Tohid Kargar Tasooji

I am currently a Postdoctoral Research Fellow at the Heterogeneous Robotics (HeRo) Research Lab, working with Prof. Ramviyas Parasuraman. I am developing advanced algorithms for multi-robot systems, focusing on resilient cooperative localization, communication-aware coverage, and learning-enabled control strategies for autonomous navigation in dynamic and adversarial environments. My work integrates event-triggered control, model predictive frameworks, and hierarchical decision-making to enhance the robustness, safety, and efficiency of heterogeneous robotic teams.

I received my PhD in Control and Robotic Engineering from the University of Alberta in 2023. During my PhD, I led efforts in the Advanced Control Systems Lab, working on event-triggered cooperative control and localization for multi-robot systems, designing resilient and secure control schemes, and developing real-time algorithms for autonomous navigation and multi-agent coordination.

I also worked at General Motors, where I developed algorithms for vehicle dynamics, autonomous driving, and ADAS features, including route optimization, model-based control strategies, and simulation-based testing using MIL, SIL, and HIL environments.

Email / Google Scholar / LinkedIn / CV
Tohid Kargar Tasooji

Publications

Distributed Fault-Tolerant Multi-Robot Cooperative Localization in Adversarial Environments

Tohid Kargar Tasooji*, Ramviyas Parasuraman

2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2025).

[ Paper / Code ]

In multi-robot systems (MRS), cooperative localization is a crucial task for enhancing system robustness and scalability, especially in GPS-denied or communication-limited environments. However, adversarial attacks, such as sensor manipulation, and communication jamming, pose significant challenges to the performance of traditional localization methods. In this paper, we propose a novel distributed fault-tolerant cooperative localization framework to enhance resilience against sensor and communication disruptions in adversarial environments. We introduce an adaptive event-triggered communication strategy that dynamically adjusts communication thresholds based on real-time sensing and communication quality. This strategy ensures optimal performance even in the presence of sensor degradation or communication failure. Furthermore, we conduct a rigorous analysis of the convergence and stability properties of the proposed algorithm, demonstrating its resilience against bounded adversarial zones and maintaining accurate state estimation. Robotarium-based experiment results show that our proposed algorithm significantly outperforms traditional methods in terms of localization accuracy and communication efficiency, particularly in adversarial settings. Our approach offers improved scalability, reliability, and fault tolerance for MRS, making it suitable for large-scale deployments in real-world, challenging environments.

H-Cov: Multi-UAV Sensor Coverage with Altitude Optimization for Target Tracking

Swaraj Nistane, Tohid Kargar Tasooji*, Ramviyas Parasuraman

IEEE ICRA 2025 Workshop on 25 YEARS OF AERIAL ROBOTICS: CHALLENGES AND OPPORTUNITIES.

[ Paper ]

This paper presents a distributed multi-target coverage control framework for multiple unmanned aerial vehicle (UAV) systems that integrates Voronoi-based coverage control with altitude optimization. The proposed method enables robots to adjust their positions and altitudes dynamically to optimize sensing performance across varying target distributions. The framework effectively balances trade-offs between detection costs and coverage area by determining the optimal position and altitude for each robot. This allows the system to adapt to different environmental conditions and target densities, ensuring optimal performance in various scenarios. In the proposed framework, robots descend to lower altitudes in high-density target regions to improve detection accuracy, while in sparse regions, they ascend to maximize coverage. A minimum altitude constraint is obtained to maintain precise tracking in dense areas, ensuring that robots do not operate at excessively low altitudes. The approach guarantees complete coverage of the target space by guiding robots toward the weighted centroids of their respective Voronoi cells, thereby ensuring efficient task allocation and spatial distribution. Simulation experiments demonstrate the effectiveness of the proposed framework in improving tracking accuracy and coverage efficiency in different environments. The results validate the capability of the framework to handle real-time, multi-target tracking and sensor coverage in complex target distributions.

Cooperative Localization in Mobile Robots Using Event-Triggered Mechanism: Theory and Experiments

Tohid Kargar Tasooji*, Horacio J. Marquez

IEEE Transactions on Automation Science and Engineering.

