Bio-inspired robots; medical robots; collaborative, cooperative and networked robots; robotics for extreme environments; unmanned air and water vehicles; AI and computer vision for robots

ISR faculty have advanced flocking and swarming theory, control and design for robotic groups and swarms. ISR developed motion description languages; designed and fabricated a modular dexterous hand; made advances in underwater robots, flapping-wing micro air vehicles and micro robots; and introduced innovations in computer vision and hyperdimensional computing theory for robots. A multi-part, in-mold assembly process that reduces process cost and enables robotic design possibilities created articulated structures without requiring post-molding assembly steps. ISR started the Maryland Robotics Center as one of its initiatives in 2010.

Recent publications by ISR robotics faculty


New Formulation of Mixed-Integer Conic Programming for Globally Optimal Grasp Planning

Min Liu, Zherong Pan, Kai Xu, and Dinesh Manocha

Classical robotics research presents the first grasp planning algorithm to compute globally optimal gripper pose that maximizes a grasp metric.

DCAD: Decentralized Collision Avoidance with Dynamics Constraints for Agile Quadrotor Swarms

Senthil Hariharan Arul, Dinesh Manocha

A new algorithm for decentralized collision avoidance for quadrotor swarm navigation in dense environments with static and dynamic obstacles.

3-D Deformable Object Manipulation using Deep Neural Networks

Zhe Hu, Tao Han, Peigen Sun, Jia Pan, Dinesh Manocha

Deep neural network-based controller to servo-control position and shape of deformable objects with unknown deformation properties.

IEEE Robotics and Automation Letters

DeepMNavigate: Deep Reinforced Multi-Robot Navigation Unifying Local & Global Collision Avoidance

Qingyang Tan, Tingxiang Fan, Jia Pan, Dinesh Manocha

A novel algorithm (DeepMNavigate) for global multi-agent navigation in dense scenarios using deep reinforcement learning. | Watch a video about DeepMNavigate |

GraphRQI: Classifying Driver Behaviors Using Graph Spectrums

Rohan Chandra, Uttaran Bhattacharya, Trisha Mittal, Xiaoyu Li, Aniket Bera, Dinesh Manocha

The GraphRQI algorithm identifies driver behaviors from road agent trajectories. It is 25 percent more accurate over prior behavior classification algorithms for autonomous vehicles.  | Watch a video about GraphRQI |

Realtime Simulation of Thin-Shell Deformable Materials using CNN-Based Mesh Embedding

Qingyang Tan, Zherong Pan, Lin Gao, and Dinesh Manocha

A new method bridges the gap between mesh embedding and physical simulation for efficient dynamic models of clothes. The key technique is a graph-based convolutional neural network (CNN) defined on meshes with arbitrary topologies, and a new mesh embedding approach based on physics-inspired loss term. After training, the learned simulator runs10–100 times faster and the accuracy is high enough for robot manipulation tasks. | Watch a video about this method |



Non-Gaussian Estimation of a Potential Flow by an Actuated Lagrangian Sensor Steered to Separating Boundaries by Augmented Observability

Francis D. Lagor, Kayo Ide, Derek A. Paley

An architecture for estimation of a flow field using a hypothetical oceanographic vehicle that is guided along paths of high flow-field observability, a concept quantifying the informativeness of a path.

IEEE Journal of Oceanic Engineering

Mobile Sensor Networks Control: Adaptive Sampling of Spatiotemporal Processes

Derek Paley, Artur Wolek

A review of control of mobile sensor networks for environmental monitoring and other applications. Includes sensor platform dynamics and cooperative control and estimation; process modeling and estimation for both continuous and discrete models; sampling metrics and optimization, including coverage-, topology-, information-, and estimation-based metrics; and methods for task design and allocation.

Annual Review of Control, Robotics, and Autonomous Systems

ISR and Maryland Robotics Center robotics news