Key Publications

• Crater Observing Bioinspired Rolling Articulator (COBRA)

  Adarsh Salagame, Henry Noyes, Eric Sihite, Arash Kalantari, Alireza Ramezani

  2025 Advanced Intelligent Systems

  NASA aims to establish a sustainable human basecamp on the Moon as a stepping stone for future missions to Mars and beyond. The discovery of water ice on the Moon's craters located in permanently shadowed regions, which can provide drinking water, oxygen, and rocket fuel, is therefore of critical importance. However, current methods to access lunar ice deposits are limited. While rovers have been used to explore the lunar surface for decades, they face significant challenges in navigating harsh terrains. This report introduces Crater Observing Bioinspired Rolling Articulator (COBRA), a multimodal snake robot designed to overcome mobility challenges in Shackleton Crater's rugged environment. COBRA combines slithering and tumbling locomotion to adapt to various crater terrains. In snake mode, it uses sidewinding to traverse flat or low inclined surfaces, while in tumbling mode, it forms a circular barrel by linking its head and tail, enabling rapid movement with minimal energy on steep slopes. Equipped with an onboard computer, stereo camera, inertial measurement unit, and joint encoders, COBRA facilitates real-time data collection and autonomous operation. This article highlights COBRA's robustness and efficiency in navigating extreme terrains through both simulations and experimental validation.

  Open Paper

• NMPC-Based Unified Posture Manipulation and Thrust Vectoring for Fault Recovery

  Adarsh Salagame, Shashwat Pandya, Eric Sihite, Alireza Ramezani, Morteza Gharib

  2025 IEEE Control Systems Letters

  Multi-rotors face significant risks, as actuator failures at high altitudes can easily result in a crash and the robot’s destruction. Therefore, rapid fault recovery in the event of an actuator failure is necessary for the fault-tolerant and safe operation of unmanned aerial robots. In this letter, we present a fault recovery approach based on the unification of posture manipulation and thrust vectoring. The key contributions of this letter are: 1) Derivation of two flight dynamics models (high-fidelity and reduced-order) that capture posture control and thrust vectoring. 2) Design of a controller based on Nonlinear Model Predictive Control (NMPC) and demonstration of fault recovery in simulation using a high-fidelity model of the Multi-Modal Mobility Morphobot (M4) in Simscape.

  Open Paper

• Optimal Trajectory Planning in a Vertically Undulating Snake Locomotion using Contact-implicit Optimization

  Adarsh Salagame, Eric Sihite, Alireza Ramezani

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

  Contact-rich problems, such as snake robot locomotion, offer unexplored yet rich opportunities for optimization-based trajectory and acyclic contact planning. So far, a substantial body of control research has focused on emulating snake locomotion and replicating its distinctive movement patterns using shape functions that either ignore the complexity of interactions or focus on complex interactions with matter (e.g., burrowing movements). However, models and control frameworks that lie in between these two paradigms and are based on simple, fundamental rigid body dynamics, which alleviate the challenging contact and control allocation problems in snake locomotion, remain absent. This work makes meaningful contributions, substantiated by simulations and experiments, in the following directions: 1) introducing a reduced-order model based on Moreau’s stepping-forward approach from differential inclusion mathematics, 2) verifying model accuracy, 3) experimental validation.

  Open Paper

• Vision-Guided Loco-Manipulation with a Snake Robot

  Adarsh Salagame, Sasank Potluri, Keshav Bharadwaj Vaidyanathan, Kruthika Gangaraju, Eric Sihite, Milad Ramezani, Alireza Ramezani

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

  This paper presents the development and integration of a vision-guided loco-manipulation pipeline for Northeastern University’s snake robot, COBRA. The system leverages a YOLOv8-based object detection model and depth data from an onboard stereo camera to estimate the 6-DOF pose of target objects in real time. We introduce a framework for autonomous detection and control, enabling closed-loop loco-manipulation for transporting objects to specified goal locations. Additionally, we demonstrate open-loop experiments in which COBRA successfully performs real-time object detection and loco-manipulation tasks.

  Open Paper

• Heading Control for Obstacle Avoidance using Dynamic Posture Manipulation during Tumbling Locomotion

  Adarsh Salagame, Kruthika Gangaraju, Eric Sihite, Gunar Schirner, Alireza Ramezani

  2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

  Passive tumbling structures are energy efficient, but often sacrifice control authority due to their under actuated nature. Unlike many passive tumbling robots, Northeastern University’s COBRA is a snake robot with eleven articulated joints that transforms into a wheel-like structure with a high degree of posture control during tumbling, and using this posture manipulation, COBRA can control its forward velocity and heading angle while tumbling. This paper presents a mathematical framework that describes the dynamics of posture manipulation during tumbling and identifies two types of control actions that allow it to control its movement. This is validated in hardware testing to demonstrate obstacle avoidance during passive tumbling using only posture manipulation.

  Open Paper

Last synced on 3/31/2026. Google Scholar might have a more recent record.