[ Paper / Code ]

This article addresses a new cooperative localization problem for a team of mobile robots subject to limited communication resources. First, we develop a decentralized event-triggered cooperative localization (DECL) algorithm for multirobot system such that each robot localizes itself with minimum communication exchange between robots. Then, using an event-triggered mechanism we propose an optimization framework to achieve a balance between estimation performance and communication rate. Simulation results show the main benefits of the event-triggered mechanism. Also, experimental results using four e-puck2 mobile robots demonstrate the effectiveness of the proposed method. Note to Practitioners—Multiple mobile robots are able to implement certain tasks that are beyond the capabilities of individual robots. In multirobot system, the accurate localization of each robot in the team is essential for a successful operation. Existing cooperative localization approaches neglect some realistic limitations of mobile robots, such as battery capacity and communication bandwidth. Especially, this issue is important when the number of sensors, actuators, and robots in the team increases. This article was motivated by these realistic limitations of mobile robots and it suggests a new approach for cooperative localization based on event-triggered mechanism. Motived by the aforementioned discussion, our objective is to design and implement the event-triggered cooperative localization for a group of e-puck2 robots. Our theoretical analysis and experimental results show that we achieve a tradeoff between localization accuracy and communication resources.

Event-Triggered Consensus Control for Multirobot Systems With Cooperative Localization

Tohid Kargar Tasooji*, Horacio J. Marquez

IEEE Transactions on Industrial Electronics.

[ Paper / Code ]

In multirobot systems, the accurate localization of each mobile robot in the team is a prerequisite to reach consensus. This article investigates the problem of event-triggered consensus control for a group of mobile robots based on cooperative localization (CL). In our framework, each robot employs the position estimates from CL to jointly achieve consensus. An event-triggered mechanism based on a mixed-type condition is adopted in order to reduce the frequency of control updates and unnecessary transmission of information between system components. Our goal is to design an event-triggered consensus controller based on CL such that the closed-loop system achieves the prescribed consensus in spite of inaccurate sensor measurements. We provide sufficient conditions that guarantee the desired consensus using eigenvalues and eigenvectors of the Laplacian matrix. We design the controller and filter gains as well as the parameters of the event-triggering mechanism simultaneously in terms of the solution for a linear matrix inequality. Finally, simulation and experimental results are used to demonstrate the effectiveness of proposed approach.

Distributed Adaptive Event-Based Cubature Kalman Filter for Multi-Robot Cooperative Localization

Tohid Kargar Tasooji*, Ramviyas Parasuraman

Submitted to the 2026 International Conference on Robotics and Automation (ICRA 2026).

[Code ]

Cooperative multi-robot systems (MRS) are a promising solution for various applications such as autonomous driving, search and rescue, and warehouse logistics. A crucial challenge for these systems is cooperative localization, where multiple robots estimate their relative positions to enhance accuracy in GPS/localization-denied environments. Traditional methods, such as the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), struggle with nonlinear motion models, sensor noise, and computational inefficiencies, often leading to filter divergence in dynamic scenarios. These challenges are further exacerbated in large-scale MRS, where maintaining reliable communication becomes increasingly difficult due to dynamic network topologies, constrained bandwidth, and the high computational and communication demands of processing dense inter-robot measurements in real-time. To address these challenges, this paper introduces a distributed adaptive event-based Cubature Kalman Filter (DEB-CKF) that integrates an event-triggered mechanism with a novel CKF to enhance cooperative localization accuracy while optimizing communication and computation efficiency. Specifically, an adaptive event filter dynamically adjusts data transmission based on network topology and position estimates. We theoretically and experimentally analyze the convergence and stability of the proposed algorithm and validate it against EKF and UKF methods in a leader-follower scenario. Experimental results demonstrate that DEB-CKF achieves superior localization accuracy and communication efficiency, particularly in highdensity networks with dynamic maneuvers.

Safe and Adaptive Multi-Robot Formation Control via Composed CBF-QP in the Presence of Dynamic and Uncertain Obstacles

Tohid Kargar Tasooji*, Ramviyas Parasuraman

Submitted to the 2026 International Conference on Robotics and Automation (ICRA 2026).

