flying only.

It was generally assumed that weak van der Waals interactions exist between neighboring layers in the two-dimensional group-IV chalcogenides. Using PbSe as a prototypal example, however, we find additional strong coupling between the Pb-Pb layers, as evidenced by detailed analysis of the differential charge density. The coupling resembles covalent-like bond and exhibits strong harmonicity around the equilibrium distance, which can be fine tuned to obviously reduce the phonon thermal conductivity but slightly change the electronic transport of PbSe. As a consequence, a maximum ZT value of 2.5 can be realized at 900 K for the p-type system. Our work offers an effective and feasible design strategy to enhance the thermoelectric performance of similar layered structures.

Robots and particularly drones are especially useful in exploring extreme environments that pose hazards to humans. To ensure safe operations in these situations, usually perceptually degraded and without good GNSS, it is critical to have a reliable and robust state estimation solution. The main body of literature in robot state estimation focuses on developing complex algorithms favoring accuracy. Typically, these approaches rely on a strong underlying assumption: the main estimation engine will not fail during operation. In contrast, we propose an architecture that pursues robustness in state estimation by considering redundancy and heterogeneity in both sensing and estimation algorithms. The architecture is designed to expect and detect failures and adapt the behavior of the system to ensure safety. To this end, we present HeRO (Heterogeneous Redundant Odometry): a stack of estimation algorithms running in parallel supervised by a resiliency logic. This logic carries out three main functions: a) perform confidence tests both in data quality and algorithm health; b) re-initialize those algorithms that might be malfunctioning; c) generate a smooth state estimate by multiplexing the inputs based on their quality. The state and quality estimates are used by the guidance and control modules to adapt the mobility behaviors of the system. The validation and utility of the approach are shown with real experiments on a flying robot for the use case of autonomous exploration of subterranean environments, with particular results from the STIX event of the DARPA Subterranean Challenge., Comment: In International Symposium on Robotics Research (ISRR) 2019

Learning-based control aims to construct models of a system to use for planning or trajectory optimization, e.g. in model-based reinforcement learning. In order to obtain guarantees of safety in this context, uncertainty must be accurately quantified. This uncertainty may come from errors in learning (due to a lack of data, for example), or may be inherent to the system. Propagating uncertainty forward in learned dynamics models is a difficult problem. In this work we use deep learning to obtain expressive and flexible models of how distributions of trajectories behave, which we then use for nonlinear Model Predictive Control (MPC). We introduce a deep quantile regression framework for control that enforces probabilistic quantile bounds and quantifies epistemic uncertainty. Using our method we explore three different approaches for learning tubes that contain the possible trajectories of the system, and demonstrate how to use each of them in a Tube MPC scheme. We prove these schemes are recursively feasible and satisfy constraints with a desired margin of probability. We present experiments in simulation on a nonlinear quadrotor system, demonstrating the practical efficacy of these ideas., Comment: RSS 2020 Camera Ready Version

The design of uranium-based thermoelectric materials presents a novel and intriguing strategy for directly converting nuclear heat into electrical power. Using high-level first-principles approach combined with accurate solution of Boltzmann transport equation, we demonstrate that a giant n-type power factor of 13.8 mW/mK^2 and a peak ZT value of 2.2 can be realized in the heavy-fermion UN2 compound at 700 K. Such promising thermoelectric performance arises from the large degeneracy (Nv=14) of heavy conduction band coupled with weak electron-phonon interactions, which is in principle governed by the strong Coulomb correlation among the partially filled U-5f electrons in the face-centered cubic structure. Collectively, our theoretical work suggests that the energetic UN2 is an excellent alternative to efficient radioisotope power conversion, which also uncovers an underexplored area for thermoelectric research.