• Conjugate momentum based thruster force estimate in dynamic multimodal robot

  Shreyansh Pitroda, Eric Sihite, Taoran Liu, Kaushik Venkatesh Krishnamurthy, Chenghao Wang, Adarsh Salagame, Reza Nemovi, Alireza Ramezani, Morteza Gharib

  2025 American Control Conference (ACC)

  In a multi-modal system which combines thruster and legged locomotion such our state-of-the-art Harpy platform to perform dynamic locomotion. Therefore, it is very important to have a proper estimate of Thruster force. Harpy is a bipedal robot capable of legged-aerial locomotion using its legs and thrusters attached to its main frame. we can characterize thruster force using a thrust stand but it generally does not account for working conditions such as battery voltage. In this study, we present a momentum-based thruster force estimator. One of the key information required to estimate is terrain information. we show estimation results with and without terrain knowledge. In this work, we derive a conjugate momentum thruster force estimator and implement it on a numerical simulator that uses thruster force to perform thruster-assisted walking.

  Open Paper

• Crater Observing Bioinspired Rolling Articulator (COBRA)

  Adarsh Salagame, Henry Noyes, Eric Sihite, Arash Kalantari, Alireza Ramezani

  2025 Advanced Intelligent Systems

  NASA aims to establish a sustainable human basecamp on the Moon as a stepping stone for future missions to Mars and beyond. The discovery of water ice on the Moon's craters located in permanently shadowed regions, which can provide drinking water, oxygen, and rocket fuel, is therefore of critical importance. However, current methods to access lunar ice deposits are limited. While rovers have been used to explore the lunar surface for decades, they face significant challenges in navigating harsh terrains. This report introduces Crater Observing Bioinspired Rolling Articulator (COBRA), a multimodal snake robot designed to overcome mobility challenges in Shackleton Crater's rugged environment. COBRA combines slithering and tumbling locomotion to adapt to various crater terrains. In snake mode, it uses sidewinding to traverse flat or low inclined surfaces, while in tumbling mode, it forms a circular barrel by linking its head and tail, enabling rapid movement with minimal energy on steep slopes. Equipped with an onboard computer, stereo camera, inertial measurement unit, and joint encoders, COBRA facilitates real-time data collection and autonomous operation. This article highlights COBRA's robustness and efficiency in navigating extreme terrains through both simulations and experimental validation.

  Open Paper

• Dynamic Quadrupedal Legged and Aerial Locomotion via Structure Repurposing

  Chenghao Wang, Kaushik Venkatesh Krishnamurthy, Shreyansh Pitroda, Adarsh Salagame, Ioannis Mandralis, Eric Sihite, Alireza Ramezani, Morteza Gharib

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

  Multi-modal ground-aerial robots have been extensively studied, with a significant challenge lying in the integration of conflicting requirements across different modes of operation. The Husky robot family, developed at North-eastern University, and specifically the Husky v.2 discussed in this study, addresses this challenge by incorporating posture manipulation and thrust vectoring into multi-modal locomotion through structure repurposing. This quadrupedal robot features leg structures that can be repurposed for dynamic legged locomotion and flight. In this paper, we present the hardware design of the robot and report primary results on dynamic quadrupedal legged locomotion and hovering.

  Open Paper

• NMPC-Based Unified Posture Manipulation and Thrust Vectoring for Fault Recovery

  Adarsh Salagame, Shashwat Pandya, Eric Sihite, Alireza Ramezani, Morteza Gharib

  2025 IEEE Control Systems Letters

  Multi-rotors face significant risks, as actuator failures at high altitudes can easily result in a crash and the robot’s destruction. Therefore, rapid fault recovery in the event of an actuator failure is necessary for the fault-tolerant and safe operation of unmanned aerial robots. In this letter, we present a fault recovery approach based on the unification of posture manipulation and thrust vectoring. The key contributions of this letter are: 1) Derivation of two flight dynamics models (high-fidelity and reduced-order) that capture posture control and thrust vectoring. 2) Design of a controller based on Nonlinear Model Predictive Control (NMPC) and demonstration of fault recovery in simulation using a high-fidelity model of the Multi-Modal Mobility Morphobot (M4) in Simscape.

  Open Paper

• Optimal Trajectory Planning in a Vertically Undulating Snake Locomotion using Contact-implicit Optimization

  Adarsh Salagame, Eric Sihite, Alireza Ramezani

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

  Contact-rich problems, such as snake robot locomotion, offer unexplored yet rich opportunities for optimization-based trajectory and acyclic contact planning. So far, a substantial body of control research has focused on emulating snake locomotion and replicating its distinctive movement patterns using shape functions that either ignore the complexity of interactions or focus on complex interactions with matter (e.g., burrowing movements). However, models and control frameworks that lie in between these two paradigms and are based on simple, fundamental rigid body dynamics, which alleviate the challenging contact and control allocation problems in snake locomotion, remain absent. This work makes meaningful contributions, substantiated by simulations and experiments, in the following directions: 1) introducing a reduced-order model based on Moreau’s stepping-forward approach from differential inclusion mathematics, 2) verifying model accuracy, 3) experimental validation.