[ Code ]

This paper introduces a decentralized control framework for safe and adaptive multi-robot formation in dynamic and uncertain environments. The proposed approach formulates a generalized quadratic program (QP) that integrates multiple Control Barrier Functions (CBFs) for safety, connectivity preservation, and obstacle avoidance with a Control Lyapunov Function (CLF) for goal convergence. To resolve inherent conflicts among these objectives, we present the first integration of behavior trees with CBF–CLF–QP control, providing a structured and modular mechanism for prioritized switching across safety, connectivity, formation, and goal-reaching tasks without deadlock. The proposed framework is fully decentralized, computationally efficient, and scalable, with each agent solving a local QP in sub-millisecond cycles, making it practical for real-time, on-board implementation. Extensive simulations and experiments in cluttered, dynamic environments with moving obstacles and uncertain obstacle geometries demonstrate the method’s ability to ensure safety, maintain network connectivity, and achieve resilient formation control while adapting to rapidly changing conditions.

Hybrid Framework for Multi-Robot Target Search in Unknown and Adversarial Environments

Tohid Kargar Tasooji*, Ramviyas Parasuraman

Submitted to the 2026 International Conference on Robotics and Automation (ICRA 2026).

[ Code ]

This paper presents a hybrid approach to multitarget search in unknown environments using multi-robot systems, designed to adapt to varying communication conditions and adversary levels. The proposed method addresses key challenges, including limited sensing range and inaccurate decisionmaking due to communication disruptions. To support adaptive decision-making, a grid-based environmental map is employed, integrating information on obstacles, coverage, target occupancy, and communication quality. To overcome communication constraints, we develop an adaptive communication-aware, learning-based distributed model predictive control framework. Under robust communication, the framework leverages shared map data to generate the trajectory of robots. When communication degrades, it autonomously generates reliable predictions based on prior inter-robot interactions, reducing dependence on real-time data exchange. Additionally, a probabilistic model incorporating layered circular danger zones with a radially symmetric Gaussian-like distribution is introduced to assess and mitigate the impact of varying adversary levels on communication quality. The approach dynamically balances cooperative search with learning-based local search based on the severity of the adversarial zone. Extensive comparative experiments conducted on diverse maps demonstrate that the proposed framework significantly reduces communication overhead and search time while enhancing prediction accuracy and overall system resilience against different adversary levels.

Event-Triggered Nonlinear Model Predictive Control for Cooperative Cable-Suspended Payload Transportation with Multi-Quadrotors

Tohid Kargar Tasooji*, Sakineh Khodadadi, Guangjun Liu

Submitted to the 2026 International Conference on Robotics and Automation (ICRA 2026).

[ Paper ]

Autonomous Micro Aerial Vehicles (MAVs), particularly quadrotors, have shown significant potential in assisting humans with tasks such as construction and package delivery. These applications benefit greatly from the use of cables for manipulation mechanisms due to their lightweight, low-cost, and simple design. However, designing effective control and planning strategies for cable-suspended systems presents several challenges, including indirect load actuation, nonlinear configuration space, and highly coupled system dynamics. In this paper, we introduce a novel event-triggered distributed Nonlinear Model Predictive Control (NMPC) method specifically designed for cooperative transportation involving multiple quadrotors manipulating a cable-suspended payload. This approach addresses key challenges such as payload manipulation, inter-robot separation, obstacle avoidance, and trajectory tracking, all while optimizing the use of computational and communication resources. By integrating an event-triggered mechanism, our NMPC method reduces unnecessary computations and communication, enhancing energy efficiency and extending the operational range of MAVs. The proposed method employs a lightweight state vector parametrization that focuses on payload states in all six degrees of freedom, enabling efficient planning of trajectories on the SE(3) manifold. This not only reduces planning complexity but also ensures real-time computational feasibility. Our approach is validated through extensive simulation, demonstrating its efficacy in dynamic and resource-constrained environments.

Decentralized Learning-Enabled Control Barrier Functions for Resilient Multi-Robot Navigation in Uncertain Adversarial Environments

Tohid Kargar Tasooji*, Ramviyas Parasuraman

Submitted to the 2026 International Conference on Robotics and Automation (ICRA 2026).

