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This paper introduces and solves a visibility-based escort planning problem. This novel problem, which is closely related to the well-researched family of visibility-based pursuit-evasion problems in robotics, entails an escort agent tasked with escorting a vulnerable agent, called the VIP, in a 2-dimensional environment. The escort protects the VIP from adversaries that pose line-of-sight threats. We describe a correct and complete planning algorithm whose inputs are a simply-connected polygonal map of the environment, starting locations for the escort and the VIP, along with a goal location to which the VIP agent should be safely moved. The algorithm computes trajectories for the escort and VIP which allow the VIP to reach its goal without coming into the line-of-sight of the adversary at any time. During the execution of these trajectories, the adversary is allowed to move along any continuous path that does not enter into the line-of-sight of the escort. The algorithm proceeds by dividing the environment into a collection of conservative regions and planning the escort's movements as a sequence of these regions via breadth-first search over an information graph. The trajectory of the VIP can then be constructed by tracing the 'safe zones' swept out by the escort's trajectory. We describe an implementation of this algorithm and present computed examples of escort agent strategies in diverse environments. 

Reconfigurable modular robots can dynamically assemble/disassemble to accomplish the desired task better. Magnetic modular cubes are scalable modular subunits with embedded permanent magnets in a 3D-printed cubic body and can be wirelessly controlled by an external, uniform, timevarying magnetic field. This paper considers the problem of self-assembling these modules into desired 2D polyomino shapes using such magnetic fields. Although the applied magnetic field is the same for each magnetic modular cube, we use collisions with workspace boundaries to rearrange the cubes. We present a closed-loop control method for self-assembling the magnetic modular cubes into polyomino shapes, using computer vision-based feedback with re-planning. Experimental results demonstrate that the proposed closed-loop control improves the success rate of forming 2D user-specified polyominoes compared to an open-loop baseline. We also demonstrate the validity of the approach over changes in length scales, testing with both 10mm edge length cubes and 2.8mm edge length cubes. 

This paper discusses a novel approach for the exploration of an underwater structure. A team of robots splits into two roles: certain robots approach the structure collecting detailed information (proximal observers) while the rest (distal observers) keep a distance providing an overview of the mission and assist in the localization of the proximal observers via a Cooperative Localization framework. Proximal observers utilize a novel robust switching model-based/visual-inertial odometry to overcome vision-based localization failures. Exploration strategies for the proximal and the distal observer are discussed.