  Open Paper

• Quadratic Programming-Based Posture Manipulation and Thrust-vectoring for Agile Dynamic Walking on Narrow Pathways

  Chenghao Wang, Eric Sihite, Kaushik Venkatesh Krishnamurthy, Shreyansh Pitroda, Adarsh Salagame, Alireza Ramezani, Morteza Gharib

  2025 American Control Conference (ACC)

  There has been significant advancement in legged robot’s agility where they can show impressive acrobatic maneuvers, such as parkour. These maneuvers rely heavily on posture manipulation. To expand the stability and locomotion plasticity, we use the multi-modal ability in our legged-aerial platform, the Husky Beta, to perform thruster-assisted walking. This robot has thrusters on each of its sagittal knee joints which can be used to stabilize its frontal dynamic as it walks. In this work, we perform a simulation study of quadruped narrow-path walking with Husky β, where the robot will utilize its thrusters to stably walk on a narrow path. The controller is designed based on a centroidal dynamics model with thruster and foot ground contact forces as inputs. These inputs are regulated using a QP solver to be used in a model predictive control framework. In addition to narrow-path walking, we also perform a lateral push-recovery simulation to study how the thrusters can be used to stabilize the frontal dynamics of our robot.

  Open Paper

• Self-Supervised Cost of Transport Estimation for Multimodal Path Planning

  Vincent Gherold, Ioannis Mandralis, Eric Sihite, Adarsh Salagame, Alireza Ramezani, Morteza Gharib

  2025 IEEE Robotics and Automation Letters

  Autonomous robots operating in real environments are often faced with decisions on how best to navigate their surroundings. In this work, we address a particular instance of this problem: how can a robot autonomously decide on the energetically optimal path to follow given a high-level objective and information about the surroundings? To tackle this problem we developed a self-supervised learning method that allows the robot to estimate the cost of transport of its surroundings using only vision inputs. We apply our method to the multi-modal mobility morphobot (M4), a robot that can drive, fly, segway, and crawl through its environment. By deploying our system in the real world, we show that our method accurately assigns different cost of transports to various types of environments e.g. grass vs smooth road. We also highlight the low computational cost of our method, which is deployed on an Nvidia Jetson Orin Nano robotic compute unit. We believe that this work will allow multi-modal robotic platforms to unlock their full potential for navigation and exploration tasks.

  Open Paper

• Thruster-Assisted Incline Walking of a Legged-Aerial Robot Using Reduced Order Model and Collocation Method

  Kaushik Venkatesh Krishnamurthy, Chenghao Wang, Shreyansh Pitroda, Adarsh Salagame, Eric Sihite, Reza Nemovi, Alireza Ramezani, Morteza Gharib

  2025 American Control Conference (ACC)

  In this study, our aim is to evaluate the effectiveness of thruster-assisted steep slope walking for the Husky Carbon, a quadrupedal robot equipped with custom-designed actuators and plural electric ducted fans, through simulation prior to conducting experimental trials. Thruster-assisted steep slope walking draws inspiration from wing-assisted incline running (WAIR) observed in birds, and intriguingly incorporates posture manipulation and thrust vectoring, a locomotion technique not previously explored in the animal kingdom. Our approach involves developing a reduced-order model of the Husky robot, followed by the application of an optimization-based controller utilizing collocation methods and dynamics interpolation to determine control actions. Through simulation testing, we demonstrate the feasibility of hardware implementation of our controller.

  Open Paper

• Vision-Guided Loco-Manipulation with a Snake Robot

  Adarsh Salagame, Sasank Potluri, Keshav Bharadwaj Vaidyanathan, Kruthika Gangaraju, Eric Sihite, Milad Ramezani, Alireza Ramezani

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

  This paper presents the development and integration of a vision-guided loco-manipulation pipeline for Northeastern University’s snake robot, COBRA. The system leverages a YOLOv8-based object detection model and depth data from an onboard stereo camera to estimate the 6-DOF pose of target objects in real time. We introduce a framework for autonomous detection and control, enabling closed-loop loco-manipulation for transporting objects to specified goal locations. Additionally, we demonstrate open-loop experiments in which COBRA successfully performs real-time object detection and loco-manipulation tasks.

  Open Paper

• Bounding Flight Control of Dynamic Morphing Wings

  Bibek Gupta, Aniket Dhole, Adarsh Salagame, Xuejian Niu, Yizhe Xu, Kaushik Venkatesh, Paul Ghanem, Ioannis Mandralis, Eric Sihite, Alireza Ramezani

  2024 IEEE International Conference on Advanced Intelligent Mechatronics (AIM)

  Vertebrate flyers perform intermittent flights as bounding or oscillating flights for power management. Intermittent flights and the resulting oscillating height during flapping and soaring provide the means of increasing speed without increasing flapping speed. These maneuvers and their robotic biomimicry have remained unexplored so far, which, if understood, can lead to aerial robot designs with endured flight operations. This works attempts to achieve robotic bounding flight using Northeastern’s Aerobat platform. Aerobat can dynamically morph its wings by collapsing them rapidly during each gaitcycle. We present a launcher designed that allows bounding flight experimentation of Aerobat in a computer-aided fashion. We augmented Aerobat with a plural tiny thruster to stabilize its unstable roll, pitch, and yaw dynamics. This paper presents our control design based on extending Aerobat’s states to accommodate unobservable aerodynamic forces and offers experimental results to support our proposed approach.