[ Code ]

Multi-robot systems operating in dynamic and adversarial environments face challenges such as communication disruptions, sensor failures, and cyberattacks, compromising navigation, safety, and coordination. This paper proposes a decentralized resilience framework that integrates Reinforcement Learning (RL) with Control Barrier Functions (CBFs) to enable adaptive and safe navigation under adversarial conditions. First, we introduce a probabilistic adversary model based on a radially symmetric Gaussian-like distribution. This model allows each robot to estimate adversarial region parameters via likelihood maximization and refine these estimates through collaborative consensus with neighboring agents. Second, we develop an RL-based control framework augmented with a CBFbased safety mechanism to ensure real-time policy adaptation while enforcing safety constraints. During exploration, robots actively navigate adversarial regions to learn their characteristics. In the exploitation phase, they leverage the learned policy to achieve task objectives while maintaining safety guarantees. Multi-robot experiments validate the proposed framework’s ability to ensure system stability, enforce safety constraints, and dynamically adapt trajectories in GPS-denied and adversarial environments. The results demonstrate superior performance over state-of-the-art baselines, showcasing the effectiveness of integrating probabilistic adversary modeling, decentralized situational awareness, and adaptive safety-aware control, ultimately paving the way for key advances in realizing resilient multi-robot navigation in uncertain environments.

Multi-Robot Consensus and Coordination with Localization Uncertainty

Tohid Kargar Tasooji*, Ramviyas Parasuraman

Submitted to the 2026 International Conference on Robotics and Automation (ICRA 2026).

[ Code ]

Multi-robot coordination requires robots to share or obtain their position information using sensors. However, the uncertainty in these position measurements is generally ignored in tight consensus-based coordination protocols like rendezvous, formation control, and role assignments. This paper presents a novel Wasserstein-based consensus control law for robust multi-robot consensus-based coordination, accounting for localization uncertainty. Our primary contribution lies in explicitly modeling each robot’s position as a Gaussian distribution and incorporating theWasserstein distance into the control law. This uncertainty-aware formulation ensures robust performance by mitigating the effects of sensor noise and guarantees exponential convergence to a consensus equilibrium under realistic conditions. We further contribute a rigorous convergence analysis that establishes a lower bound on the convergence rate as a function of uncertainty bounds and network topology. Extensive simulations conducted in the Robotarium environment validate the scalability and efficacy of our approach, demonstrating that it outperforms traditional Euclidean distance-based controllers, particularly in high-uncertainty settings. The proposed method consistently maintains sub-meter consensus convergence accuracy and achieves stable formation shapes based on consensus across various geometries and team sizes. These contributions provide a practical means to incorporate uncertainty into consensus controllers, demonstrating effectiveness in multirobot coordination for time-critical applications such as search and rescue, exploration, and coordinated surveillance.

A Secure Decentralized Event-Triggered Cooperative Localization in Multi-Robot Systems Under Cyber Attack

Tohid Kargar Tasooji*, Horacio J. Marquez

IEEE Access

[ Paper / Code ]

We study the problem of secure decentralized event-triggered cooperative localization (SDECL) for a team of mobile robots in an adversarial environment, where the objective is to perform localization in the presence of a malicious attacker. We consider a scenario in which an intruder is able to attack the communication channels between exteroceptive sensors and filter of the robot and between two robots independently. First, we design a secure decentralized event-triggered cooperative localization in the multi-robot system against random Denial of Service (DoS) and False Data Injection (FDI) attacks. Then, we provide sufficient conditions that ensure the resilience and convergence of the proposed algorithm when the attacker signal rate is bounded. Simulation results show that by properly tuning the parameters of the event-triggered mechanism and considering bounded attack rate, the proposed algorithm is resilient against cyber attacks. Also, experimental results using four e-puck2 mobile robots have demonstrated the effectiveness of the proposed method.

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Design and Simulation of an Optimal Energy Management Strategy for Plug-In Electric Vehicles

Mehmet Ali Gozukucuk, Taylan Akdogan, Waqas Hussain, Tohid Kargar Tasooji*, Mert Sahin, Mert Celik, H. Fatih Ugurdag
International Conference on Control Engineering & Information Technology, 2018
[ Paper]
Energy management algorithms play a critical role in improving the energy efficiency of modern electric vehicles. In order to be desirable for the customer, electric vehicles should be capable of long distance driving on a single battery charge with a range which must be comparable to the values of their conventional counterparts. To achieve this goal, both the use of large-capacity battery and the development of a custom energy management algorithm are necessary. Thus, one must solve equations of vehicle dynamics, which is a part of conventional methods used in generalized energy management problems. In this paper, a Monte Carlo method is proposed for probabilistic prediction of the optimum energy to attain a given route. The route in question is obtained from the Google Maps and includes locations and road topologies. First, optimum speed set-points are generated for each state of the journey, and this generated speed array is imported into the vehicle control system to generate the required torque for vehicle propulsion. Then, this process is repeated with a constant average speed for comparison purposes. The simulation results show that an electric vehicle gains significant energy efficiency over a Hardware in the Loop (HIL) emulation, when it is being controlled with the proposed speed set-points generated by the Monte Carlo method.
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Observer-Based Secure Control for Vehicular Platooning Under DoS Attacks

