Rekha Raja

Sourabha Bharadwaj, Karunakar Gonabattula, Sudipta Saha et al.

An efficient communication mechanism forms the backbone for any multi-robot system to achieve fruitful collaboration and coordination. Limitation in the existing asynchronous transmission based strategies in fast dissemination and aggregation compels the designers to prune down such requirements as much as possible. This also restricts the possible application areas of mobile multi-robot systems. In this work, we introduce concurrent transmission based strategy as an alternative. Despite the commonly found difficulties in concurrent transmission such as microsecond level time synchronization, hardware heterogeneity, etc., we demonstrate how it can be exploited for multi-robot systems. We propose a split architecture where the two major activities - communication and computation are carried out independently and coordinate through periodic interactions. The proposed split architecture is applied on a custom build full networked control system consisting of five two-wheel differential drive mobile robots having heterogeneous architecture. We use the proposed design in a leader-follower setting for coordinated dynamic speed variation as well as the independent formation of various shapes. Experiments show a centimeter-level spatial and millisecond-level temporal accuracy while spending very low radio duty-cycling over multi-hop communication under a wide testing area.

Swagat Kumar, Anima Majumder, Samrat Dutta et al.

In this paper, we provide details of a robotic system that can automate the task of picking and stowing objects from and to a rack in an e-commerce fulfillment warehouse. The system primarily comprises of four main modules: (1) Perception module responsible for recognizing query objects and localizing them in the 3-dimensional robot workspace; (2) Planning module generates necessary paths that the robot end- effector has to take for reaching the objects in the rack or in the tote; (3) Calibration module that defines the physical workspace for the robot visible through the on-board vision system; and (4) Gripping and suction system for picking and stowing different kinds of objects. The perception module uses a faster region-based Convolutional Neural Network (R-CNN) to recognize objects. We designed a novel two finger gripper that incorporates pneumatic valve based suction effect to enhance its ability to pick different kinds of objects. The system was developed by IITK-TCS team for participation in the Amazon Picking Challenge 2016 event. The team secured a fifth place in the stowing task in the event. The purpose of this article is to share our experiences with students and practicing engineers and enable them to build similar systems. The overall efficacy of the system is demonstrated through several simulation as well as real-world experiments with actual robots.