Deep learning has enjoyed much recent success, and applying state-of-the-art model learning methods to controls is an exciting prospect. However, there is a strong reluctance to use these methods on safety-critical systems, which have constraints on safety, stability, and real-time performance. We propose a framework which satisfies these constraints while allowing the use of deep neural networks for learning model uncertainties. Central to our method is the use of Bayesian model learning, which provides an avenue for maintaining appropriate degrees of caution in the face of the unknown. In the proposed approach, we develop an adaptive control framework leveraging the theory of stochastic CLFs (Control Lyapunov Functions) and stochastic CBFs (Control Barrier Functions) along with tractable Bayesian model learning via Gaussian Processes or Bayesian neural networks. Under reasonable assumptions, we guarantee stability and safety while adapting to unknown dynamics with probability 1. We demonstrate this architecture for high-speed terrestrial mobility targeting potential applications in safety-critical high-speed Mars rover missions., Comment: Corrected an error in section II, where previously the problem was introduced in a non-stochastic setting and wrongly assumed the solution to an ODE with Gaussian distributed parametric uncertainty was equivalent to an SDE with a learned diffusion term. See Lew, T et al. "On the Problem of Reformulating Systems with Uncertain Dynamics as a Stochastic Differential Equation"

Autonomous exploration of unknown environments with aerial vehicles remains a challenge, especially in perceptually degraded conditions. Dust, fog, or a lack of visual or LiDAR-based features results in severe difficulties for state estimation algorithms, which failure can be catastrophic. In this work, we show that it is indeed possible to navigate in such conditions without any exteroceptive sensing by exploiting collisions instead of treating them as constraints. To this end, we present a novel contact-based inertial odometry (CIO) algorithm: it uses estimated external forces with the environment to detect collisions and generate pseudo-measurements of the robot velocity, enabling autonomous flight. To fully exploit this method, we first perform modeling of a hybrid ground and aerial vehicle which can withstand collisions at moderate speeds, for which we develop an external wrench estimation algorithm. Then, we present our CIO algorithm and develop a reactive planner and control law which encourage exploration by bouncing off obstacles. All components of this framework are validated in hardware experiments and we demonstrate that a quadrotor can traverse a cluttered environment using an IMU only. This work can be used on drones to recover from visual inertial odometry failure or on micro-drones that do not have the payload capacity to carry cameras, LiDARs or powerful computers., Comment: In International Symposium on Robotics Research (ISRR) 2019

The electronic and phonon transport properties of graphene-like boron phosphide (BP), boron arsenide (BAs), and boron antimonide (BSb) monolayers are investigated using first-principles calculations and Boltzmann theory. By considering both the phonon-phonon and electron-phonon scatterings, we demonstrate that the strong bond anharmonicity in the BAs and BSb monolayers can dramatically suppress the phonon relaxation time but hardly affects that of electrons. As a consequence, both systems exhibit comparable power factors with that of the BP monolayer but much lower lattice thermal conductivities. Accordingly, a maximum ZT values above 3.0 can be achieved in both BAs and BSb monolayers at optimized carrier concentrations. Interestingly, very similar p- and n-type thermoelectric performance is observed in the BSb monolayer along the armchair direction, which is of vital importance in the fabrication of thermoelectric modules with comparable efficiencies.

Two-dimensional layered materials have attracted tremendous attentions due to their extraordinary physical and chemical properties. Using first-principles calculations and Boltzmann transport theory, we give an accurate prediction of the thermoelectric properties of boron phosphide (BP) bilayer, where the carrier relaxation time is treated within the framework of electron-phonon coupling. It is found that the lattice thermal conductivity of BP bilayer is much lower than that of its monolayer structure, which can be attributed to the presence of van der Waals interactions. On the other hand, the graphene-like BP bilayer shows very high carrier mobility with a moderate band gap of 0.88 eV. As a consequence, a maximum p-type ZT value of ~1.8 can be realized along the x-direction at 1200 K, which is amazingly high for systems consisting of light elements only. Moreover, we obtain almost identical p- and n-type ZT of ~1.6 along the y-direction, which is very desirable for fabrication of thermoelectric modules with comparative efficiencies. Collectively, these findings demonstrate great advantages of the layered structures containing earth-abundant elements for environment-friendly thermoelectric applications.