Sakineh Khodadadi, Tohid Kargar Tasooji*, Horacio J. Marquez
IEEE Access
[ Paper ]
This paper investigates an observer-based secure control problem for platooning of connected vehicles in the presence of Denial-of-Service (DoS) attacks. DoS attacks usually prevent the vehicle-tovehicle data packets transmission which will lead to performance degradation of platooning system or vehicle collision. To deal with DoS attacks, we consider an observer-based mechanism to estimate the state of vehicles based on available sensor measurements which significantly improves the resilience and tolerance of platooning system during the attack interval. Then, we provide the optimization framework to maximize the duration of the DoS attack such that the platooning system can tolerate safe operation without performance degradation. The simulation results verify the effectiveness of the proposed method.
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Event-Based Secure Consensus Control for Multirobot Systems With Cooperative Localization Against DoS Attacks

Tohid Kargar Tasooji*, Sakineh Khodadadi, Horacio J. Marquez
IEEE/ASME Transactions on Mechatronics
[ Paper / Code ]
In this article, we investigate the secure consensus control problem for multirobot systems with eventtriggered communication strategy under aperiodic energylimited denial-of-service (DoS) attacks, where DoS attacks prevent the transmission of information between robots. Each robot is equipped with onboard sensors to estimate its position cooperatively by taking relative measurements and exchanging the local positioning information with other robots through the unreliable communication network. In the meantime, each robot determines its consensus control based on transmitted position estimates and steers the robot to the desired consensus position. Therefore, our goal is to design a secure control scheme for each robot based on cooperative localization with an event-triggered mechanism and obtain a sufficient condition for the upper bound of duration and the maximum number of attacks such that N robots can move to the desired secure consensus position in the presence of DoS attacks. Finally, simulation and experimental results are presented to show the effectiveness of obtained theoretical results.
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DCL-Sparse: Distributed Range-only Cooperative Localization of Multi-Robots in Sparse and Noisy Sensing Graphs

Atharva Sagale, Tohid Kargar Tasooji*, Ramviyas Parasuraman
Submitted to the 2026 International Conference on Robotics and Automation (ICRA 2026)
[ Paper / Code ]
This paper presents a novel approach to range-based distributed cooperative localization (DCL) for robot swarms in GPS-denied environments, relying solely on interrobot range measurements, specifically addressing the limitations of current methods in noisy and sparse settings where the geometric non-rigidity of the sensing graph creates flipping (suboptimal) effects in the localization outcomes. We propose a robust multilayered localization framework (DCL-Sparse) that utilizes distributed 1-hop shadow edges (S1-Edge) to address the non-rigidity problem and improve localization convergence in sparse and noisy sensing graphs. Our approach leverages the advantages of distributed localization methods, enhancing scalability and adaptability in large robot networks. We establish theoretical conditions for the new S1-Edge that ensure solutions exist even in the presence of noise, thereby validating the effectiveness of the new shadow edge localization. Extensive simulation and real-world experiments confirm the superior performance of our method compared to state-of-theart techniques, resulting in a reduction of up to 93% in the localization error in DCL. These experiments demonstrate substantial improvements in localization accuracy and robustness to sparse graphs. DCL-Sparse increases the localizability of large multi-robot and sensor networks, offering a powerful tool for high-performance and reliable operations in challenging large-scale environments.

Multi-UGV Search-and-Rescue

IEEE SMCS Open Source AI-Empowered Human-Machine Teaming (HMT) Competition.

