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1. Deep Learning–based Reassembling of an Aerial & Legged Marsupial Robotic System–of–Systems

   https://doi.org/10.1109/ICUAS57906.2023.10155866  

   Arora, Prateek; Karakurt, Tolga; Avlonitis, Eleni; Carlson, Stephen J.; Moore, Brandon; Feil-Seifer, David; Papachristos, Christos (June 2023, International Conference on Unmanned Aircraft Systems)

   In this work we address the System-of-Systems reassembling operation of a marsupial team comprising a hybrid Unmanned Aerial Vehicle and a Legged Locomotion robot, relying solely on vision-based systems and assisted by Deep Learning. The target application domain is that of large-scale field surveying operations under the presence of wireless communication disruptions. While most real-world field deployments of multi-robot systems assume some degree of wireless communication to coordinate key tasks such as multi-agent rendezvous, a desirable feature against unrecoverable communication failures or radio degradation due to jamming cyber-attacks is the ability for autonomous systems to robustly execute their mission with onboard perception. This is especially true for marsupial air / ground teams, wherein landing onboard the ground robot is required. We propose a pipeline that relies on Deep Neural Network-based Vehicle-to-Vehicle detection based on aerial views acquired by flying at typical altitudes for Micro Aerial Vehicle-based real-world surveying operations, such as near the border of the 400ft Above Ground Level window. We present the minimal computing and sensing suite that supports its execution onboard a fully autonomous micro-Tiltrotor aircraft which detects, approaches, and lands onboard a Boston Dynamics Spot legged robot. We present extensive experimental studies that validate this marsupial aerial / ground robot’s capacity to safely reassemble while in the airborne scouting phase without the need for wireless communication. 

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2. Towards Multi-Day Field Deployment Autonomy: A Long-Term Self-Sustainable Micro Aerial Vehicle Robot

   https://doi.org/10.1109/ICRA48891.2023.10161014  

   Carlson, Stephen J.; Arora, Prateek; Karakurt, Tolga; Moore, Brandon; Papachristos, Christos (May 2023, 2023 IEEE International Conference on Robotics and Automation (ICRA))

   This works deals with the problem of long-term autonomy in the context of multi-day field deployments of Micro Aerial Vehicle (MAV) systems. To truly depart from the necessity for human intervention for the crucial task of providing battery recharging, and to liberate from the need to operate in a confined range around specially installed infrastructure such as recharging pods, the MAV robot is required to harvest power on its own, but equally importantly also sustain prolonged periods of ambient power scarcity. This implies being able to sustain the battery charge overnight when using solar recharging, or even during multiple days of illumination inadequacy (e.g., due to degraded atmospheric lucidity and heavy overcast). We address this by presenting a Self-Sustainable Autonomous System architecture for MAVs centered around a specially tailored Power Management Stack, which is capable of achieving deep system hibernation, a feature that facilitates the aforementioned functionalities. We present a) continuous, b) multi-day successive, and c) externally-powered recharging that uses a legged robot-mounted Mobile Recharging Station. We conclude by demonstrating a challenging zero-intervention multi-day field deployment mission in the N.Nevada region. 

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3. Launching a Micro–Scout UAV from a Mobile Robotic Manipulator Arm

   Arora, P.; Papachristos, C. (April 2021, 2021 IEEE Conference on Aerospace)

   null (Ed.)

   This paper addresses the problem of autonomously deploying an unmanned aerial vehicle in non-trivial settings, by leveraging a manipulator arm mounted on a ground robot, acting as a versatile mobile launch platform. As real-world deployment scenarios for micro aerial vehicles such as searchand- rescue operations often entail exploration and navigation of challenging environments including uneven terrain, cluttered spaces, or even constrained openings and passageways, an often arising problem is that of ensuring a safe take-off location, or safely fitting through narrow openings while in flight. By facilitating launching from the manipulator end-effector, a 6- DoF controllable take-off pose within the arm workspace can be achieved, which allows to properly position and orient the aerial vehicle to initialize the autonomous flight portion of a mission. To accomplish this, we propose a sampling-based planner that respects a) the kinematic constraints of the ground robot / manipulator / aerial robot combination, b) the geometry of the environment as autonomously mapped by the ground robots perception systems, and c) accounts for the aerial robot expected dynamic motion during takeoff. The goal of the proposed planner is to ensure autonomous collision-free initialization of an aerial robotic exploration mission, even within a cluttered constrained environment. At the same time, the ground robot with the mounted manipulator can be used to appropriately position the take-off workspace into areas of interest, effectively acting as a carrier launch platform. We experimentally demonstrate this novel robotic capability through a sequence of experiments that encompass a micro aerial vehicle platform carried and launched from a 6-DoF manipulator arm mounted on a four-wheel robot base. 

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4. Buoyancy Enabled Non-Inertial Dynamic Walking

   https://doi.org/10.1109/IROS55552.2023.10341582  

   Yim, Mark; Gosrich, Walker; Miskin, Marc (October 2023, IEEE)

   We propose a mechanism for low Reynolds num- ber walking (e.g., legged microscale robots). Whereas loco- motion for legged robots has traditionally been classified as dynamic (where inertia plays a role) or static (where the system is always statically stable), we introduce a new locomotion modality we call buoyancy enabled non-inertial dynamic walking in which inertia plays no role, yet the robot is not statically stable. Instead, falling and viscous drag play critical roles. This model assumes squeeze flow forces from fluid interactions combined with a well timed gait as the mechanism by which forward motion can be achieved from a reciprocating legged robot. Using two physical demonstrations of robots with Reynold’s number ranging from 0.0001 to 0.02 (a microscale robot in water and a centimeter scale robot in glycerol) we find the model qualitatively describes the motion. This model can help understand microscale locomotion and design new microscale walking robots including controlling forward and backwards motion and potentially steering these robots. 

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5. Machine Learning Vision and Nonlinear Control Approach for Autonomous Ship Landing of Vertical Flight Aircraft

   Lee, B. (May 2021, 77th Annual National Forum of the Vertical Flight Society)

   The paper discusses a machine learning vision and nonlinear control approach for autonomous ship landing of vertical flight aircraft without utilizing GPS signal. The central idea involves automating the Navy helicopter ship landing procedure where the pilot utilizes the ship as the visual reference for long-range tracking, but refers to a standardized visual cue installed on most Navy ships called the ”horizon bar” for the final approach and landing phases. This idea is implemented using a uniquely designed nonlinear controller integrated with machine vision. The vision system utilizes machine learning based object detection for long-range ship tracking, and classical computer vision for object detection and the estimation of aircraft relative position and orientation during the final approach and landing phases. The nonlinear controller operates based on the information estimated by the vision system and has demonstrated robust tracking performance even in the presence of uncertainties. The developed autonomous ship landing system is implemented on a quad-rotor vertical take-off and landing (VTOL) capable unmanned aerial vehicle (UAV) equipped with an onboard camera and was demonstrated on a moving deck, which imitates realistic ship deck motions using a Stewart platform and a visual cue equivalent to the horizon bar. Extensive simulations and flight tests are conducted to demonstrate vertical landing safety, tracking capability, and landing accuracy while the deck is in motion. 

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