Morphing Machines with Locomotion Plasticity
Quadrupedal Locomotion Control On Inclined Surfaces Using Collocation Method
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 reduced-order dynamics of Husky, called HROM, to quickly and efficiently find optimal control actions that permit high-slope walking without violating friction cone conditions.
Narrow-Path, Dynamic Walking Using Integrated Posture Manipulation and Thrust Vectoring
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.
Thruster-Assisted Incline Walking
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.
Generative design of nu’s husky carbon, a morpho-functional, legged robot
This work presents the design of a morpho-functional robot, Husky Carbon, which integrates aerial and quadrupedal legged mobility into a single platform. The design process faced significant constraints, such as tight power budgets and payload limits, particularly affecting aerial operations. To address these challenges, the Mobility Value of Added Mass (MVAM) problem was introduced, focusing on optimizing mass allocation to minimize energetic impact. A generative design approach using Grasshopper’s evolutionary solver was employed to explore a parametric design space, minimizing Total Cost of Transport (TCOT) and payload. Results showed that a front-heavy quadrupedal robot achieves lower TCOT while accommodating more added mass, leading to the construction and testing of Husky.
Dynamic modeling of wing-assisted inclined running with a morphing multi-modal robot
Inspired by nature’s resilient and fault-tolerant locomotion strategies, we designed a morphing robot with multi-functional thruster-wheel appendages, enabling it to switch between various modes of locomotion, including a rover, quad-rotor, and mobile inverted pendulum (MIP). Nature offers examples like Chukar and Hoatzin birds, which repurpose their wings for quadrupedal walking and wing-assisted incline running (WAIR) to climb steep surfaces. Drawing from these natural adaptations, we developed a dynamic model and formulated a nonlinear model predictive controller to perform WAIR, showcasing our robot’s unique capabilities. The model and controller were implemented in numerical simulations and experiments, demonstrating the feasibility and versatility of our transforming multi-modal robot.
Demonstrating Autonomous 3D Path Planning on a Novel Scalable UGV-UAV Morphing Robot
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.
Multi-Modal Mobility Morphobot (M4) with appendage repurposing for locomotion plasticity enhancement
Robot designs often take inspiration from nature, where adaptable locomotion strategies help navigate complex terrains. Birds like Chukars and Hoatzins repurpose their wings for walking and wing-assisted incline running, demonstrating the ability to generate multiple modes of locomotion. Inspired by this, we developed the Multi-Modal Mobility Morphobot (M4), a robot designed to navigate unstructured environments on land and in the air. M4 uses its components as wheels, thrusters, and legs, allowing it to fly, roll, crawl, crouch, balance, tumble, scout, and loco-manipulate. It can traverse steep slopes up to 45 degrees and handle rough terrains. M4 is equipped with onboard computers and sensors, enabling autonomous adaptation to various environments. This work presents M4’s design and capabilities.
Minimum time trajectory generation for bounding flight: Combining posture control and thrust vectoring
Biological fliers, like birds, excel in maneuvers such as bounding flight, where they fold their wings to intermittently soar or manipulate body dynamics to navigate challenging trajectories. This integration of thrust vectoring and body control allows them to optimize for various objectives, such as minimizing aerodynamic drag or enhancing maneuverability. However, the combination of posture control and thrust vectoring is still underexplored in aerial robotics. In this paper, we present a dynamical model of an aerial robot with articulated thrusters to generate minimum time trajectories under spatial constraints. We formulate an optimal control problem and solve it numerically using trapezoidal collocation. Our results show that combining posture control and thrust vectoring enables navigation through narrow and varying geometries while reducing maneuver time through careful manipulation of shape inputs.
Capture Point Control in Thruster-Assisted Bipedal Locomotion
Despite advancements in robust control design, bipedal robots remain vulnerable to falls and struggle on rough terrains. To enhance stability, our bipedal robot, Harpy, utilizes thrusters for additional posture manipulation and expanded locomotion modes. This paper presents a capture point control-based controller for the thruster-assisted Harpy, exploring new control possibilities. While capture point control with centroidal models is well-studied, incorporating external forces in linear inverted pendulum models has not been explored. These forces can lead to novel interpretations of locomotion, akin to virtual buoyancy in aquatic-legged robots. We outline Harpy’s dynamical model, capture point method for upper body stabilization, and simulation results demonstrating the controller’s feasibility.
Control of Thruster-Assisted, Bipedal Legged Locomotion of the HarpyRobot
Fast constraint satisfaction, frontal dynamics stabilization, and avoiding fallovers in dynamic bipedal walkers are challenging due to underactuation, vulnerability to perturbations, and high computational complexity. This work explores the use of thrusters to address these challenges in a bipedal robot called Harpy. We propose an optimization-free approach to satisfy gait feasibility conditions, manipulating reference trajectories to meet constraints from ground contact and prescribed states and inputs. Although unintended changes to optimized trajectories can affect gait stability, we demonstrate that Harpy’s thrusters can ensure stability and hybrid invariance. Additionally, we show that the thrusters enhance robustness by preventing fallovers and enabling jumps over large obstacles.
A HZD-based Framework for the Real-time, Optimization-free Enforcement of Gait Feasibility Constraints
Real-time constraint satisfaction for robots can be quite challenging due to the high computational complexity that arises when accounting for the system dynamics and environmental interactions, often requiring simplification in modelling that might not necessarily account for all performance criteria. We instead propose an optimization-free approach where reference trajectories are manipulated to satisfy constraints brought on by ground contact as well as those prescribed for states and inputs. Unintended changes to trajectories especially ones optimized to produce periodic gaits can adversely affect gait stability, however we will show our approach can still guarantee stability of a gait by employing the use of coaxial thrusters that are unique to our robot.
Unilateral Ground Contact Force Regulations in Thruster-Assisted Legged Locomotion
In this paper, we study the regulation of the Ground Contact Forces (GRF) in thruster-assisted legged locomotion. We will employ Reference Governors (RGs) for enforcing GRF constraints in Harpy model which is a bipedal robot that is being developed at Northeastern University. Optimization-based methods and whole body control are widely used for enforcing the no-slip constraints in legged locomotion which can be very computationally expensive. In contrast, RGs can enforce these constraints by manipulating joint reference trajectories using Lyapunov stability arguments which can be computed much faster. The addition of the thrusters in our model allows to manipulate the gait parameters and the GRF without sacrificing the locomotion stability.
Rough-Terrain Locomotion and Unilateral Contact Force Regulations With a Multi-Modal Legged Robot
Despite many accomplishments by legged robot designers, state-of-the-art bipedal robots are prone to falling over, cannot negotiate extremely rough terrains and cannot directly regulate unilateral contact forces. Our objective is to integrate merits of legged and aerial robots in a single platform. We will show that the thrusters in a bipedal legged robot called Harpy can be leveraged to stabilize the robot’s frontal dynamics and permit jumping over large obstacles which is an unusual capability not reported before. In addition, we will capitalize on the thrusters action in Harpy and will show that one can avoid using costly optimization-based schemes by directly regulating contact forces using an Reference Governor (RGs). We will resolve gait parameters and re-plan them during gait cycles by only assuming well-tuned supervisory controllers. Then, we will focus on RG-based fine-tuning of the joints desired trajectories to satisfy unilateral contact force constraints.