This paper describes an improved control system for the Treadport immersive locomotion interface, with results that generalize to any treadmill that utilizes an actuated tether to enable self-selected walking speed. A new belt controller is implemented to regulate the user's position; when combined with the user's own volition, this controller also enables the user to naturally self-select their walking speed as they would when walking over ground. A new kinesthetic-force-feedback controller is designed for the tether that applies forces to the user's torso. This new controller is derived based on maintaining the user's sense of balance during belt acceleration, rather than by rendering an inertial force as was done in our prior work. Based on the results of a human-subjects study, the improvements in both controllers significantly contribute to an improved perception of realistic walking on the Treadport. The improved control system uses intuitive dynamic-system and anatomical parameters and requires no ad hoc gain tuning. The control system simply requires three measurements to be made for a given user: the user's mass, the user's height, and the height of the tether attachment point on the user's torso.

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Much research has been done recently on getting various UAVs to perch on various surfaces, however very little research has looked at how to detect when this perch has failed, especially when the surface the UAV is perched on is moving. This paper proposes a method to detect these types of falls using the Instantaneous Center of Rotation (ICR) of the UAV. Two methods are proposed to calculate this ICR, one based on integrating accelerometers to get velocities at various points on the UAV, the other based on using the magnitude of the acceleration at these points to estimate the distance to the ICR from that point. These methods provide a way to detect a fall from a moving perch that should work with different types of perching mechanisms and perches, while requiring minimal additional hardware on the UAV.

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This study considers the control of parent-child systems where a parent system is acted on by a set of controllable child systems (i.e. a swarm). Examples of such systems include a swarm of robots pushing an object over a surface, a swarm of aerial vehicles carrying a large load, or a set of end effectors manipulating an object. In this paper, a general approach for decoupling the swarm from the parent system through a low-dimensional abstract state space is presented. The requirements of this approach are given along with how constraints on both systems propagate through the abstract state and impact the requirements of the controllers for both systems. To demonstrate, several controllers with hard state constraints are designed to track a given desired angle trajectory of a tilting plane with a swarm of robots driving on top. Both homogeneous and heterogeneous swarms of varying sizes and properties are considered to test the robustness of this architecture. The controllers are shown to be locally asymptotically stable and are demonstrated in simulation.