Large-size populations consisting of a continuum of identical and non-cooperative agents with stochastic dynamics are useful in modeling various biological and engineered systems. This paper addresses the stochastic control problem of designing optimal state-feedback controllers which guarantee the closed-loop stability of the stationary density of such agents with nonlinear Langevin dynamics, under the action of their individual steady state controls. We represent the corresponding coupled forward-backward PDEs as decoupled Schr\"odinger equations, by applying two variable transforms. Spectral properties of the linear Schr\"odinger operator which underlie the stability analysis are used to obtain explicit control design constraints. Our interpretation of the Schr\"odinger potential as the cost function of a closely related optimal control problem motivates a quadrature based algorithm to compute the finite-time optimal control., Comment: 21 pages, 2 figures

Low-dimensional layered materials have attracted tremendous attentions due to their wide range of physical and chemical properties and potential applications in electronic devices. Using first-principles method taking into account the quasiparticle self-energy correction and Boltzmann transport theory, the electronic transport properties of ZrSe3 monolayer are investigated, where the carrier relaxation time is accurately calculated within the framework of electron-phonon coupling. It is demonstrated that the high power factor of the monolayer can be attributed to the grooved bands near the conduction band minimum. Combined with the low lattice thermal conductivity obtained by solving the phonon Boltzmann transport equation, a considerable n-type ZT value of ~2.4 can be achieved at 800 K in the ZrSe3 monolayer.

We present a model to show that heat propagation away from a local source depends strongly on dimensionality, leading to dramatic localization in low-dimensional systems. An example of such a system is a carbon nanotube array. We further show that this localization is amplified due to a runaway mechanism if thermal conductivity declines rapidly with temperature. Extremely high temperatures of thousands of Kelvins and gradients of hundreds of K/{\mu}m may thus be obtained in a conductor using a modest local power source such as a laser pointer. This is of fundamental importance for high-efficiency energy conversion through thermoelectric and thermionic mechanisms, as well as various other applications., Comment: Main text 19 pages, 5 figures; Supplemental material 9 pages, 6 figures

Recent work from the reinforcement learning community has shown that Evolution Strategies are a fast and scalable alternative to other reinforcement learning methods. In this paper we show that Evolution Strategies are a special case of model-based stochastic search methods. This class of algorithms has nice asymptotic convergence properties and known convergence rates. We show how these methods can be used to solve both cooperative and competitive multi-agent problems in an efficient manner. We demonstrate the effectiveness of this approach on two complex multi-agent UAV swarm combat scenarios: where a team of fixed wing aircraft must attack a well-defended base, and where two teams of agents go head to head to defeat each other., Comment: Video at http://goo.gl/dWvQi7 Code freely available at http://github.com/ddfan/swarm_evolve

In this paper we investigate the use of MPC-inspired neural network policies for sequential decision making. We introduce an extension to the DAgger algorithm for training such policies and show how they have improved training performance and generalization capabilities. We take advantage of this extension to show scalable and efficient training of complex planning policy architectures in continuous state and action spaces. We provide an extensive comparison of neural network policies by considering feed forward policies, recurrent policies, and recurrent policies with planning structure inspired by the Path Integral control framework. Our results suggest that MPC-type recurrent policies have better robustness to disturbances and modeling error., Comment: Fixed missing reference to section 4.1

The electronic and phonon transport properties of quaternary tetradymite BiSbSeTe2 are investigated using first-principles approach and Boltzmann transport theory. Unlike the binary counterpart Bi2Te3, we obtain a pair of Rashba splitting bands induced by the absence of inversion center. Such unique characteristic could lead to a large Seebeck coefficient even at relatively higher carrier concentration. Besides, we find an ultralow lattice thermal conductivity of BiSbSeTe2, especially along the interlayer direction, which can be traced to the extremely small phonon relaxation time mainly induced by the mixed covalent bonds. As a consequence, a considerably large ZT value of ~2.0 can be obtained at 500 K, indicating that the unique lattice structure of BiSbSeTe2 caused by isoelectronic substitution could be an advantage to achieving high thermoelectric performance.

