Ensuring safe, real-time motion planning in arbitrary environments requires a robotic manipulator to avoid collisions, obey joint limits, and account for uncertainties in the mass and inertia of objects and the robot itself. This paper proposes Autonomous Robust Manipulation via Optimization with Uncertainty-aware Reachability (ARMOUR), a provably-safe, receding-horizon trajectory planner and tracking controller framework for robotic manipulators to address these challenges. ARMOUR first constructs a robust controller that tracks desired trajectories with bounded error despite uncertain dynamics. ARMOUR then uses a novel recursive Newton-Euler method to compute all inputs required to track any trajectory within a continuum of desired trajectories. Finally, ARMOUR over-approximates the swept volume of the manipulator; this enables one to formulate an optimization problem that can be solved in real-time to synthesize provably-safe motions. This paper compares ARMOUR to state of the art methods on a set of challenging manipulation examples in simulation and demonstrates its ability to ensure safety on real hardware in the presence of model uncertainty without sacrificing performance. Project page:https://roahmlab.github.io/armour/.

The optic disc is one of the prominent features of a retinal fundus image, and its segmentation is a critical component in automated retinal screening systems for ophthalmic anomalies, such as diabetic retinopathy and glaucoma. In this paper, we propose a novel method for optic disc segmentation using affine snakes, where the snake evolves using an affine transformation and requires a priori knowledge of the desired object shape. We determine the affine transformation parameters by first computing a force field on the image and then deforming the snake till the net force on the snake is zero. The affine snakes technique excels in its speed of convergence. This is attributed to the fact that only six parameters require optimization, the six parameters being the horizontal and vertical scaling, shearing and translation components of an affine transformation. Localization of the optic disc is done using normalized cross-correlation and segmentation is done using the affine snakes technique. This technique is tested on publicly available fundus image datasets, such as IDRiD, Drishti-GS, RIM-ONE, DRIONS-DB, and Messidor, with Dice In-dices of 0.943, 0.958, 0.933, 0.913, and 0.912, respectively.