@ARTICLE{ren23moesTRO, author={Ren, Zhongqiang and Srinivasan, Akshaya Kesarimangalam and Vundurthy, Bhaskar and Abraham, Ian and Choset, Howie}, journal={IEEE Transactions on Robotics}, title={A Pareto-Optimal Local Optimization Framework for Multiobjective Ergodic Search}, year={2023}, volume={}, number={}, pages={1-12}, doi={10.1109/TRO.2023.3284358} } @ARTICLE{yan23muitare, author={Yan, Jingtian and Lin, Xingqiao and Ren, Zhongqiang and Zhao, Shiqi and Yu, Jieqiong and Cao, Chao and Yin, Peng and Zhang, Ji and Scherer, Sebastian}, journal={IEEE Robotics and Automation Letters}, title={MUI-TARE: Cooperative Multi-Agent Exploration With Unknown Initial Position}, year={2023}, volume={8}, number={7}, pages={4299-4306}, doi={10.1109/LRA.2023.3281262} } @ARTICLE{ren23cbssTRO, author={Ren, Zhongqiang and Rathinam, Sivakumar and Choset, Howie}, journal={IEEE Transactions on Robotics}, title={CBSS: A New Approach for Multiagent Combinatorial Path Finding}, year={2023}, volume={}, number={}, pages={1-15}, doi={10.1109/TRO.2023.3266993} } @inproceedings{ren23matcpf, title={Search Algorithms for Multi-Agent Teamwise Cooperative Path Finding}, author={Ren, Zhongqiang and Zhang, Chaoran and Rathinam, Sivakumar and Choset, Howie}, booktitle={2023 IEEE International Conference on Robotics and Automation (ICRA)}, pages={}, year={2023}, organization={IEEE} } @ARTICLE{ren22mocbs_tase, author={Ren, Zhongqiang and Rathinam, Sivakumar and Choset, Howie}, journal={IEEE Transactions on Automation Science and Engineering}, title={A Conflict-Based Search Framework for Multiobjective Multiagent Path Finding}, year={2023}, volume={20}, number={2}, pages={1262-1274}, doi={10.1109/TASE.2022.3183183} } @ARTICLE{ren22mosipp, author={Ren, Zhongqiang and Rathinam, Sivakumar and Likhachev, Maxim and Choset, Howie}, journal={IEEE Robotics and Automation Letters}, title={Multi-Objective Safe-Interval Path Planning With Dynamic Obstacles}, year={2022}, volume={7}, number={3}, pages={8154-8161}, doi={10.1109/LRA.2022.3187270} } @INPROCEEDINGS{ren22moes, AUTHOR = {Zhongqiang Ren AND {Akshaya Kesarimangalam} Srinivasan AND Howard Coffin AND Ian Abraham AND Howie Choset}, TITLE = {{A Local Optimization Framework for Multi-Objective Ergodic Search}}, BOOKTITLE = {Proceedings of Robotics: Science and Systems}, YEAR = {2022}, ADDRESS = {New York City, NY, USA}, MONTH = {June}, DOI = {10.15607/RSS.2022.XVIII.052} } @INPROCEEDINGS{ren22cbss, AUTHOR = {Zhongqiang Ren AND Sivakumar Rathinam AND Howie Choset}, TITLE = {{Conflict-Based Steiner Search for Multi-Agent Combinatorial Path Finding}}, BOOKTITLE = {Proceedings of Robotics: Science and Systems}, YEAR = {2022}, ADDRESS = {New York City, NY, USA}, MONTH = {June}, DOI = {10.15607/RSS.2022.XVIII.058} } @InProceedings{10.1007/978-3-031-21090-7_32, author="Ren, Zhongqiang and Rathinam, Sivakumar and Choset, Howie", editor="LaValle, Steven M. and O'Kane, Jason M. and Otte, Michael and Sadigh, Dorsa and Tokekar, Pratap", title="A Lower Bounding Framework for Motion Planning Amid Dynamic Obstacles in 2D", booktitle="Algorithmic Foundations of Robotics XV", year="2023", publisher="Springer International Publishing", address="Cham", pages="540--556", abstract="This work considers a Motion Planning Problem with Dynamic Obstacles (MPDO) in 2D that requires finding a minimum-arrival-time collision-free trajectory for a point robot between its start and goal locations amid dynamic obstacles moving along known trajectories. Existing methods, such as continuous Dijkstra paradigm, can find an optimal solution by restricting the shape of the obstacles or the motion of the robot, while this work makes no such assumptions. Other methods, such as search-based planners and sampling-based approaches can compute a feasible solution to this problem but do not provide approximation bounds. Since finding the optimum is challenging for MPDO, this paper develops a framework that can provide tight lower bounds to the optimum. These bounds act as proxies for the optimum which can then be used to bound the deviation of a feasible solution from the optimum. To accomplish this, we develop a framework that consists of (i) a bi-level discretization approach that converts the MPDO to a relaxed path planning problem, and (ii) an algorithm that can solve the relaxed problem to obtain lower bounds. We also present numerical results to corroborate the performance of the proposed framework. These results show that the bounds obtained by our approach for some instances are up to twice tighter than a baseline approach showcasing potential advantages of the proposed approach.", isbn="978-3-031-21090-7" } @inproceedings{ren22emoa, title={Enhanced multi-objective {A}* using balanced binary search trees}, author={Ren, Zhongqiang and Zhan, Richard and Rathinam, Sivakumar and Likhachev, Maxim and Choset, Howie}, booktitle={Proceedings of the International Symposium on Combinatorial Search}, volume={15}, number={1}, pages={162--170}, year={2022} } @ARTICLE{ren22mopbd, author={Ren, Zhongqiang and Rathinam, Sivakumar and Likhachev, Maxim and Choset, Howie}, journal={IEEE Robotics and Automation Letters}, title={Multi-Objective Path-Based D* Lite}, year={2022}, volume={7}, number={2}, pages={3318-3325}, doi={10.1109/LRA.2022.3146918} } @article{virmani22lm, title={Subdimensional Expansion Using Attention-Based Learning For Multi-Agent Path Finding}, author={Virmani, Lakshay and Ren, Zhongqiang and Rathinam, Sivakumar and Choset, Howie}, journal={arXiv preprint arXiv:2109.14695}, year={2021} } @article{ren22mocbssipp, title={Multi-objective Conflict-based Search Using Safe-interval Path Planning}, author={Ren, Zhongqiang and Rathinam, Sivakumar and Choset, Howie}, journal={arXiv preprint arXiv:2108.00745}, year={2021} } @ARTICLE{ren21momstar, author={Ren, Zhongqiang and Rathinam, Sivakumar and Choset, Howie}, journal={IEEE Robotics and Automation Letters}, title={Subdimensional Expansion for Multi-Objective Multi-Agent Path Finding}, year={2021}, volume={6}, number={4}, pages={7153-7160}, doi={10.1109/LRA.2021.3096744} } @inproceedings{ren21mocbs, title={Multi-objective conflict-based search for multi-agent path finding}, author={Ren, Zhongqiang and Rathinam, Sivakumar and Choset, Howie}, booktitle={2021 IEEE International Conference on Robotics and Automation (ICRA)}, pages={8786--8791}, year={2021}, organization={IEEE} } @inproceedings{ren21ms, title={Ms*: A new exact algorithm for multi-agent simultaneous multi-goal sequencing and path finding}, author={Ren, Zhongqiang and Rathinam, Sivakumar and Choset, Howie}, booktitle={2021 IEEE International Conference on Robotics and Automation (ICRA)}, pages={11560--11565}, year={2021}, organization={IEEE} } @inproceedings{ren21lss, title={Loosely synchronized search for multi-agent path finding with asynchronous actions}, author={Ren, Zhongqiang and Rathinam, Sivakumar and Choset, Howie}, booktitle={2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, pages={9714--9719}, year={2021}, organization={IEEE} } @inproceedings{gong18torus, title={Geometric motion planning for systems with toroidal and cylindrical shape spaces}, author={Gong, Chaohui and Ren, Zhongqiang and Whitman, Julian and Grover, Jaskaran and Chong, Baxi and Choset, Howie}, booktitle={Dynamic Systems and Control Conference}, volume={51913}, pages={V003T32A013}, year={2018}, organization={American Society of Mechanical Engineers} } @inproceedings{ren17deformed, title={Deformed state lattice planning}, author={Ren, Zhongqiang and Gong, Chaohui and Choset, Howie}, booktitle={2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, pages={6307--6312}, year={2017}, organization={IEEE} }