@conference {Zhang2021,
	title = {Task-Space Decomposed Motion Planning Framework for Multi-Robot Loco-Manipulation},
	booktitle = {2021 IEEE International Conference on Robotics and Automation (ICRA)},
	year = {2021},
	pages = {8158{\textendash}8164},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Xi{\textquoteright}an, China},
	abstract = {This paper introduces a novel task-space decomposed motion planning framework for multi-robot simultaneous locomotion and manipulation. When several manipulators hold an object, closed-chain kinematic constraints are formed, and it will make the motion planning problems challenging by inducing lower-dimensional singularities. Unfortunately, the constrained manifold will be even more complicated when the manipulators are equipped with mobile bases. We address the problem by introducing a dual-resolution motion planning framework which utilizes a convex task region decomposition method, with each resolution tuned to efficient computation for their respective roles. Concretely, this dual-resolution approach enables a global planner to explore the low-dimensional decomposed task-space regions toward the goal, then a local planner computes a path in high-dimensional constrained configuration space. We demonstrate the proposed method in several simulations, where the robot team transports the object toward the goal in the obstacle-rich environments.},
	keywords = {Computational modeling, Conferences, Kinematics, Manifolds, Manipulators, Planning, System recovery},
	isbn = {978-1-7281-9078-5},
	issn = {1050-4729},
	doi = {10.1109/ICRA48506.2021.9560902},
	author = {Xiaoyu Zhang and Lei Yan and Tin Lun Lam and Sethu Vijayakumar}
}
@conference {38,
	title = {Fast task-sequence allocation for heterogeneous robot teams with a human in the loop},
	booktitle = {Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems {(IROS)},
	year = {2013},
	abstract = {Efficient task allocation with timing constraints to a team of possibly heterogeneous robots is a challenging problem with application, e.g., in search and rescue. In this paper a mixed-integer linear programming (MILP) approach is proposed for assigning heterogeneous robot teams to the simultaneous completion of sequences of tasks with specific requirements such as completion deadlines. For this purpose our approach efficiently combines the strength of state of the art Mixed Integer Linear Programming (MILP) solvers with human expertise in mission scheduling. We experimentally show that simple and intuitive inputs by a human user have substantial impact on both computation time and quality of the solution. The presented approach can in principle be applied to quite general missions for robot teams with human supervision.},
	keywords = {computation time, Computational modeling, fast task sequence allocation, heterogeneous robot teams, heterogeneous robots, human in the loop, human supervision, human user, integer programming, linear programming, Mathematical model, MILP solvers, mission scheduling, mixed integer linear programming, Multi-robot systems, Resource management, Robot kinematics, Schedules, task allocation},
	doi = {10.1109/IROS.2013.6696570},
	author = {Petersen, K. and Kleiner, A. and Von Stryk, O.}
}
