About

Professor Maruthi R. Akella holds the Cockrell Family Chair #19 in Engineering at The University of Texas at Austin. He specializes in the control of complex dynamical systems that are subject to large scale nonlinearities and uncertainties. Professor Akella is the founding director of the Center for Autonomous Air Mobility (CAAM), where his research focuses on advancing autonomous systems and control methodologies. His expertise encompasses multi-agent systems, autonomous robotic systems, spacecraft guidance, navigation, and control (GNC), as well as GPS-denied navigation. Through his leadership and research, Professor Akella contributes to the development of innovative control strategies for aerospace and robotic applications, addressing challenges in nonlinear dynamics, adaptive control, and optimal control within complex and uncertain environments.

Research topics

• Artificial Intelligence
• Computer Science
• Computer vision
• Engineering
• Classical mechanics
• Real-time computing
• Mathematics
• Geography
• Geometry
• Physics
• Statistics
• Aerospace engineering
• Algorithm
• Remote sensing
• Mathematical analysis

Selected publications

• Rapid Response Missions to Interstellar Objects Using Lyapunov Wait-in-Orbit Constellations

  Journal of Spacecraft and Rockets · 2026-01-26

  articleSenior author

  The discovery of 1I/‘Oumuamua and 2I/Borisov triggered a surge of interest in the exploration of interstellar objects (ISOs). This paper proposes a conceptual framework for rapid response missions to these objects by deploying a constellation of satellites in Lagrange point orbits, where they loiter until optimal departure conditions for interception are met. The spacecraft intercept the ISOs via three different strategies: a purely ballistic transfer, a transfer utilizing a powered Earth flyby, and one executing a deep space maneuver after departing the Lagrange point orbit via an invariant manifold. We evaluate whether deploying a fleet of satellites in Lagrange point orbits and employing one of these three strategies reduces mission duration while optimizing costs compared to the Lambert transfer approach departing from the Lagrange points.

  PublisherDOI

• Auction-Based Task Allocation Under Untruthful Agents

  2026-01-08

  articleSenior author

  In this paper, we propose an iterative task negotiation algorithm for distributed multi-agent systems in the presence of deceptive agents. Compared to the baseline consensus-based bundle algorithm, the proposed method integrates a second-price payment scheme that incentivizes truthful bidding among honest agents. This framework can capture both truthful behavior and strategic deception, wherein agents may overbid to secure additional utility regardless of payment. The auction-based allocation algorithm is embedded within an iterative negotiation loop. In each iteration, agents estimate the trustworthiness of their opponents using Bayesian inference, which in turn determines the extent of task information to disclose. This enables agents to limit negotiation with deceptive counterparts while maintaining full engagement with trustworthy ones by adaptively adjusting the accessible task set based on opponent type. The effectiveness of the proposed algorithm is demonstrated in a 2D multi-agent task allocation scenario involving varying degrees of deception.

  PublisherDOI

• Explicit Symplectic Integrators for Cislunar Regimes

  Journal of Guidance Control and Dynamics · 2026-02-12

  articleSenior author

  Hamiltonian mechanics provides the mathematical foundations for analytical solutions and geometric insights into a range of mechanical systems that remain elusive through alternative methods. These rich insights conjoined with perturbation theory provide valuable avenues for computing approximate solutions for astrodynamics problems. In numerous instances documented in the literature, separable Hamiltonians can use explicit symplectic integrators, such as the Störmer Verlet method, to preserve phase space and the underlying constants of motion during numerical propagation. This paper establishes a crucial result that there exists a broad class of nonintegrable dynamic systems that include both the circular and elliptic restricted three-body problems wherein the Hamiltonian is nonseparable; it is indeed possible to realize explicit symplectic integrators. Furthermore, our numerical analysis in this paper confirms that using these explicit symplectic integrators provides computational accelerations and improved numerical stability for long-term state propagation and maintaining first integrals when compared with conventional nonsymplectic approaches.

  PublisherDOI

• Game-Theoretic Distributed Sensor Tasking in Earth Observation Satellite Constellations

  Journal of Guidance Control and Dynamics · 2026-01-01

  articleSenior author

  This paper presents a game-theoretic sensor selection for coordinating multiple satellites, where each satellite adjusts its roll angle to observe multiple targets. The main objective is to find a joint sensor schedule for the satellite constellation over a finite time horizon that minimizes both mean square error (MSE) and slew costs, which is a challenging combinatorial optimization problem. Standard MSE-based sensor scheduling problems typically involve optimizing non-submodular functions, which introduces computational complexity in designing algorithms that achieve provable performance guarantees. To address this issue, we propose a submodular surrogate objective function that is monotonic under specific conditions for the weights on MSE and slew cost terms. This reformulation enables the problem to be modeled as a potential game, facilitating the use of a distributed algorithm to find a Nash equilibrium that provides a bounded efficiency. The proposed solution is compared with an alternative game-theoretic solution using a different performance metric, as well as with a centralized greedy solution, to demonstrate the performance of the surrogate approach, particularly in the presence of isolated subgraphs and stochastic communication delays. Additionally, the versatility of the proposed scheme is highlighted, as it is applicable to heterogeneous satellites and sensors with disconnected communication graphs.

