Underwater Gripper using Distributed Network and Adaptive Control

Underwater identification and grasping of objects is a major challenge faced by the marine engineers even today. Nowadays, almost all underwater operations are either autonomous or tele-operated. In fact remotely operated vehicles (ROVs) are used to deal with inspection tasks and industrial maintenance whenever there is need for intervention. However, the field of autonomous underwater vehicle (AUV) is a blooming filed with research involving proper moving base control and forces interacting which leads to complicated configuration. Hence the presented work is focused implementation of end-effector with appropriate control and signal processing resulting in autonomous manipulation of movement under water.

Minimal multi-camera system for perception of deformable shapes

Within the context of autonomous manipulation of deformable objects, we propose a minimal multi-camera perception system that allows to cover a deforming 2D shape over time according to a specific visibility objective. Our method iteratively solves an optimization problem that includes collision avoidance and robust visibility constraints.

A Latent State-Based Multimodal Execution Monitor with Anomaly Detection and Classification for Robot Introspection

Robot introspection is expected to greatly aid longer-term autonomy of autonomous manipulation systems. By equipping robots with abilities that allow them to assess the quality of their sensory data, robots can detect and classify anomalies and recover appropriately from common anomalies. This work builds on our previous Sense-Plan-Act-Introspect-Recover (SPAIR) system. We introduce an improved anomaly detector that exploits latent states to monitor anomaly occurrence when robots collaborate with humans in shared workspaces, but also present a multiclass classifier that is activated with anomaly detection. Both implementations are derived from Bayesian non-parametric methods with strong modeling capabilities for learning and inference of multivariate time series with complex and uncertain behavior patterns. In particular, we explore the use of a hierarchical Dirichlet stochastic process prior to learning a Hidden Markov Model (HMM) with a switching vector auto-regressive observation model (sHDP-VAR-HMM). The detector uses a dynamic log-likelihood threshold that varies by latent state for anomaly detection and the anomaly classifier is implemented by calculating the cumulative log-likelihood of testing observation based on trained models. The purpose of our work is to equip the robot with anomaly detection and anomaly classification for the full set of skills associated with a given manipulation task. We consider a human–robot cooperation task to verify our work and measure the robustness and accuracy of each skill. Our improved detector succeeded in detecting 136 common anomalies and 368 nominal executions with a total accuracy of 91.0%. An overall anomaly classification accuracy of 97.1% is derived by performing the anomaly classification on an anomaly dataset that consists of 7 kinds of detected anomalies from a total of 136 anomalies samples.