Senthil Hariharan Arul — accepted AI-conference papers

2025

Behav: Behavioral Rule Guided Autonomy Using VLMs for Robot Navigation in Outdoor Scenes

Kasun Weerakoon, Mohamed Elnoor, Gershom Seneviratne, Vignesh Rajagopal, Senthil Hariharan Arul, Jing Liang +2

ICRA 2025

We present BehAV, a novel approach for autonomous robot navigation in outdoor scenes guided by human instructions and leveraging Vision Language Models (VLMs). Our method interprets human commands using a Large Language Model (LLM), and categorizes the instructions into navigation and behavioral gui

Cited by 22 Source ↗Scholar ↗

2024

Unconstrained Model Predictive Control for Robot Navigation under Uncertainty

Senthil Hariharan Arul, Jong Jin Park, Vishnu Prem, Yang Zhang, Dinesh Manocha

ICRA 2024poster

In this paper, we present a probabilistic and unconstrained model predictive control formulation for robot navigation under uncertainty. We present (1) a closed-form approximation of the probability of collision that naturally models the propagation of uncertainty over the planning horizon and is co…

Cited by 2 Source ↗Scholar ↗

2024

VLPG-Nav: Object Navigation Using Visual Language Pose Graph and Object Localization Probability Maps

Senthil Hariharan Arul, Dhruva Kumar, Vivek Sugirtharaj, Richard Kim, Xuewei Tony Qi, Rajasimman Madhivanan +2

IROS 2024poster

We present VLPG-Nav, a visual language navigation method for guiding robots to specified objects within household scenes. Unlike existing methods primarily focused on navigating the robot toward objects, our approach considers the additional challenge of centering the object within the robot’s camer…

Cited by 1 Source ↗Scholar ↗

2024

When, What, and with Whom to Communicate: Enhancing RL-based Multi-Robot Navigation through Selective Communication

Senthil Hariharan Arul, Amrit Singh Bedi, Dinesh Manocha

IROS 2024poster

Decentralized navigation methods rely primarily on local observations, lacking the global awareness needed to coordinate effectively within a multi-agent system. Exchanging relevant messages between agents can promote cooperation and improve navigation efficiency. We present a Reinforcement Learning…

Cited by 3 Source ↗Scholar ↗

2023

3D-Online Generalized Sensed Shape Expansion: A Probabilistically Complete Motion Planner in Obstacle-Cluttered Unknown Environments

Vrushabh Zinage, Senthil Hariharan Arul, Dinesh Manocha, Satadal Ghosh

RA-L 2023

We present an online motion planning algorithm (3D-OGSSE) for generating smooth, collision-free trajectories over multiple planning iterations for a 3-D agent operating in an unknown, obstacle-cluttered, 3-D environment. In each planning iteration, 3D-OGSSE constructs an obstacle-free region termed

Cited by 3 Source ↗Scholar ↗

2023

DS-MPEPC: Safe and Deadlock-Avoiding Robot Navigation in Cluttered Dynamic Scenes

Senthil Hariharan Arul, Jong Jin Park, Dinesh Manocha

IROS 2023poster

We present an algorithm for safe robot navigation in complex dynamic environments using a variant of model predictive equilibrium point control. We use an optimization formulation to navigate robots gracefully in dynamic environments by optimizing over a trajectory cost function at each timestep. We…

Cited by 6 Source ↗Scholar ↗

2022

CGLR: Dense Multi-Agent Navigation Using Voronoi Cells and Congestion Metric-based Replanning

Senthil Hariharan Arul, Dinesh Manocha

IROS 2022poster

We present a decentralized path-planning algorithm for navigating multiple differential-drive robots in dense environments. In contrast to prior decentralized methods, we propose a novel congestion metric-based replanning that couples local and global planning techniques to efficiently navigate in s…

Cited by 2 Source ↗Scholar ↗

2021

SwarmCCO: Probabilistic Reactive Collision Avoidance for Quadrotor Swarms Under Uncertainty

Senthil Hariharan Arul, Dinesh Manocha

RA-L 2021

We present decentralized collision avoidance algorithms for quadrotor swarms operating under uncertain state estimation. Our approach exploits the differential flatness property and feedforward linearization to approximate the quadrotor dynamics and performs reciprocal collision avoidance. We accoun

Cited by 14 Source ↗Scholar ↗

2021

V-RVO: Decentralized Multi-Agent Collision Avoidance using Voronoi Diagrams and Reciprocal Velocity Obstacles

Senthil Hariharan Arul, Dinesh Manocha

IROS 2021poster

We present a decentralized collision avoidance method for dense environments based on buffered Voronoi cells (BVC) and reciprocal velocity obstacles (RVO). Our approach is designed for scenarios with a large number of agents in close proximity and provides passive-friendly collision avoidance guaran…

Cited by 41 Source ↗Scholar ↗

2020

DCAD: Decentralized Collision Avoidance With Dynamics Constraints for Agile Quadrotor Swarms

Senthil Hariharan Arul, Dinesh Manocha

RA-L 2020

We present DCAD, a novel, decentralized collision avoidance algorithm for navigating a swarm of quadrotors in dense environments populated with static and dynamic obstacles. Our algorithm relies on the concept of Optimal Reciprocal Collision Avoidance (ORCA) and utilizes a flatness-based Model Predi

Cited by 72 Source ↗Scholar ↗

2019

LSwarm: Efficient Collision Avoidance for Large Swarms With Coverage Constraints in Complex Urban Scenes

Senthil Hariharan Arul, Adarsh Jagan Sathyamoorthy, Shivang Patel, Michael W. Otte, Huan Xu, Ming C. Lin +1

RA-L 2019

In this letter, we address the problem of collision avoidance for a swarm of UAVs used for continuous surveillance of an urban environment. Our method, LSwarm, efficiently avoids collisions with static obstacles, dynamic obstacles and other agents in three-dimensional urban environments while consid

Cited by 42 Source ↗Scholar ↗