  Open Paper

• Capture Point Control in Thruster-Assisted Bipedal Locomotion

  Shreyansh Pitroda, Aditya Bondada, Kaushik Venkatesh, Adarsh Salagame, Chenghao Wang, Taoran Liu, Bibek Gupta, Eric Sihite, Reza Nemovi, Alireza Ramezani, Morteza Gharib

  2024 IEEE International Conference on Advanced Intelligent Mechatronics (AIM)

  Despite major advancements in control design that are robust to unplanned disturbances, bipedal robots are still susceptible to falling over and struggle to negotiate rough terrains. By utilizing thrusters in our bipedal robot, we can perform additional posture manipulation and expand the modes of locomotion to enhance the robot’s stability and ability to negotiate rough and difficult-to-navigate terrains. In this paper, we present our efforts in designing a controller based on capture point control for our thruster-assisted walking model named Harpy and explore its control design possibilities. While capture point control based on centroidal models for bipedal systems has been extensively studied, the incorporation of external forces that can influence the dynamics of linear inverted pendulum models, often used in capture point-based works, has not been explored before. The inclusion of these external forces can lead to interesting interpretations of locomotion, such as virtual buoyancy studied in aquatic-legged locomotion. This paper outlines the dynamical model of our robot, the capture point method we use to assist the upper body stabilization, and the simulation work done to show the controller’s feasibility.

  Open Paper

• Dynamic Posture Manipulation During Tumbling for Closed-Loop Heading Angle Control

  Adarsh Salagame, Eric Sihite, Gunar Schirner, Alireza Ramezani

  2024 IEEE International Conference on Advanced Intelligent Mechatronics (AIM)

  Passive tumbling uses natural forces like gravity for efficient travel. But without an active means of control, passive tumblers must rely entirely on external forces. Northeastern University’s COBRA is a snake robot that can morph into a ring, which employs passive tumbling to traverse down slopes. However, due to its articulated joints, it is also capable of dynamically altering its posture to manipulate the dynamics of the tumbling locomotion for active steering. This paper presents a modelling and control strategy based on collocation optimization for real-time steering of COBRA’s tumbling locomotion. We validate our approach using Matlab simulations.

  Open Paper

• Enabling steep slope walking on Husky using reduced order modeling and quadratic programming

  Kaushik Venkatesh Krishnamurthy, Eric Sihite, Chenghao Wang, Shreyansh Pitroda, Adarsh Salagame, Alireza Ramezani, Morteza Gharib

  2024 Report/Whitepaper

  Wing-assisted inclined running (WAIR) observed in some young birds, is an attractive maneuver that can be extended to legged aerial systems. This study proposes a control method using a modified Variable Length Inverted Pendulum (VLIP) by assuming a fixed zero moment point and thruster forces collocated at the center of mass of the pendulum. A QP MPC is used to find the optimal ground reaction forces and thruster forces to track a reference position and velocity trajectory. Simulation results of this VLIP model on a slope of 40 degrees is maintained and shows thruster forces that can be obtained through posture manipulation. The simulation also provides insight to how the combined efforts of the thrusters and the tractive forces from the legs make WAIR possible in thruster-assisted legged systems.

  Open Paper

• Enhanced Capture Point Control Using Thruster Dynamics and QP-Based Optimization for Harpy

  Shreyansh Pitroda, Eric Sihite, Taoran Liu, Kaushik Venkatesh Krishnamurthy, Chenghao Wang, Adarsh Salagame, Reza Nemovi, Alireza Ramezani, Morteza Gharib

  2024 Report/Whitepaper

  Our work aims to make significant strides in understanding unexplored locomotion control paradigms based on the integration of posture manipulation and thrust vectoring. These techniques are commonly seen in nature, such as Chukar birds using their wings to run on a nearly vertical wall. In this work, we developed a capture-point-based controller integrated with a quadratic programming (QP) solver which is used to create a thruster-assisted dynamic bipedal walking controller for our state-of-the-art Harpy platform. Harpy is a bipedal robot capable of legged-aerial locomotion using its legs and thrusters attached to its main frame. While capture point control based on centroidal models for bipedal systems has been extensively studied, the use of these thrusters in determining the capture point for a bipedal robot has not been extensively explored. The addition of these external thrust forces can lead to interesting interpretations of locomotion, such as virtual buoyancy studied in aquatic-legged locomotion. In this work, we derive a thruster-assisted bipedal walking with the capture point controller and implement it in simulation to study its performance.

  Open Paper

• Heading Control for Obstacle Avoidance using Dynamic Posture Manipulation during Tumbling Locomotion

  Adarsh Salagame, Kruthika Gangaraju, Eric Sihite, Gunar Schirner, Alireza Ramezani

  2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

  Passive tumbling structures are energy efficient, but often sacrifice control authority due to their under actuated nature. Unlike many passive tumbling robots, Northeastern University’s COBRA is a snake robot with eleven articulated joints that transforms into a wheel-like structure with a high degree of posture control during tumbling, and using this posture manipulation, COBRA can control its forward velocity and heading angle while tumbling. This paper presents a mathematical framework that describes the dynamics of posture manipulation during tumbling and identifies two types of control actions that allow it to control its movement. This is validated in hardware testing to demonstrate obstacle avoidance during passive tumbling using only posture manipulation.