It is generally assumed in the thermoelectric community that the lattice thermal conductivity of a given material is independent of the electronic properties. This perspective is however questionable since the electron-phonon coupling could have certain effects on both the carrier and phonon transport, which in turn will affect the thermoelectric properties. Using SiGe compound as a prototypical example, we give an accurate prediction of the carrier relaxation time by considering scattering from all the phonon modes, as opposed to the simple deformation potential theory. It is found that the carrier relaxation time does not change much with the concentration, which is however not the case for the phonon transport where the lattice thermal conductivity can be significantly reduced by electron-phonon coupling at higher carrier concentration. As a consequence, the figure-of-merit of SiGe compound is obviously enhanced at optimized carrier concentration, and becomes more pronounced at elevated temperature.

We report a study of magnetism and magnetic transitions of hexagonal ErMnO$_3$ single crystals by magnetization, specific heat and heat transport measurements. Magnetization data show that the $c$-axis magnetic field induces three magnetic transitions at 0.8, 12 and 28 T. The specific heat shows a peak at 2.2 K, which is due to a magnetic transition of Er$^{3+}$ moments. For low-$T$ thermal conductivity ($\kappa$), a clear dip-like feature appears in $\kappa(H)$ isotherm at 1--1.25 T for $H \parallel ab$; while in the case of $H \parallel c$, a step-like increase is observed at 0.5--0.8 T. The transition fields in $\kappa(H)$ are in good agreement with those obtained from magnetization, and the anomaly of $\kappa$ can be understood by a spin-phonon scattering scenario. The natures of magnetic structures and corresponding field-induced transitions at low temperatures are discussed., Comment: 11 pages, 14 figures, accepted for publication in Phys. Rev. B

The unique structure of Zintl phase makes it an ideal system to realize the concept of phonon-glass and electron-crystal in the thermoelectric community. In this work, by combining first-principles calculations and Boltzmann transport theory for both electrons and phonons, we demonstrate that the ZT value of Zintl compound KSnSb can reach ~2.6 at 800 K. Such extraordinary thermoelectric performance originates from the large Seebeck coefficient due to multi-valley band structures and particularly very small lattice thermal conductivity caused by mixed-bond characteristics.

Trajectory optimization considers the problem of deciding how to control a dynamical system to move along a trajectory which minimizes some cost function. Differential Dynamic Programming (DDP) is an optimal control method which utilizes a second-order approximation of the problem to find the control. It is fast enough to allow real-time control and has been shown to work well for trajectory optimization in robotic systems. Here we extend classic DDP to systems with multiple time-delays in the state. Being able to find optimal trajectories for time-delayed systems with DDP opens up the possibility to use richer models for system identification and control, including recurrent neural networks with multiple timesteps in the state. We demonstrate the algorithm on a two-tank continuous stirred tank reactor. We also demonstrate the algorithm on a recurrent neural network trained to model an inverted pendulum with position information only., Comment: 7 pages, 6 figures, conference, Decision and Control (CDC), 2016 IEEE 55th Conference on

Using first-principles calculations combined with Boltzmann transport theory, we investigate the effects of topological edge states on the thermoelectric properties of Bi nanoribbons. It is found that there is a competition between the edge and bulk contributions to the Seebeck coefficients. However, the electronic transport of the system is dominated by the edge states because of its much larger electrical conductivity. As a consequence, a room temperature value exceeding 3.0 could be achieved for both p- and n-type systems when the relaxation time ratio between the edge and the bulk states is tuned to be 1000. Our theoretical study suggests that the utilization of topological edge states might be a promising approach to cross the threshold of the industrial application of thermoelectricity.