  PublisherDOI

• Indirect Optimal Control Bootstrapped via Suboptimal Policies

  Journal of Guidance Control and Dynamics · 2025-03-17

  articleSenior author

  This paper presents a framework for numerical continuation that can transform a previously known, potentially suboptimal, control history into a minimum effort control history without needing to find the appropriate initial costate values for the known solution. This formulation is motivated by the fact that analytical and/or approximate solutions to aerospace control problems generally are significantly easier to compute compared to corresponding optimal trajectories for the same boundary conditions. Moreover, numerical methods for computing indirect optimal control solutions often greatly benefit from having an initial guess that is “close” to the optimal solution. For this reason, it is often desirable to produce a quickly computable approximate solution that can bootstrap an optimal control solution process. Salient to note here is that using an approximate solution as an initial guess for an indirect optimal control solver requires the user to find the appropriate initial costate values corresponding to the previously known control history, which is generally a nontrivial problem itself. The proposed algorithm provides a systematic framework for addressing this initial costate generation hurdle together with strong convergence properties. The methodology is applied and illustrated for a wide array of benchmark control problems.

  PublisherDOI

• Attitude and Gyro Bias Estimation with Intermittent Measurements

  Journal of Guidance Control and Dynamics · 2025-06-18

  articleSenior author
  PublisherDOI

• Indirect Multithread Adaptation for Aerospace Robotics Applications

  Journal of Guidance Control and Dynamics · 2025-11-03

  articleSenior author

  In this paper, a new indirect multithread attracting manifold adaptive controller for a general class of aerospace mechanical/robotic systems is presented. The controller certifies closed-loop stability for any given bounded reference trajectory while adapting many estimates of unknown parameters such that any one particular estimate almost never moves further away from the unknown truth in a distance measure when compared with its current estimate. While maintaining this “no-regret” learning feature for each individual thread, all threads are mixed into a single composite estimate using a judiciously designed weighting scheme that rewards past performance of each individual thread. This provides faster error elimination and superior transient response performance compared with traditional adaptive control implementations that utilize only a single thread. The need for rapid convergence of poorly determined parameters is particularly relevant to on-orbit servicing applications such as capturing non-cooperative space objects and debris. The beneficial features of the proposed multithread learning scheme are demonstrated via numerical simulations of various prototype problems ranging from robotic manipulators to spacecraft control applications.

  PublisherDOI

• Non-Gaussian Distribution Steering in Nonlinear Dynamics with Conjugate Unscented Transformation

  ArXiv.org · 2025-10-14

  preprintOpen accessSenior author

  In highly nonlinear systems such as the ones commonly found in astrodynamics, Gaussian distributions generally evolve into non-Gaussian distributions. This paper introduces a method for effectively controlling non-Gaussian distributions in nonlinear environments using optimized linear feedback control. This paper utilizes Conjugate Unscented Transformation to quantify the higher-order statistical moments of non-Gaussian distributions. The formulation focuses on controlling and constraining the sigma points associated with the uncertainty quantification, which would thereby reflect the control of the entire distribution and constraints on the moments themselves. This paper develops an algorithm to solve this problem with sequential convex programming, and it is demonstrated through a two-body and three-body example. The examples show that individual moments can be directly controlled, and the moments are accurately approximated for non-Gaussian distributions throughout the controller's time horizon in nonlinear dynamics.

  PublisherOA PDFDOI

• Minimum Fuel Indirect Optimal Control Transfers Across Multiple Regimes in the Earth-Moon System

  2025-01-01

  articleSenior author
  PublisherDOI

• A Game-Theoretical Exploration of L1/L2 Cislunar Space Situational Awareness Using Bayesian Games

  2025-01-01

  articleSenior author
  PublisherDOI

Frequent coauthors

• Erik Blasch

  117 shared

• Mark Davis

  117 shared

• Walter Downing

  Southwest Research Institute

  117 shared

• Eli Brookner

  RTX (United States)

  117 shared

• Frederick Daum

  RTX (United States)

  117 shared

• Lorenzo Lo

  Applied Radar (United States)

  108 shared

• Michael Braasch

  Ionic Systems (United States)

  108 shared

• Larry Chasteen

  The University of Texas at Dallas

  108 shared

Labs

• Maruthi R. Akella LabPI

Education

• Ph.D.

  Texas A&M University

Awards & honors

• VAIBHAV (Vaishvik Bharatiya Vaigyanik) Fellow, Indian Govern…
• Distinguished Alumni Award, National Institute of Technology…
• Elected Fellow of AIAA, 2022
• Named Fellow of IEEE, 2021
• Dirk Brouwer Award, American Astronautical Society, 2020