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Wenhao Luo

28 accepted papers

2026

Geometry-Aware Control Barrier Functions for Collision Avoidance Via Bernstein Polynomial Approximations

ICRA 2026poster

Safe navigation often relies on well-defined conditions based on the shape of robots and obstacles, and can be challenging when they have irregular geometries. While Control Barrier Functions (CBFs) offer an efficient mechanism to enforce safe set forward invariance, common shape surrogates (e.g., s…

2025

Adaptive Deadlock Avoidance for Decentralized Multi-Agent Systems via CBF-Inspired Risk Measurement

ICRA 2025

Decentralized safe control plays an important role in multi-agent systems given the scalability and robustness without reliance on a central authority. However, without an explicit global coordinator, the decentralized control methods are often prone to deadlock - a state where the system reaches eq

Cited by 5SourceScholar
2025

Computationally and Sample Efficient Safe Reinforcement Learning Using Adaptive Conformal Prediction

ICRA 2025

Safety is a critical concern in learning-enabled autonomous systems especially when deploying these systems in real-world scenarios. An important challenge is accurately quantifying the uncertainty of unknown models to generate provably safe control policies that facilitate the gathering of informat

Cited by 4SourceScholar
2025

Distributed Multi-Robot Source Seeking in Unknown Environments with Unknown Number of Sources

ICRA 2025

We introduce a novel distributed source seeking framework, DIAS, designed for multi-robot systems in scenarios where the number of sources is unknown and potentially exceeds the number of robots. Traditional robotic source seeking methods typically focused on directing each robot to a specific stron

Cited by 1SourceScholar
2025

Integrating Multi-Robot Adaptive Sampling and Informative Path Planning for Spatiotemporal Natural Environment Prediction

ICRA 2025

Learning to predict spatiotemporal (ST) environmental processes from a sparse set of samples collected autonomously is a difficult task from both a sampling perspective (collecting the best sparse samples) and from a learning perspective (predicting the next timestep). In this work, we focus on inve

Cited by 3SourceScholar
2025

Merry-Go-Round: Safe Control of Decentralized Multi-Robot Systems with Deadlock Prevention

IROS 2025

We propose a hybrid approach for decentralized multi-robot navigation that ensures both safety and deadlock prevention. Building on a standard control formulation, we add a lightweight deadlock prevention mechanism by forming temporary "roundabouts" (circular reference paths). Each robot relies only

Cited by 2SourceScholar
2024

Decentralized Multi-Robot Line-of-Sight Connectivity Maintenance under Uncertainty

RSS 2024poster

In this paper, we propose a novel decentralized control method to maintain Line-of-Sight connectivity for multi-robot networks in the presence of Guassian-distributed localization uncertainty. In contrast to most existing work that assumes perfect positional information about robots or enforces over…

Cited by 1SourcePDFScholar
2024

Integrating Online Learning and Connectivity Maintenance for Communication-Aware Multi-Robot Coordination

IROS 2024poster

This paper proposes a novel data-driven control strategy for maintaining connectivity in networked multi-robot systems. Existing approaches often rely on a predetermined communication model specifying whether pairwise robots can communicate given their relative distance to guide the connectivity-awa…

Cited by 0SourcecodeScholar
2023

Minimally Constrained Multi-Robot Coordination with Line-of-Sight Connectivity Maintenance

ICRA 2023poster

In this paper, we consider a team of mobile robots executing simultaneously multiple behaviors by different subgroups, while maintaining global and subgroup line-of-sight (LOS) network connectivity that minimally constrains the original multi-robot behaviors. The LOS connectivity between pairwise ro…

Cited by 7SourceScholar
2023

Risk-Aware Decentralized Safe Control via Dynamic Responsibility Allocation (Student Abstract)

AAAI 2023technical

In this work, we present a novel risk-aware decentralized Control Barrier Function (CBF)-based controller for multi-agent systems. The proposed decentralized controller is composed based on pairwise agent responsibility shares (a percentage), calculated from the risk evaluation of each individual ag…

Cited by 0SourcePDFScholar
2023

Risk-Aware Safe Control for Decentralized Multi-Agent Systems via Dynamic Responsibility Allocation

IROS 2023poster

Decentralized control schemes are increasingly favored in various domains that involve multi-agent systems due to the need for computational efficiency as well as general applicability to large-scale systems. However, in the absence of an explicit global coordinator, it is hard for distributed agent…

Cited by 9SourceScholar
2022

Collective Conditioned Reflex: A Bio-Inspired Fast Emergency Reaction Mechanism for Designing Safe Multi-Robot Systems

