Ragesh K. Ramachandran — accepted AI-conference papers

2022

Adaptive and Risk-Aware Target Tracking for Robot Teams With Heterogeneous Sensors

Siddharth Mayya, Ragesh K. Ramachandran, Lifeng Zhou, Vinay Senthil, Dinesh Thakur, Gaurav S. Sukhatme +1

RA-L 2022

We consider a scenario where a team of robots with heterogeneous sensors must track a set of targets or hazards which may induce sensory failures on the robots. In particular, the likelihood of failures depends on the proximity between the targets and the robots. We propose a control framework that

Cited by 25 Source ↗Scholar ↗

2020

Pac-Man is Overkill

Renato Fernando dos Santos, Ragesh K. Ramachandran, Marcos A. M. Vieira, Gaurav S. Sukhatme

IROS 2020poster

Pursuit-Evasion Game (PEG) consists of a team of pursuers trying to capture one or more evaders. PEG is important due to its application in surveillance, search and rescue, disaster robotics, boundary defense and so on. In general, PEG requires exponential time to compute the minimum number of pursu…

Cited by 1 Source ↗Scholar ↗

2020

Physics-based Simulation of Continuous-Wave LIDAR for Localization, Calibration and Tracking

Eric Heiden, Ziang Liu, Ragesh K. Ramachandran, Gaurav S. Sukhatme

ICRA 2020poster

Light Detection and Ranging (LIDAR) sensors play an important role in the perception stack of autonomous robots, supplying mapping and localization pipelines with depth measurements of the environment. While their accuracy outperforms other types of depth sensors, such as stereo or time-of-flight ca…

Cited by 20 Source ↗Scholar ↗

2020

Resilience in multi-robot target tracking through reconfiguration

Ragesh K. Ramachandran, Nicole Fronda, Gaurav S. Sukhatme

ICRA 2020poster

We address the problem of maintaining resource availability in a networked multi-robot system performing distributed target tracking. In our model, robots are equipped with sensing and computational resources enabling them to track a target's position using a Distributed Kalman Filter (DKF). We use…

Cited by 18 Source ↗Scholar ↗

2020

Resilient Coverage: Exploring the Local-to-Global Trade-off

Ragesh K. Ramachandran, Lifeng Zhou, James A. Preiss, Gaurav S. Sukhatme

IROS 2020poster

We propose a centralized control framework to select suitable robots from a heterogeneous pool and place them at appropriate locations to monitor a region for events of interest. In the event of a robot failure, our framework repositions robots in a user-defined local neighborhood of the failed robo…

Cited by 13 Source ↗Scholar ↗

2019

Resilience by Reconfiguration: Exploiting Heterogeneity in Robot Teams

Ragesh K. Ramachandran, James A. Preiss, Gaurav S. Sukhatme

IROS 2019poster

We propose a method to maintain high resource availability in a networked heterogeneous multi-robot system subject to resource failures. In our model, resources such as sensing and computation are available on robots. The robots are engaged in a joint task using these pooled resources. When a resour…

Cited by 48 Source ↗Scholar ↗

2017

A Probabilistic Approach to Automated Construction of Topological Maps Using a Stochastic Robotic Swarm

Ragesh K. Ramachandran, Sean Wilson, Spring Berman

RA-L 2017

In this paper, we present a novel procedure for constructing a topological map of an unknown environment from data collected by a swarm of robots with limited sensing capabilities and no communication or global localization. Topological maps are sparse roadmap representations of environments that ca

Cited by 15 Source ↗Scholar ↗