Swarms of robots have many uses, from exploring an area in a parallel manner to manipulating objects, both on the ground and in the air. In this paper, we consider a class of swarms in which robots within the swarm work together to manipulate a parent system with its own dynamics. Using the process of abstraction, we find a set of variables that represent key elements of the swarm such as its mean position, the variance of the swarm's distribution, etc. Using these variables, we can generate a simplified model of how the swarm interacts with the parent system and design a controller to control the parent system through this abstract state. We show that we are able to use a swarm to regulate the parent system, track a step input, and reject step disturbances in an example problem involving a swarm of robots balancing a plane that is allowed to rotate about an axis through its center.

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.

An accurate dynamics model of a robot is an important ingredient of many algorithms used to solve robotics problems, including motion planning, control, localization, and mapping. Models derived from first principles often contain parameters (e.g. mass, moment of inertia, arm lengths, etc.) for which values are unknown. Those which cannot be easily measured must be estimated from the observed behavior of the robot. A good approach to address this problem is to plan control policies for the robot that elicit maximal amounts of information about the parameters of the system, while still achieving other objectives specified for the robot. In case of parameters subject to drift, this must be done continuously over the lifetime of the robot if costly re-calibrations are to be avoided. In this paper, we introduce a new method that formulates the parameter estimation problem as a continuous partially-observable Markov decision process (POMDP), which plans control policies that optimally trade-off the effort spent on learning parameters and effort spent on achieving regular robot objectives (exploration vs. exploitation), and allow for online, continual parameter estimation. While POMDPs have, until recently, been mostly of theoretical interest due to their inherent complexity, we build on recent advances that allow continuous, Gaussian POMDPs to be approximately-optimally solved in near-real-time rates. We show that the computed control policies lead to improved convergence of the belief of the parameters compared to system identification approaches based on applying random controls.