Home Page for Juan Irving Solis Vidana | Parasol Laboratory


Picture Juan Irving Solis Vidana
PhD Student
Parasol Laboratory url: http://parasollab.web.illinois.edu/~juanis/
Department of Computer Science email:
University of Illinois at Urbana-Champaign office: 3307 Siebel Center
Urbana, IL 61801, USA


CV

Hi!, My name is Juan Irving Solis, and I am a PhD candidate from Texas A&M University.

My research interests are motion planning with a focus on applications to robotics, specifically in the domain of multi-robot systems. Currently, I am investigating hybrid multi-robot motion planning algorithms to enhance scalability and reduce planning time while maintaining efficient cost solutions.

I am also interested in applying our algorithms to physical robots to solve real-world problems. I recently collaborated in a human-robot collaboration projects for the manufacturing industry, specifically focusing on the mobile robot navigation part.

Research

Multi-Agent Systems


We present projects related to multi-agent systems, ranging from pure motion planning techniques for coordinating a team of robots to more complex problems involving task allocations and task-and-motion for multiple agents.

Hybrid Planning


Multi-robot hybrid techniques guide individual robots and coordinate them when conflicts arise. Our research explores these algorithms for solving complex scenarios with numerous robots requiring precise coordination.

Publications

Adaptive Robot Coordination: A Subproblem-based Approach for Hybrid Multi-Robot Motion Planning, Irving Solis, James Motes, Mike Qin, Marco Morales, Nancy M. Amato, ArXiv Preprint, Dec 2023. DOI: https://arxiv.org/abs/2312.08554
Keywords: Motion Planning, Multi-Agent Systems, Sampling-Based Motion Planning
Links : [ArXiv]

BibTex

@misc{solis2023adaptive,
title={Adaptive Robot Coordination: A Subproblem-based Approach for Hybrid Multi-Robot Motion Planning},
author={Irving Solis and James Motes and Mike Qin and Marco Morales and Nancy M. Amato},
year={2023},
eprint={2312.08554},
archivePrefix={arXiv},
primaryClass={cs.RO}
}


Abstract

This work presents Adaptive Robot Coordination (ARC), a novel hybrid framework for multi-robot motion planning (MRMP) that employs local subproblems to resolve inter-robot conflicts. ARC creates subproblems centered around conflicts, and the solutions represent the robot motions required to resolve these conflicts. The use of subproblems enables an inexpensive hybrid exploration of the multi-robot planning space. ARC leverages the hybrid exploration by dynamically adjusting the coupling and decoupling of the multi-robot planning space. This allows ARC to adapt the levels of coordination efficiently by planning in decoupled spaces, where robots can operate independently, and in coupled spaces where coordination is essential. ARC is probabilistically complete, can be used for any robot, and produces efficient cost solutions in reduced planning times. Through extensive evaluation across representative scenarios with different robots requiring various levels of coordination, ARC demonstrates its ability to provide simultaneous scalability and precise coordination. ARC is the only method capable of solving all the scenarios and is competitive with coupled, decoupled, and hybrid baselines.


Representation-Optimal Multi-Robot Motion Planning using Conflict-Based Search, Irving Solis, James Motes, Read Sandström, Nancy M. Amato, IEEE Robotics and Automation Letters, Mar 2021. DOI: https://doi.org/10.1109/LRA.2021.3068910
Keywords: Industrial Applications, Motion Planning, Multi-Agent
Links : [Published] [Manuscript]

BibTex

@article{solis2019representation,
title={Representation-optimal multi-robot motion planning using conflict-based search},
author={Solis, Irving and Sandstr{\"o}m, Read and Motes, James and Amato, Nancy M},
journal={arXiv preprint arXiv:1909.13352},
year={2019}
}


Abstract

Multi-Agent Motion Planning (MAMP) is the problem of computing feasible paths for a set of agents each with individual start and goal states within a continuous state space. Existing approaches can be split into coupled methods which provide optimal solutions but struggle with scalability or decoupled methods which provide scalable solutions but offer no optimality guarantees. Recent work has explored hybrid approaches that leverage the advantages of both coupled and decoupled approaches in an easier discrete subproblem, Multi-Agent Pathfinding (MAPF). In this work, we adapt recent developments in hybrid MAPF to the continuous domain of MAMP. We demonstrate the scalability of our method to manage groups of up to 32 agents, demonstrate the ability to handle up to 8 high-DOF manipulators, and plan for heterogeneous teams. In all scenarios, our approach plans significantly faster while providing higher quality solutions.