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1. 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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2. Observer-Aware Legibility for Social Navigation

   https://doi.org/10.1109/RO-MAN53752.2022.9900676  

   Taylor, Ada V.; Mamantov, Ellie; Admoni, Henny (August 2022, EEE International Conference on Robot and Human Interactive Communication (RO-MAN))

   We designed an observer-aware method for creating navigation paths that simultaneously indicate a robot’s goal while attempting to remain in view for a particular observer. Prior art in legible motion does not account for the limited field of view of observers, which can lead to wasted communication efforts that are unobserved by the intended audience. Our observer-aware legibility algorithm directly models the locations and perspectives of observers, and places legible movements where they can be easily seen. To explore the effectiveness of this technique, we performed a 300-person online user study. Users viewed first-person videos of restaurant scenes with robot waiters moving along paths optimized for different observer perspectives, along with a baseline path that did not take into account any observer’s field of view. Participants were asked to report their estimate of how likely it was the robot was heading to their table versus the other goal table as it moved along each path. We found that for observers with incomplete views of the restaurant, observer-aware legibility is effective at increasing the period of time for which observers correctly infer the goal of the robot. Non-targeted observers have lower performance on paths created for other observers than themselves, which is the natural drawback of personalizing legible motion to a particular observer. We also find that an observer’s relationship to the environment (e.g. what is in their field of view) has more influence on their inferences than the observer’s relative position to the targeted observer, and discuss how this implies knowledge of the environment is required in order to effectively plan for multiple observers at once. 

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3. Wrench Analysis of Kinematically Redundant Planar CDPRs

   https://doi.org/10.1007/978-3-030-75789-2_8  

   Raman A.; Schmid M.; Krovi, V. (June 2021, Cable-Driven Parallel Robots. CableCon 2021. Mechanisms and Machine Science)

   Gouttefarde M.; Bruckmann T.; Pott A. (Ed.)

   A fully-constrained 𝑛−𝐷𝑂𝐹 cable-driven parallel robot (CDPR) has wrench closure if there are 𝑛+1 cables exerting positive tensions spanning the wrench space. However, the quality of wrench closure is often dependent on the geometric configuration of the supporting in-parallel chains of the CDPR. The reconfigurability endowed by adding in-chain kinematic and/or actuation redundancy to a conventional cable robot could greatly improve quality of the workspace. However, the status of various joints (active, passive or locked) affect the complexity of the systematic formulation and ultimate wrench-based analysis. Past efforts have tended to equilibrate the forces in these systems in such a way as to avoid kinematic redundancies. To this end, we formulate the kinematics of the redundant reconfigurable CDPR using matrix Lie group formulation (to allow ease of formulation and subsequent generalizability). Reciprocity (and selective reciprocity) permits the development of wrench analyses including the partitioning of actuation vs structural equilibration components. The total wrench set is greatly expanded both by the addition of kinematic redundancy and selective actuation/locking of the joints. The approach adopted facilitates the holistic determination of the true wrench polytope which accounts for the wrench contributions from all actuation sources. All these aspects are examined with variants of a 4-PRPR planar cable driven parallel manipulator (with varied active/passive/locked joints). 

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4. Observing a trained demonstrator influences associative appetitive learning in rats

   https://doi.org/10.1098/rsos.221224  

   Agee, Laura A.; Ortega, Miriam E.; Lee, Hongjoo J.; Monfils, Marie-H. (April 2023, Royal Society Open Science)

   The ability to acquire information about the environment through social observation or instruction is an essential form of learning in humans and other animals. Here, we assessed the ability of rats to acquire an association between a light stimulus and the presentation of a reward that is either hidden (sucrose solution) or visible (food pellet) via observation of a trained demonstrator. Subsequent training of observers on the light-reward association indicated that while observation alone was not sufficient for observers to acquire the association, contact with the reward location was higher in observers that were paired with a demonstrator. However, this was only true when the light cue predicted a sucrose reward. Additionally, we found that in the visible reward condition, levels of demonstrator orienting and food cup contact during the observation period tended to be positively correlated with the corresponding behaviour of their observer. This relationship was only seen during later sessions of observer training. Together, these results suggest that while our models were not sufficient to induce associative learning through observation alone, demonstrator behaviour during observation did influence how their paired observer's behavioural response to the cue evolved over the course of direct individual training. 

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5. Examining Distance in UAV Gesture Perception

   https://doi.org/10.1109/IROS47612.2022.9981971  

   Jelonek, Karissa; Fletcher, Paul; Duncan, Brittany; Detweiler, Carrick (October 2022, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   Unmanned aerial vehicles (UAVs) are becoming more common, presenting the need for effective human-robot communication strategies that address the unique nature of unmanned aerial flight. Visual communication via drone flight paths, also called gestures, may prove to be an ideal method. However, the effectiveness of visual communication techniques is dependent on several factors including an observer's position relative to a UAV. Previous work has studied the maximum line-of-sight at which observers can identify a small UAV [1]. However, this work did not consider how changes in distance may affect an observer's ability to perceive the shape of a UAV's motion. In this study, we conduct a series of online surveys to evaluate how changes in line-of-sight distance and gesture size affect observers' ability to identify and distinguish between UAV gestures. We first examine observers' ability to accurately identify gestures when adjusting a gesture's size relative to the size of a UAV. We then measure how observers' ability to identify gestures changes with respect to varying line-of-sight distances. Lastly, we consider how altering the size of a UAV gesture may improve an observer's ability to identify drone gestures from varying distances. Our results show that increasing the gesture size across varying UAV to gesture ratios did not have a significant effect on participant response accuracy. We found that between 17 m and 75 m from the observer, their ability to accurately identify a drone gesture was inversely proportional to the distance between the observer and the drone. Finally, we found that maintaining a gesture's apparent size improves participant response accuracy over changing line-of-sight distances. 

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