  Open Paper

• Hierarchical RL-Guided Large-scale Navigation of a Snake Robot

  Shuo Jiang, Adarsh Salagame, Alireza Ramezani, Lawson L.S. Wong

  2024 IEEE International Conference on Advanced Intelligent Mechatronics (AIM)

  Classical snake robot control leverages mimicking snake-like gaits tuned for specific environments. However, to operate adaptively in unstructured environments, gait generation must be dynamically scheduled. In this work, we present a four-layer hierarchical control scheme to enable the snake robot to navigate freely in large-scale environments. The proposed model decomposes navigation into global planning, local planning, gait generation, and gait tracking. Using reinforcement learning (RL) and a central pattern generator (CPG), our method learns to navigate in complex mazes within hours and can be directly deployed to arbitrary new environments in a zero-shot fashion. We use the high-fidelity model of Northeastern’s slithering robot COBRA to test the effectiveness of the proposed hierarchical control approach.

  Open Paper

• How Strong a Kick Should be to Topple Northeastern’s Tumbling Robot?

  Adarsh Salagame, Neha Bhattachan, Andre Caetano, Ian McCarthy, Henry Noyes, Brandon Petersen, Alexander Qiu, Matthew Schroeter, Nolan Smithwick, Konrad Sroka, Jason Widjaja, Yash Bohra, Kaushik Venkatesh, Kruthika Gangaraju, Paul Ghanem, Ioannis Mandralis, Eric Sihite, Arash Kalantari, Alireza Ramezani

  2024 IEEE International Conference on Advanced Intelligent Mechatronics (AIM)

  Rough terrain locomotion has remained one of the most challenging mobility questions. In 2022, NASA’s Innovative Advanced Concepts (NIAC) Program invited US academic institutions to participate NASA’s Breakthrough, Innovative \& Game-changing (BIG) Idea competition by proposing novel mobility systems that can negotiate extremely rough terrain, lunar bumpy craters. In this competition, Northeastern University won NASA’s top Artemis Award award by proposing an articulated robot tumbler called COBRA (Crater Observing Bio-inspired Rolling Articulator). This report briefly explains the underlying principles that made COBRA successful in competing with other concepts ranging from cable-driven to multi-legged designs from six other participating US institutions.

  Open Paper

• Loco-Manipulation with Nonimpulsive Contact-Implicit Planning in a Slithering Robot

  Adarsh Salagame, Kruthika Gangaraju, Harin Kumar Nallaguntla, Eric Sihite, Gunar Schirner, Alireza Ramezani

  2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

  Object manipulation has been extensively studied in the context of fixed base and mobile manipulators. However, the overactuated locomotion modality employed by snake robots allows for a unique blend of object manipulation through locomotion, referred to as loco-manipulation. The following work presents an optimization approach to solving the loco-manipulation problem based on non-impulsive implicit contact path planning for our snake robot COBRA. We present the mathematical framework and show high-fidelity simulation results and experiments to demonstrate the effectiveness of our approach.

  Open Paper

• Narrow-Path, Dynamic Walking Using Integrated Posture Manipulation and Thrust Vectoring

  Kaushik Venkatesh Krishnamurthy, Chenghao Wang, Shreyansh Pitroda, Adarsh Salagame, Eric Sihite, Reza Nemovi, Alireza Ramezani, Morteza Gharib

  2024 IEEE International Conference on Advanced Intelligent Mechatronics (AIM)

  This research concentrates on enhancing the navigational capabilities of Northeastern University’s Husky, a multi-modal quadrupedal robot, that can integrate posture manipulation and thrust vectoring, to traverse through narrow pathways such as walking over pipes and slacklining. The Husky is outfitted with thrusters designed to stabilize its body during dynamic walking over these narrow paths. The project involves modeling the robot using the HROM (Husky Reduced-Order Model) and developing an optimal control framework. This framework is based on polynomial approximation of the HROM and a collocation approach to derive optimal thruster commands necessary for achieving dynamic walking on narrow paths. The effectiveness of the modeling and control design approach is validated through simulations conducted using Matlab.

  Open Paper

• Non-impulsive Contact-Implicit Motion Planning for Morpho-functional Loco-manipulation

  Adarsh Salagame, Kruthika Gangaraju, Harin Kumar Nallaguntla, Bibek Gupta, Eric Sihite, Gunar Schirner, Alireza Ramezani

  2024 IEEE International Conference on Advanced Intelligent Mechatronics (AIM)

  Object manipulation has been extensively studied in the context of fixed base and mobile manipulators. However, the overactuated locomotion modality employed by snake robots allows for a unique blend of object manipulation through locomotion, referred to as loco-manipulation. The following work presents an optimization approach to solving the loco-manipulation problem based on non-impulsive implicit contact path planning for our snake robot COBRA. We present the mathematical framework and show high fidelity simulation results for fixed-shape lateral rolling trajectories that demonstrate the object manipulation.