Using first-principles pseudopotential method and Boltzmann transport theory, we give a comprehensive understanding of the electronic and phonon transport properties of thermoelectric material BiCuSeO. By choosing proper hybrid functional for the exchange-correlation energy, we find that the system is semiconducting with a direct band gap of ~0.8 eV, which is quite different from those obtained previously using standard functionals. Detailed analysis of a three-dimensional energy band structure indicates that there is a valley degeneracy of eight around the valence band maximum, which leads to a sharp density of states and is responsible for a large p-type Seebeck coefficient. Moreover, we find that the density of states effective masses are much larger and results in very low hole mobility of BiCuSeO. On the other hand, we find larger atomic displacement parameters for the Cu atoms, which indicates that the stronger anharmonicity of BiCuSeO may originate from the rattling behavior of Cu instead of previously believed Bi atoms.

The two-dimensional graphene-like carbon allotrope, graphyne, has been recently fabricated and exhibits many interesting electronic properties. In this work, we investigate the thermoelectric properties of {\gamma}-graphyne by performing first-principles calculations combined with Boltzmann transport theory for both electron and phonon. The carrier relaxation time is accurately evaluated from the ultra-dense electron-phonon coupling matrix elements calculated by adopting the density functional perturbation theory and Wannier interpolation, rather than the generally used deformation potential theory which only considers the electron-acoustic phonon scattering. It is found that the thermoelectric performance of {\gamma}-graphyne exhibits a strong dependence on the temperature and carrier type. At an intermediate temperature of 600 K, a maximum ZT value of 1.5 and 1.0 can be achieved for the p- and n-type systems, respectively.

Topological insulators are new class of quantum materials, which have insulating energy gaps in bulk, but exhibit gapless edge states or surface states that are protected by time-reversal symmetry at boundary. It was theoretically predicted and experimentally confirmed that the binary tetradymites Bi2Te3, Bi2Se3, and Sb2Te3 are three-dimensional topological insulators. In this work, we demonstrate by first-principles approach that the ignored Sb2Se3, although with relatively smaller spin-orbital coupling strength, can also exhibit topologically protected surface states with a bulk gap of 0.19 eV, as long as the van der Waals interaction is explicitly included in the calculations. Detailed analysis of the band structures of Sb2Se3 thin films indicates that the non-trivial surface state appears at a critical thickness of six quintuple layers.

The lattice thermal conductivity of thermoelectric material CuInTe2 is predicted using classical molecular dynamics simulations, where a simple but effective Morse-type interatomic potential is constructed by fitting first-principles total energy calculations. In a broad temperature range from 300 to 900 K, our simulated results agree well with those measured experimentally, as well as those obtained from phonon Boltzmann transport equation. By introducing the Cd impurity and Cu vacancy, the thermal conductivity of CuInTe2 can be effectively reduced to further enhance the thermoelectric performance of this chalcopyrite compound.

Using first-principles method and Boltzmann theory, we provide an accurate prediction of the electronic band structure and thermoelectric transport properties of alpha-MgAgSb. Our calculations demonstrate that only when an appropriate exchange-correlation functional is chosen can we correctly reproduce the semiconducting nature of this compound. By fine tuning the carrier concentration, the thermoelectric performance of alpha-MgAgSb can be significantly optimized, which exhibits a strong temperature dependence and gives a maximum ZT value of 1.7 at 550 K.

The electronic and transport properties of the half-Heusler compound LaPtSb are investigated by performing first-principles calculations combined with semi-classical Boltzmann theory and deformation potential theory. Compared with many typical half-Heusler compounds, the LaPtSb exhibits obviously larger power factor at room temperature, especially for the n-type system. Together with the very low lattice thermal conductivity, the thermoelectric figure of merit (ZT) of LaPtSb can be optimized to a record high value of 2.2 by fine tuning the carrier concentration.