[ Webpage / Code ]

In Search and Rescue missions, robots must operate in cluttered environments with limited sensing and communication. The challenge is to efficiently explore unknown areas while minimizing redundant coverage, ensure reliable and low-latency victim detection, and maintain safe, collision-free navigation with smooth recovery when stuck. Our goal is to design a robust, plug-and-play system that runs in Webots using standard differential-drive robots, enabling scalable deployment in realistic search-and-rescue scenarios. The approach combines cooperative exploration, consensus-based rendezvous, and robust safety control. Each robot maintains a grid map, sharing with teammates to coordinate coverage. Victim detection has the highest priority—robots navigate to the detected victim’s world position or visually track it until depth data is available. Safety mechanisms include short-range braking, mid-field CBF with LiDAR, and stuck detection for autonomous recovery.

University of Georgia and GNEM visit to Beep, Inc.

Enhancing Cooperative Perception for Connected and Automated Vehicles with Event-Based Vision for Critical Scenario Detection

Connected and Automated Vehicles (CAVs) have the potential to significantly improve road safety by enhancing vehicle perception and decision-making. Current perception systems face challenges in critical scenarios requiring high-speed detection and rapid response, such as sudden pedestrian appearances, emergency vehicle maneuvers, and complex work-zone navigation. Traditional sensors like LiDAR and RGB cameras struggle in high-speed, low-light, or occluded conditions, which can delay detection and hinder timely reaction. To address these limitations, this project integrates event-based vision with multi-sensor data fusion from both vehicle-mounted and infrastructure sensors. Event cameras, with their high temporal resolution and asynchronous data acquisition, enable real-time detection and tracking of fast-moving objects. By fusing event-based data with LiDAR, radar, and depth camera information, this framework facilitates predictive perception and low-latency response, ensuring robust detection of critical events for safer and more efficient autonomous driving.
University of Georgia and GNEM visit to Beep, Inc.

Energy consumption modeling and optimization of speed profile for plug-in electric vehicles

Master of science thesis

[ Webpage / Code ]

In recent years, there is a surge in research on Plug-in Electric Vehicles (PEVs), as PEVs are one of the ways that we can reduce carbon emissions. Dramatic cost reduction in PEVs has resulted in their becoming mainstream vehicles. However, there is still one big hurdle before they can take over other types of vehicles, and that is their limited range. Lack of charging stations exacerbates the situation further, and proliferation of charging stations does not completely alleviate the range problem as charging can take a few hours. Therefore, a PEV should cover as much distance with a given battery charge, or equivalently, should consume as little energy as possible for a given distance. The choice of the instantaneous control algorithm plays a role in this energy minimization problem, but on the other hand, the most critical aspect of this minimization problem is the speed profile of the vehicle, which is given as a reference input to the instantaneous control algorithm. This thesis offers a significant contribution to the literature in range optimization of PEVs given a realistic vehicle model, which it does through a combination of Monte Carlo and Newton-Raphson optimization methods.
University of Georgia and GNEM visit to Beep, Inc.

Design and Analysis of a Robust Control System for Triple Inverted Pendulum Stabilization

[ Webpage ]

The design of robust controllers for triple inverted pendulum systems presents significant challenges due to their inherent instability and nonlinear dynamics. Moreover, uncertainties in system parameters further complicate the control design. This paper investigates a robust control strategy for triple inverted pendulums under parameter uncertainty. Two control approaches, namely the H∞ controller and the µ-synthesis controller, are compared in terms of their ability to achieve reference tracking and disturbance rejection. Simulation results demonstrate that the H∞ controller provides superior transient performance, making it a promising solution for robust stabilization of such complex systems.
University of Georgia

Optimal Charging Station Allocation and Path Planning for Multi-Robot Electric Fleets

This project explored mathematical optimization techniques to design efficient charging station allocation and route planning for a group of electric autonomous robots (e.g., delivery robots). The key goal was to minimize total energy consumption while ensuring all robots can complete assigned tasks without running out of battery.

Outreach & Industry Engagement

University of Georgia and GNEM visit to Beep, Inc.

Industry Visit — Beep, Inc.

May 2025

In May 2025, I joined with colleagues from the University of Georgia and members of the Georgia Network for Electric Mobility (GNEM) for a site visit to Beep, Inc.. During the visit, we discussed opportunities to establish research partnerships and develop innovative autonomous mobility solutions that advance sustainable and intelligent transportation systems.