Paper Reading AI Learner

Autonomous Cooperative Multi-Vehicle System for Interception of Aerial and Stationary Targets in Unknown Environments

2021-09-01 16:36:49
Lima Agnel Tony, Shuvrangshu Jana, Varun V. P., Aashay Anil Bhise, Aruul Mozhi Varman S., Vidyadhara B. V., Mohitvishnu S. Gadde, Raghu Krishnapuram, Debasish Ghose

Abstract

This paper presents the design, development, and testing of hardware-software systems by the IISc-TCS team for Challenge 1 of the Mohammed Bin Zayed International Robotics Challenge 2020. The goal of Challenge 1 was to grab a ball suspended from a moving and maneuvering UAV and pop balloons anchored to the ground, using suitable manipulators. The important tasks carried out to address this challenge include the design and development of a hardware system with efficient grabbing and popping mechanisms, considering the restrictions in volume and payload, design of accurate target interception algorithms using visual information suitable for outdoor environments, and development of a software architecture for dynamic multi-agent aerial systems performing complex dynamic missions. In this paper, a single degree of freedom manipulator attached with an end-effector is designed for grabbing and popping, and robust algorithms are developed for the interception of targets in an uncertain environment. Vision-based guidance and tracking laws are proposed based on the concept of pursuit engagement and artificial potential function. The software architecture presented in this work proposes an Operation Management System (OMS) architecture that allocates static and dynamic tasks collaboratively among multiple UAVs to perform any given mission. An important aspect of this work is that all the systems developed were designed to operate in completely autonomous mode. A detailed description of the architecture along with simulations of complete challenge in the Gazebo environment and field experiment results are also included in this work. The proposed hardware-software system is particularly useful for counter-UAV systems and can also be modified in order to cater to several other applications.

Abstract (translated)

URL

https://arxiv.org/abs/2109.00481

PDF

https://arxiv.org/pdf/2109.00481.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot