update

Author: zkingston

_bibliography/papers.bib

@@ -6,6 +6,18 @@ @string{iros
 @string{ral = {IEEE Robotics and Automation Letters}}
 @string{tro = {IEEE Transactions on Robotics}}
 @string{ijrr = {The International Journal of Robotics Research}}
+@article{guo2026stac,
+  title        = {Efficient Multi-Robot Motion Planning for Manifold-Constrained Manipulators by Randomized Scheduling and Informed Path Generation},
+  author       = {Weihang Guo and Zachary Kingston and Kaiyu Hang and Lydia E. Kavraki},
+  abstract     = {Multi-robot motion planning for high degree-of-freedom manipulators in shared, constrained, and narrow spaces is a complex problem and essential for many scenarios such as construction, surgery, and more. Traditional coupled and decoupled methods either scale poorly or lack completeness, and hybrid methods that compose paths from individual robots together require the enumeration of many paths before they can find valid composite solutions. This paper introduces Scheduling to Avoid Collisions (StAC), a hybrid approach that more effectively composes paths from individual robots by scheduling (adding random stops and coordination motion along each path) and generates paths that are more likely to be feasible by using bidirectional feedback between the scheduler and motion planner for informed sampling. StAC uses 10 to 100 times fewer paths from the low-level planner than state-of-the-art baselines on challenging problems in manipulator cases.},
+  journal      = ral,
+  year         = 2026,
+  pdf          = {https://arxiv.org/abs/2412.00366},
+  projects     = {constraints,multi},
+  note         = {To Appear},
+  abbr         = {RAL},
+  preview      = {stac.jpg}
+}
 @misc{yan2025vizcoast,
   title        = {Using {VLM} Reasoning to Constrain Task and Motion Planning}, 
   author       = {Muyang Yan* and Miras Mengdibayev* and Ardon Floros and Weihang Guo and Lydia E. Kavraki and Zachary Kingston},
@@ -211,7 +223,7 @@ @inproceedings{agrawal2025mrangler
   doi          = {10.1109/OCEANS58557.2025.11104649},
   address      = {Brest, France},
   pdf          = {https://arxiv.org/abs/2506.06612},
-  projects     = {software},
+  projects     = {software,multi},
   abbr         = {OCEANS},
   preview      = {mrangler.jpg}
 }
@@ -255,7 +267,7 @@ @inproceedings{agrawal2025cnkz
   doi          = {10.1109/ICRA55743.2025.11127991},
   pdf          = {https://arxiv.org/abs/2410.21630},
   code         = {https://github.com/JBVAkshaya/PlanningOnManifoldIntersection},
-  projects     = {constraints},
+  projects     = {constraints,multi},
   abbr         = {ICRA},
   preview      = {cnkz.jpg}
 }
@@ -306,20 +318,6 @@ @inproceedings{liang2024ropras
   abbr         = {ISRR},
   preview      = {ropras_1.jpg}
 }
-@misc{guo2024stac,
-  title        = {Efficient Multi-Robot Motion Planning for Manifold-Constrained Manipulators by Randomized Scheduling and Informed Path Generation},
-  author       = {Weihang Guo and Zachary Kingston and Kaiyu Hang and Lydia E. Kavraki},
-  abstract     = {Multi-robot motion planning for high degree-of-freedom manipulators in shared, constrained, and narrow spaces is a complex problem and essential for many scenarios such as construction, surgery, and more. Traditional coupled and decoupled methods either scale poorly or lack completeness, and hybrid methods that compose paths from individual robots together require the enumeration of many paths before they can find valid composite solutions. This paper introduces Scheduling to Avoid Collisions (StAC), a hybrid approach that more effectively composes paths from individual robots by scheduling (adding random stops and coordination motion along each path) and generates paths that are more likely to be feasible by using bidirectional feedback between the scheduler and motion planner for informed sampling. StAC uses 10 to 100 times fewer paths from the low-level planner than state-of-the-art baselines on challenging problems in manipulator cases.},
-  eprint       = {2412.00366},
-  archiveprefix = {arXiv},
-  primaryclass = {cs.RO},
-  year         = 2024,
-  pdf          = {https://arxiv.org/abs/2412.00366},
-  projects     = {constraints},
-  note         = {Under Review},
-  abbr         = {ARXIV},
-  preview      = {stac.jpg}
-}
 @inproceedings{meng2024icra40,
   title        = {Perception-aware Planning for Robotics: Challenges and Opportunities},
   author       = {Qingxi Meng and Carlos Quintero-Peña and Zachary Kingston and Vaibhav Unhelkar and Lydia E. Kavraki},
@@ -641,6 +639,7 @@ @incollection{habibi2018dars
   publisher    = {Springer Proceedings in Advanced Robotics},
   pdf          = {https://s3.amazonaws.com/zk-bucket/rsc/Habibi2018.pdf},
   video        = {https://player.vimeo.com/video/287250201?loop=1&color=ffffff&byline=0&portrait=0},
+  projects     = {multi},
   preview      = {swarmchar.gif}
 }
 @article{dantam2018tmp,
@@ -734,6 +733,7 @@ @incollection{habibi2015aamas
   publisher    = {International Foundation for Autonomous Agents and Multiagent Systems},
   pdf          = {https://zkingston.com/papers/habibi2015aamas.pdf},
   abbr         = {AAMAS},
+  projects     = {multi},
   preview      = {pipeline.png}
 }
 @inproceedings{habibi2015icra,
@@ -747,5 +747,6 @@ @inproceedings{habibi2015icra
   pdf          = {https://zkingston.com/papers/habibi2015icra.pdf},
   video        = {https://player.vimeo.com/video/287250199?loop=1&color=ffffff&byline=0&portrait=0},
   abbr         = {ICRA},
+  projects     = {multi},
   preview      = {swarmtrans.gif}
 }

_members/shyam.md

@@ -11,5 +11,6 @@ website: "https://shyam-sivasubramanian.github.io/personalwebsite/"
 image: "shyam.jpg"
 group: Undergraduate Students
 group_rank: 5
+alumni: true
 ---
 I am a junior studying computer and data science at [Purdue University](https://www.purdue.edu/) My research interests include motion planning and deep reinorcement learning.

_projects/multi.md

@@ -0,0 +1,15 @@
+---
+layout: project
+key: multi 
+title: Multi-Robot Planning
+video: 
+front: true
+caption: Planning to coordinate multiple robots to achieve tasks that require collaboration.
+rank: 8
+---
+
+Multi-robot systems can do more than single robots through coordination and collaboration.
+Planning for teams introduces challenges in scalability, as the joint configuration space grows exponentially with the number of robots, and in handling interactions between robots that must share workspace, coordinate actions, or physically collaborate on tasks.
+
+Effective multi-robot planning requires reasoning about dependencies between robots' motions and managing computational complexity.
+Applications include warehouse automation with fleets of mobile robots and collaborative manipulation where multiple arms transport objects together, requiring task-level coordination.