RA-L 2022

A multi-robot system (MRS) is a group of coordinated robots designed to cooperate with each other and accomplish given tasks. Due to the uncertainties in operating environments, the system may encounter emergencies, such as unobserved obstacles, moving vehicles, and extreme weather. Animal groups su

Cited by 5SourceScholar
2021

Distributed Topology Correction for Flexible Connectivity Maintenance in Multi-Robot Systems

ICRA 2021poster

Multi-robot systems can perform task-related collaborative behaviors while maintaining connectivity within the system. However, some robots may fail to execute tasks or converge relatively slowly due to connectivity constraints. We consider the case that some robots may not have tasks assigned at a…

Cited by 11SourceScholar
2021

Hiding Leader’s Identity in Leader-Follower Navigation through Multi-Agent Reinforcement Learning

IROS 2021poster

Leader-follower navigation is a popular class of multi-robot algorithms where a leader robot leads the follower robots in a team. The leader has specialized capabilities or mission critical information (e.g. goal location) that the followers lack, and this makes the leader crucial for the mission’s…

Cited by 6SourcecodeScholar
2021

Meta Preference Learning for Fast User Adaptation in Human-Supervisory Multi-Robot Deployments

IROS 2021poster

As multi-robot systems (MRS) are widely used in various tasks such as natural disaster response and social security, people enthusiastically expect an MRS to be ubiquitous that a general user without heavy training can easily operate. However, humans have various preferences on balancing between tas…

Cited by 17SourceScholar
2020

Adaptive Informative Sampling with Environment Partitioning for Heterogeneous Multi-Robot Systems

IROS 2020poster

Multi-robot systems are widely used in environmental exploration and modeling, especially in hazardous environments. However, different types of robots are limited by different mobility, battery life, sensor type, etc. Heterogeneous robot systems are able to utilize various types of robots and provi…

Cited by 45SourceScholar
2020

Behavior Mixing with Minimum Global and Subgroup Connectivity Maintenance for Large-Scale Multi-Robot Systems

ICRA 2020poster

In many cases the multi-robot systems are desired to execute simultaneously multiple behaviors with different controllers, and sequences of behaviors in real time, which we call behavior mixing. Behavior mixing is accomplished when different subgroups of the overall robot team change their controlle…

Cited by 24SourceScholar
2020

Minimally Disruptive Connectivity Enhancement for Resilient Multi-Robot Teams

IROS 2020poster

In this work, we focus on developing algorithms to maintain and enhance the connectivity of a multi-robot system with minimal disruption to the primary tasks that the robots are performing. Such algorithms are useful for collaborating robots to be resilient to reduction in connectivity of the commun…

Cited by 8SourceScholar
2020

Multi-Robot Collision Avoidance under Uncertainty with Probabilistic Safety Barrier Certificates

NeurIPS 2020spotlight

Safety in terms of collision avoidance for multi-robot systems is a difficult challenge under uncertainty, non-determinism, and lack of complete information. This paper aims to propose a collision avoidance method that accounts for both measurement uncertainty and motion uncertainty. In particular,…

2019

Heuristic-based Multiple Mobile Depots Route Planning for Recharging Persistent Surveillance Robots

IROS 2019poster

Persistent surveillance of a target space using multiple unmanned aerial vehicles (UAVs) has multiple applications such as geographical surveys, air quality monitoring, and security monitoring. The limited onboard battery capacity challenges the continuous operation in these applications of persiste…

Cited by 7SourceScholar
2018

Adaptive Sampling and Online Learning in Multi-Robot Sensor Coverage with Mixture of Gaussian Processes

ICRA 2018poster

We consider the problem of online environmental sampling and modeling for multi-robot sensor coverage, where a team of robots spread out over the workspace in order to optimize the overall sensing performance. In contrast to most existing works on multi-robot coverage control that assume prior knowl…

Cited by 123SourceScholar
2017

Decentralized coordinated motion for a large team of robots preserving connectivity and avoiding collisions

ICRA 2017poster

We consider the general problem of moving a large number of networked robots toward a goal position through a cluttered environment while preserving network communication connectivity and avoiding both inter-robot collisions and collision with obstacles. In contrast to previous approaches that eithe…

Cited by 7SourceScholar
2016

Distributed knowledge leader selection for multi-robot environmental sampling under bandwidth constraints

IROS 2016poster

In many multi-robot applications such as target search, environmental monitoring and reconnaissance, the multi-robot system operates semi-autonomously, but under the supervision of a remote human who monitors task progress. In these applications, each robot collects a large amount of task-specific d…

Cited by 24SourceScholar