Phosphorene has attracted tremendous interest recently due to its intriguing electronic properties. However, the thermal transport properties of phosphorene, especially for its allotropes, are still not well-understood. In this work, we calculate the thermal conductivities of five phosphorene allotropes ({\alpha}-, \b{eta}-, {\gamma}-, {\delta}- and {\zeta}-phase) by using phonon Boltzmann transport theory combined with first-principles calculations. It is found that the {\alpha}-phosphorene exhibits considerable anisotropic thermal transport, while it is less obvious in the other four phosphorene allotropes. The highest thermal conductivity is found in the \b{eta}-phosphorene, followed by {\delta}-, {\gamma}- and {\zeta}-phase. The much lower thermal conductivity of the {\zeta}-phase can be attributed to its relatively complex atomic configuration. It is expected that the rich thermal transport properties of phosphorene allotropes can have potential applications in the thermoelectrics and thermal management.

The thermoelectric properties of distorted bismuth (110) layer are investigated using first-principles calculations combined with the Boltzmann transport equation for both electrons and phonons. To accurately predict the electronic and transport properties, the quasiparticle corrections with the GW approximation of many-body effects have been explicitly included. It is found that a maximum ZT value of 6.4 can be achieved for n-type system, which is essentially stemmed from the weak scattering of electrons. Moreover, we demonstrate that the distorted Bi layer remains high ZT values at relatively broad regions of both temperature and carrier concentration. Our theoretical work emphasizes that the deformation potential constant characterizing the electron-phonon scattering strength is an important paradigm for searching high thermoelectric performance materials., Comment: Physical Chemistry Chemical Physics, 2016

Few-layer black phosphorus has recently emerged as a promising candidate for novel electronic and optoelectronic device. Here we demonstrate by first-principles calculations and Boltzmann theory that, black phosphorus could also have potential thermoelectric applications and a fair ZT value of 1.1 can be achieved at elevated temperature. Moreover, such value can be further increased to 5.4 by substituting P atom with Sb atom, giving nominal formula of P0.75Sb0.25. Our theoretical work suggests that high thermoelectric performance can be achieved without using complicated crystal structure or seeking for low-dimensional systems.

The electronic and transport properties of CuInTe2 chalcopyrite are investigated using density functional calculations combined with Boltzmann theory. The band gap predicted from hybrid functional is 0.92 eV, which agrees well with experimental data and leads to relatively larger Seebeck coefficient compared with those of narrow-gap thermoelectric materials. By fine tuning the carrier concentration, the electrical conductivity and power factor of the system can be significantly optimized. Together with the inherent low thermal conductivity, the ZT values of CuInTe2 compound can be enhanced to as high as 1.72 at 850 K, which is obviously larger than those measured experimentally and suggests there is still room to improve the thermoelectric performance of this chalcopyrite compound.

The electronic and transport properties of (10, 0) single-walled carbon nanotube are studied by performing the first-principles calculations and semi-classical Boltzmann theory. It is found that the (10, 0) tube exhibits considerably large Seebeck coefficient and electrical conductivity which is highly desirable for good thermoelectric materials. Together with the lattice thermal conductivity predicted by non-equilibrium molecular dynamics simulations, the room temperature ZT value of (10, 0) tube is estimated to be 0.15 for p-type carriers. Moreover, the ZT value exhibits strong temperature dependence and can be reached to 0.77 at 1000 K. Such ZT value can be further enhanced to as high as 1.7 by isotope substitution and chemisorptions of hydrogen on the tube.

As a new carbon allotrope, the recently fabricated graphdiyne has attracted much attention due to its interesting two-dimensional character. Here we demonstrate by multiscale computations that, unlike graphene, graphdiyne has a natural band gap, and simultaneously possess high electrical conductivity, large Seebeck coefficient, and low thermal conductivity. At a carrier concentration of 2.74*10^11/cm^2 for holes and 1.62*10^11/cm^2 for electrons, the room temperature ZT value of graphdiyne can be optimized to 3.0 and 4.8, respectively, which makes it an ideal system to realize the concept of "phonon-glass and electron-crystal" in the thermoelectric community.