109 citations · 261 across the 15 of their papers we have counts for
6 papers · 1 filter
VIO-UWB-Based Collaborative Localization and Dense Scene Reconstruction within Heterogeneous Multi-Robot Systems
Jorge Peña Queralta, Li Qingqing, Fabrizio Schiano +1
Effective collaboration in multi-robot systems requires accurate and robust estimation of relative localization: from cooperative manipulation to collaborative sensing, and includi…
Secure Encoded Instruction Graphs for End-to-End Data Validation in Autonomous Robots
Jorge Peña Queralta, Li Qingqing, Eduardo Castelló Ferrer +1
As autonomous robots are becoming more widespread, more attention is being paid to the security of robotic operation. Autonomous robots can be seen as cyber-physical systems: they…
Towards Closing the Sim-to-Real Gap in Collaborative Multi-Robot Deep Reinforcement Learning
Wenshuai Zhao, Jorge Peña Queralta, Li Qingqing +1
Current research directions in deep reinforcement learning include bridging the simulation-reality gap, improving sample efficiency of experiences in distributed multi-agent reinfo…
Ubiquitous Distributed Deep Reinforcement Learning at the Edge: Analyzing Byzantine Agents in Discrete Action Spaces
Wenshuai Zhao, Jorge Peña Queralta, Li Qingqing +1
The integration of edge computing in next-generation mobile networks is bringing low-latency and high-bandwidth ubiquitous connectivity to a myriad of cyber-physical systems. This…
Enhancing Autonomy with Blockchain and Multi-Access Edge Computing in Distributed Robotic Systems
Jorge Peña Queralta, Li Qingqing, Zhuo Zou +1
This conceptual paper discusses how different aspects involving the autonomous operation of robots and vehicles will change when they have access to next-generation mobile networks…
Localization in Unstructured Environments: Towards Autonomous Robots in Forests with Delaunay Triangulation
Qingqing Li, Paavo Nevalainen, Jorge Peña Queralta +2
Autonomous harvesting and transportation is a long-term goal of the forest industry. One of the main challenges is the accurate localization of both vehicles and trees in a forest.…