  Open Paper

• Posture manipulation of thruster-enhanced bipedal robot performing dynamic wall-jumping using model predictive control

  Eric Sihite, Shreyansh Pitroda, Taoran Liu, Chenghao Wang, Kaushik Venkatesh Krishnamurthy, Adarsh Salagame, Reza Nemovi, Alireza Ramezani, Morteza Gharib

  2024 IEEE-RAS 23rd International Conference on Humanoid Robots (Humanoids)

  Multi-modal mobility in robots can enable versatile, adaptable, and plastic locomotion in various environments. The additional mode of mobility can allow the robot to perform maneuvers that it can’t do with a single mode of locomotion and expand the range of locomotion that the robot can do. In this work, we look at a legged-thruster multi-modal robot, Harpy, to perform a multiple wall jump maneuver and vertically climb inside a vent. The problem is defined using a simplified planar reduced order model using a single inertial body and a hybrid model. The controller utilized MPC while on the wall to satisfy the no-slip ground constraints, and the controller’s performance is shown in simulations.

  Open Paper

• Quadratic Programming Optimization for Bio-Inspired Thruster-Assisted Bipedal Locomotion on Inclined Slopes

  Shreyansh Pitroda, Eric Sihite, Kaushik Venkatesh Krishnamurthy, Chenghao Wang, Adarsh Salagame, Reza Nemovi, Alireza Ramezani, Morteza Gharib

  2024 Report/Whitepaper

  Our work aims to make significant strides in understanding unexplored locomotion control paradigms based on the integration of posture manipulation and thrust vectoring. These techniques are commonly seen in nature, such as Chukar birds using their wings to run on a nearly vertical wall. In this work, we show quadratic programming with contact constraints which is then given to the whole body controller to map on robot states to produce a thruster-assisted slope walking controller for our state-of-the-art Harpy platform. Harpy is a bipedal robot capable of legged-aerial locomotion using its legs and thrusters attached to its main frame. The optimization-based walking controller has been used for dynamic locomotion such as slope walking, but the addition of thrusters to perform inclined slope walking has not been extensively explored. In this work, we derive a thruster-assisted bipedal walking with the quadratic programming (QP) controller and implement it in simulation to study its performance.

  Open Paper

• Quadrupedal Locomotion Control On Inclined Surfaces Using Collocation Method

  Adarsh Salagame, Maria Gianello, Chenghao Wang, Kaushik Venkatesh, Shreyansh Pitroda, Rohit Rajput, Eric Sihite, Miriam Leeser, Alireza Ramezani

  2024 American Control Conference (ACC)

  Inspired by Chukars wing-assisted incline running (WAIR), in this work, we employ a high-fidelity model of our Husky Carbon quadrupedal-legged robot to walk over steep slopes of up to 45 degrees. Chukars use the aerodynamic forces generated by their flapping wings to manipulate ground contact forces and traverse steep slopes and even overhangs. By exploiting the thrusters on Husky, we employed a collocation approach to rapidly resolving the joint and thruster actions. Our approach uses a polynomial approximation of the reducedorder dynamics of Husky, called HROM, to quickly and efficiently find optimal control actions that permit high-slope walking without violating friction cone conditions.

  Open Paper

• Reinforcement Learning-Based Model Matching to Reduce the Sim-Real Gap in COBRA

  Adarsh Salagame, Harin Kumar Nallaguntla, Bardia Ardakanian, Eric Sihite, Gunar Schirner, Alireza Ramezani

  2024 Report/Whitepaper

  This paper employs a reinforcement learning-based model identification method aimed at enhancing the accuracy of the dynamics for our snake robot, called COBRA. Leveraging gradient information and iterative optimization, the proposed approach refines the parameters of COBRA's dynamical model such as coefficient of friction and actuator parameters using experimental and simulated data. Experimental validation on the hardware platform demonstrates the efficacy of the proposed approach, highlighting its potential to address sim-to-real gap in robot implementation.

  Open Paper

• Snake Robot with Tactile Perception Navigates on Large-scale Challenging Terrain

  Shuo Jiang, Adarsh Salagame, Alireza Ramezani, Lawson L. S. Wong

  2024 IEEE International Conference on Robotics and Automation (ICRA)

  Along with the advancement of robot skin technology, there has been notable progress in the development of snake robots featuring body-surface tactile perception. In this study, we proposed a locomotion control framework for snake robots that integrates tactile perception to augment their adaptability to various terrains. Our approach embraces a hierarchical reinforcement learning (HRL) architecture, wherein the high-level orchestrates global navigation strategies while the low-level uses curriculum learning for local navigation maneuvers. Due to the significant computational demands of collision detection in whole-body tactile sensing, the efficiency of the simulator is severely compromised. Thus a distributed training pattern to mitigate the efficiency reduction was adopted. We evaluated the navigation performance of the snake robot in complex large-scale cave exploration with challenging terrains to exhibit improvements in motion efficiency, evidencing the efficacy of tactile perception in terrain-adaptive locomotion.

  Open Paper

• Thruster-Assisted Incline Walking

  Kaushik Venkatesh Krishnamurthy, Chenghao Wang, Shreyansh Pitroda, Adarsh Salagame, Eric Sihite, Reza Nemovi, Alireza Ramezani, Morteza Gharib

  2024 Report/Whitepaper

  In this study, our aim is to evaluate the effectiveness of thruster-assisted steep slope walking for the Husky Carbon, a quadrupedal robot equipped with custom-designed actuators and plural electric ducted fans, through simulation prior to conducting experimental trials. Thruster-assisted steep slope walking draws inspiration from wing-assisted incline running (WAIR) observed in birds, and intriguingly incorporates posture manipulation and thrust vectoring, a locomotion technique not previously explored in the animal kingdom. Our approach involves developing a reduced-order model of the Husky robot, followed by the application of an optimization-based controller utilizing collocation methods and dynamics interpolation to determine control actions. Through simulation testing, we demonstrate the feasibility of hardware implementation of our controller.

  Open Paper

• Validation of Tumbling Robot Dynamics with Posture Manipulation for Closed-Loop Heading Angle Control

  Adarsh Salagame, Eric Sihite, Alireza Ramezani

  2024 Report/Whitepaper

  Navigating rugged terrain and steep slopes is a challenge for mobile robots. Conventional legged and wheeled systems struggle with these environments due to limited traction and stability. Northeastern University's COBRA (Crater Observing Bio-inspired Rolling Articulator), a novel multi-modal snake-like robot, addresses these issues by combining traditional snake gaits for locomotion on flat and inclined surfaces with a tumbling mode for controlled descent on steep slopes. Through dynamic posture manipulation, COBRA can modulate its heading angle and velocity during tumbling. This paper presents a reduced-order cascade model for COBRA's tumbling locomotion and validates it against a high-fidelity rigid-body simulation, presenting simulation results that show that the model captures key system dynamics.

  Open Paper

• Actuation and Flight Control of High-DOF Dynamic Morphing Wing Flight by Shifting Structure Response

  Eric Sihite, Adarsh Salagame, Paul Ghanem, Alireza Ramezani

  2023 62nd IEEE Conference on Decision and Control (CDC)

  Bat's dynamically morphing wings are highly versatile with many active and passive modes which allows them to display highly dexterous flight maneuvers. We take inspiration from bat wings and attempt to mimic their high degrees of freedom and flexibility in our small bat robot with dynamically morphing wings called the Aerobat. This small robot uses linkages, or computational structure, to animate the robot's flapping gait. In this work, we present the theoretical framework of using small low-energy actuators, called the primers, to adjust highly sensitive linkages length for changing the robot's flapping gait and use it to control the robot's orientation. This method is applied in a dynamic simulation to show its feasibility.

  Open Paper

• Demonstrating Autonomous 3D Path Planning on a Novel Scalable UGV-UAV Morphing Robot

  Eric Sihite, Filip Slezak, Ioannis Mandralis, Adarsh Salagame, Milad Ramezani, Arash Kalantari, Alireza Ramezani, Morteza Gharib

  2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

  Some animals exhibit multi-modal locomotion capability to traverse a wide range of terrains and environments, such as amphibians that can swim and walk or birds that can fly and walk. This capability is extremely beneficial for expanding the animal's habitat range and they can choose the most energy efficient mode of locomotion in a given environment. The robotic biomimicry of this multi-modal locomotion capability can be very challenging but offer the same advantages. However, the expanded range of locomotion also increases the complexity of performing localization and path planning. In this work, we present our morphing multi-modal robot, which is capable of ground and aerial locomotion, and the implementation of readily available SLAM and path planning solutions to navigate a complex indoor environment.

  Open Paper

• Hovering Control of Flapping Wings in Tandem with Multi-Rotors

  Aniket Dhole, Bibek Gupta, Adarsh Salagame, Xuejian Niu, Yizhe Xu, Kaushik Venkatesh, Paul Ghanem, Ioannis Mandralis, Eric Sihite, Alireza Ramezani

  2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

  This work briefly covers our efforts to stabilize the flight dynamics of Northeatern's tailless bat-inspired micro aerial vehicle, Aerobat. Flapping robots are not new. A plethora of examples is mainly dominated by insect-style design paradigms that are passively stable. However, Aerobat, in addition for being tailless, possesses morphing wings that add to the inherent complexity of flight control. The robot can dynamically adjust its wing platform configurations during gaitcycles, increasing its efficiency and agility. We employ a guard design with manifold small thrusters to stabilize Aerobat's position and orientation in hovering, a flapping system in tandem with a multi-rotor. For flight control purposes, we take an approach based on assuming the guard cannot observe Aeroat's states. Then, we propose an observer to estimate the unknown states of the guard which are then used for closed-loop hovering control of the Guard-Aerobat platform.

