40 citations · 150 across the 28 of their papers we have counts for
19 papers · 1 filter
Towards Understanding Underwater Weather Events in Rivers Using Autonomous Surface Vehicles
Alice K. Li, Yue Mao, Sandeep Manjanna +10
Climate change has increased the frequency and severity of extreme weather events such as hurricanes and winter storms. The complex interplay of floods with tides, runoff, and sedi…
Enabling Large-scale Heterogeneous Collaboration with Opportunistic Communications
Fernando Cladera, Zachary Ravichandran, Ian D. Miller +3
Multi-robot collaboration in large-scale environments with limited-sized teams and without external infrastructure is challenging, since the software framework required to support…
TOPPQuad: Dynamically-Feasible Time Optimal Path Parametrization for Quadrotors
Katherine Mao, Igor Spasojevic, M. Ani Hsieh +1
Planning time-optimal trajectories for quadrotors in cluttered environments is a challenging, non-convex problem. This paper addresses minimizing the traversal time of a given coll…
Enhancing Sample Efficiency and Uncertainty Compensation in Learning-based Model Predictive Control for Aerial Robots
Kong Yao Chee, Thales C. Silva, M. Ani Hsieh +1
The recent increase in data availability and reliability has led to a surge in the development of learning-based model predictive control (MPC) frameworks for robot systems. Despit…
EV-Catcher: High-Speed Object Catching Using Low-latency Event-based Neural Networks
Ziyun Wang, Fernando Cladera Ojeda, Anthony Bisulco +6
Event-based sensors have recently drawn increasing interest in robotic perception due to their lower latency, higher dynamic range, and lower bandwidth requirements compared to sta…
Leveraging Predictive Models for Adaptive Sampling of Spatiotemporal Fluid Processes
Sandeep Manjanna, Tom Z. Jiahao, M. Ani Hsieh
Persistent monitoring of a spatiotemporal fluid process requires data sampling and predictive modeling of the process being monitored. In this paper we present PASST algorithm: Pre…
Stochastic Nonlinear Ensemble Modeling and Control for Robot Team Environmental Monitoring
Victoria Edwards, Thales C. Silva, M. Ani Hsieh
We seek methods to model, control, and analyze robot teams performing environmental monitoring tasks. During environmental monitoring, the goal is to have teams of robots collect v…
Proportional Control for Stochastic Regulation on Allocation of Multi-Robots
Thales C. Silva, Victoria Edwards, M. Ani Hsieh
Any strategy used to distribute a robot ensemble over a set of sequential tasks is subject to inaccuracy due to robot-level uncertainties and environmental influences on the robots…
Receding Horizon Control on the Broadcast of Information in Stochastic Networks
Thales C. Silva, Li Shen, Xi Yu +1
This paper focuses on the broadcast of information on robot networks with stochastic network interconnection topologies. Problematic communication networks are almost unavoidable i…
LEARNEST: LEARNing Enhanced Model-based State ESTimation for Robots using Knowledge-based Neural Ordinary Differential Equations
Kong Yao Chee, M. Ani Hsieh
State estimation is an important aspect in many robotics applications. In this work, we consider the task of obtaining accurate state estimates for robotic systems by enhancing the…
Adaptive Sampling of Latent Phenomena using Heterogeneous Robot Teams (ASLaP-HR)
Matthew Malencia, Sandeep Manjanna, M. Ani Hsieh +2
In this paper, we present an online adaptive planning strategy for a team of robots with heterogeneous sensors to sample from a latent spatial field using a learned model for decis…
Online Dynamics Learning for Predictive Control with an Application to Aerial Robots
Tom Z. Jiahao, Kong Yao Chee, M. Ani Hsieh
In this work, we consider the task of improving the accuracy of dynamic models for model predictive control (MPC) in an online setting. Although prediction models can be learned an…
MARLAS: Multi Agent Reinforcement Learning for cooperated Adaptive Sampling
Lishuo Pan, Sandeep Manjanna, M. Ani Hsieh
The multi-robot adaptive sampling problem aims at finding trajectories for a team of robots to efficiently sample the phenomenon of interest within a given endurance budget of the…
Flow-Based Control of Marine Robots in Gyre-Like Environments
Gedaliah Knizhnik, Peihan Li, Xi Yu +1
We present a flow-based control strategy that enables resource-constrained marine robots to patrol gyre-like flow environments on an orbital trajectory with a periodicity in a give…
Learning and Leveraging Features in Flow-Like Environments to Improve Situational Awareness
Tahiya Salam, Victoria Edwards, M. Ani Hsieh
This paper studies how global dynamics and knowledge of high-level features can inform decision-making for robots in flow-like environments. Specifically, we investigate how cohere…
Learning to Swarm with Knowledge-Based Neural Ordinary Differential Equations
Tom Z. Jiahao, Lishuo Pan, M. Ani Hsieh
Understanding decentralized dynamics from collective behaviors in swarms is crucial for informing robot controller designs in artificial swarms and multiagent robotic systems. Howe…
KNODE-MPC: A Knowledge-based Data-driven Predictive Control Framework for Aerial Robots
Kong Yao Chee, Tom Z. Jiahao, M. Ani Hsieh
In this work, we consider the problem of deriving and incorporating accurate dynamic models for model predictive control (MPC) with an application to quadrotor control. MPC relies…
Scalable Multirobot Planning for Informed Spatial Sampling
Sandeep Manjanna, M. Ani Hsieh, Gregory Dudek
This paper presents a distributed scalable multi-robot planning algorithm for informed sampling of quasistatic spatial fields. We address the problem of efficient data collection u…
Heterogeneous robot teams for modeling and prediction of multiscale environmental processes
Tahiya Salam, M. Ani Hsieh
This paper presents a framework to enable a team of heterogeneous mobile robots to model and sense a multiscale system. We propose a coupled strategy, where robots of one type coll…