  Open Paper

• Progress Towards Untethered Autonomous Flight of Northeastern University Aerobat

  Adarsh Salagame

  2023 Report/Whitepaper

  State estimation and control is a well-studied problem in conventional aerial vehicles such as multi-rotors. But multi-rotors, while versatile, are not suitable for all applications. Due to turbulent airflow from ground effects, multi-rotors cannot fly in confined spaces. Flapping wing micro aerial vehicles have gained research interest in recent years due to their lightweight structure and ability to fly in tight spaces. Further, their soft deformable wings also make them relatively safer to fly around humans. This thesis will describe the progress made towards developing state estimation and controls on Northeastern University's Aerobat, a bio-inspired flapping wing micro aerial vehicle, with the goal of achieving untethered autonomous flight. Aerobat has a total weight of about 40g and an additional payload capacity of 40g, precluding the use of large processors or heavy sensors. With limited computation resources, this report discusses the challenges in achieving perception on such a platform and the steps taken towards untethered autonomous flight.

  Open Paper

• A Letter on Progress Made on Husky Carbon: A Legged-Aerial, Multi-modal Platform

  Adarsh Salagame, Shoghair Manjikian, Chenghao Wang, Kaushik Venkatesh Krishnamurthy, Shreyansh Pitroda, Bibek Gupta, Tobias Jacob, Benjamin Mottis, Eric Sihite, Milad Ramezani, Alireza Ramezani

  2022 Report/Whitepaper

  Animals, such as birds, widely use multi-modal locomotion by combining legged and aerial mobility with dominant inertial effects. The robotic biomimicry of this multi-modal locomotion feat can yield ultra-flexible systems in terms of their ability to negotiate their task spaces. The main objective of this paper is to discuss the challenges in achieving multi-modal locomotion, and to report our progress in developing our quadrupedal robot capable of multi-modal locomotion (legged and aerial locomotion), the Husky Carbon. We report the mechanical and electrical components utilized in our robot, in addition to the simulation and experimentation done to achieve our goal in developing a versatile multi-modal robotic platform.

  Open Paper

• Bang-Bang Control Of A Tail-less Morphing Wing Flight

  Eric Sihite, Xintao Hu, Bozhen Li, Adarsh Salagame, Paul Ghanem, Alireza Ramezani

  2022 Report/Whitepaper

  Bats' dynamic morphing wings are known to be extremely high-dimensional, and they employ the combination of inertial dynamics and aerodynamics manipulations to showcase extremely agile maneuvers. Bats heavily rely on their highly flexible wings and are capable of dynamically morphing their wings to adjust aerodynamic and inertial forces applied to their wing and perform sharp banking turns. There are technical hardware and control challenges in copying the morphing wing flight capabilities of flying animals. This work is majorly focused on the modeling and control aspects of stable, tail-less, morphing wing flight. A classical control approach using bang-bang control is proposed to stabilize a bio-inspired morphing wing robot called Aerobat. Robot-environment interactions based on horseshoe vortex shedding and Wagner functions is derived to realistically evaluate the feasibility of the bang-bang control, which is then implemented on the robot in experiments to demonstrate first-time closed-loop stable flights of Aerobat.

  Open Paper

• Practical Challenges in Landing a UAV on a Dynamic Target

  Adarsh Salagame, Sushant Govindraj, S. N. Omkar

  2022 Report/Whitepaper

  Unmanned Aerial Vehicles grow more popular by the day and applications for them are crossing boundaries of science and industry, with everything from aerial photography to package delivery to disaster management benefiting from the technology. But before they become commonplace, there are challenges to be solved to make them reliable and safe. The following paper discusses the challenges associated with the precision landing of an Unmanned Aerial Vehicle, including methods for sensing and control and their merits and shortcomings for various applications.

  Open Paper

• Precision Landing of a UAV on a Moving Platform for Outdoor Applications

  Adarsh Salagame, Sushant Govindraj, S. N. Omkar

  2022 Report/Whitepaper

  As UAV technology improves, more uses have been found for these versatile autonomous vehicles, from surveillance to aerial photography, to package delivery, and each of these applications poses unique challenges. This paper implements a solution for one such challenge: To land on a moving target. This problem has been addressed before with varying degrees of success, however, most implementations focus on indoor applications. Outdoor poses greater challenges in the form of variables such as wind and lighting, and outdoor drones are heavier and more susceptible to inertial effects. Our approach is purely vision based, using a monocular camera and fiducial markers to localize the drone and a PID control to follow and land on the platform.

  Open Paper

• Unsteady aerodynamic modeling of Aerobat using lifting line theory and Wagner's function

  Eric Sihite, Paul Ghanem, Adarsh Salagame, Alireza Ramezani

  2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

  Flying animals possess highly complex physical characteristics and are capable of performing agile maneuvers using their wings. The flapping wings generate complex wake structures that influence the aerodynamic forces, which can be difficult to model. While it is possible to model these forces using fluidstructure interaction, it is very computationally expensive and difficult to formulate. In this paper, we follow a simpler approach by deriving the aerodynamic forces using a relatively small number of states and presenting them in a simple state-space form. The formulation utilizes Prandtl's lifting line theory and Wagner's function to determine the unsteady aerodynamic forces acting on the wing in a simulation, which then are compared to experimental data of the bat-inspired robot called the Aerobat. The simulated trailingedge vortex shedding can be evaluated from this model, which then can be analyzed for a wake-based gait design approach to improve the aerodynamic performance of the robot.

  Open Paper