Combining motion planning and task assignment for a dual-arm system

Rodríguez, Carlos; Suárez, Raúl

doi:10.1109/iros.2016.7759624

Cited by 14 publications

(5 citation statements)

References 19 publications

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“…We focus on a subclass of these problems, where we wish to move objects in the presence of movable obstacles. In [18], [19], efficient approaches are proposed for the multi-robot object retrieval problem, assuming permanent object removal and considering one target object at a time, while our planner considers several target objects at the same time and relocates the obstacles within the workspace. Multi-robot rearrangement planning problems [5]- [7] are also closely related to MR-GTAMP.…”

Section: Related Workmentioning

confidence: 99%

“…Then, for each pair of a robot R ∈ R and its grasp g M,R ∈ Gr M,R , we find all partially grounded pick-and-place actions ā that move object M to its target region Re M with R as the pick robot (Alg. 1, line [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. For each partially grounded pick-and-place action ā, we find all movable objects that block the pick action of ā and add the corresponding block-pick edges (Alg.…”

mentioning

confidence: 99%

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A MIP-Based Approach for Multi-Robot Geometric Task-and-Motion Planning

Zhang¹,

Chan²,

Zhong³

et al. 2022

Preprint

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We address multi-robot geometric task-andmotion planning (MR-GTAMP) problems in synchronous, monotone setups. The goal of the MR-GTAMP problem is to move objects with multiple robots to goal regions in the presence of other movable objects. To perform the tasks successfully and effectively, the robots have to adopt intelligent collaboration strategies, i.e., decide which robot should move which objects to which positions, and perform collaborative actions, such as handovers. To endow robots with these collaboration capabilities, we propose to first collect occlusion and reachability information for each robot as well as information about whether two robots can perform a handover action by calling motionplanning algorithms. We then propose a method that uses the collected information to build a graph structure which captures the precedence of the manipulations of different objects and supports the implementation of a mixed-integer program to guide the search for highly effective collaborative task-andmotion plans. The search process for collaborative task-andmotion plans is based on a Monte-Carlo Tree Search (MCTS) exploration strategy to achieve exploration-exploitation balance. We evaluate our framework in two challenging GTAMP domains and show that it can generate high-quality task-andmotion plans with respect to the planning time, the resulting plan length and the number of objects moved compared to two state-of-the-art baselines.

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Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

A MIP-Based Approach for Multi-Robot Geometric Task-and-Motion Planning

Zhang¹,

Chan²,

Zhong³

et al. 2022

Preprint

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“…Current TMP approaches for multi-arm robot systems focus on coordinated planning strategies, and consider simple pick-and-place or assembly tasks. As such, these methods do not scale to complex manipulation tasks [29,40]. TMP for multi-arm robot systems in the context of welding is considered in [1], whereas [40] discusses an approach for multi-arm TMP manipulation.…”

Section: Related Workmentioning

confidence: 99%

Task Allocation for Multi-Robot Task and Motion Planning: a case for Object Picking in Cluttered Workspaces

Karami¹,

Thomas²,

Mastrogiovanni³

2021

Preprint

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We present an AND/OR graph-based, integrated multi-robot task and motion planning approach which (i) performs task allocation coordinating the activity of a given number of robots, and (ii) is capable of handling tasks which involve an a priori unknown number of object re-arrangements, such as those involved in retrieving objects from cluttered workspaces. Such situations may arise, for example, in search and rescue scenarios, while locating/picking a cluttered object of interest. The corresponding problem falls under the category of planning in clutter. One of the challenges while planning in clutter is that the number of object re-arrangements required to pick the target object is not known beforehand, in general. Moreover, such tasks can be decomposed in a variety of ways, since different cluttering object re-arrangements are possible to reach the target object. In our approach, task allocation and decomposition is achieved by maximizing a combined utility function. The allocated tasks are performed by an integrated task and motion planner, which is robust to the requirement of an unknown number of re-arrangement tasks. We demonstrate our results with experiments in simulation on two Franka Emika manipulators.

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“…Manipulation problems of different nature have been tackled in the literature with different strategies, e.g., the manipulation problem of Navigation Among Movable Obstacles (NAMO) has been addressed in [2,3] using a backward search algorithm, and dual-arm table-top manipulation problems by combining motion planning and task assignment [4]. These robotic applications, like many others, must deal with different sources of uncertainty and the use of sensors and perception strategies may be required, e.g., the studies in [5,6] have investigated the machine robotic cell scheduling problem for manufacturing systems with or without sensor inspection.…”

Section: Related Workmentioning

confidence: 99%

“…Evaluating π [lines [3][4][5][6][7][8][9][10][11][12][13]: Actions in π are sent to the relaxed geometric reasoning for the feasibility evaluation [line 5]. Basically, this process tries to figure out whether there is any feasible world to meet the action conditions or not as we proposed in [19].…”

Section: Contingent Heuristic Computation Using Relaxed Informationmentioning

confidence: 99%

Contingent Task and Motion Planning under Uncertainty for Human–Robot Interactions

2020

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Manipulation planning under incomplete information is a highly challenging task for mobile manipulators. Uncertainty can be resolved by robot perception modules or using human knowledge in the execution process. Human operators can also collaborate with robots for the execution of some difficult actions or as helpers in sharing the task knowledge. In this scope, a contingent-based task and motion planning is proposed taking into account robot uncertainty and human-robot interactions, resulting a tree-shaped set of geometrically feasible plans. Different sorts of geometric reasoning processes are embedded inside the planner to cope with task constraints like detecting occluding objects when a robot needs to grasp an object. The proposal has been evaluated with different challenging scenarios in simulation and a real environment.

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Combining motion planning and task assignment for a dual-arm system

Cited by 14 publications

References 19 publications

A MIP-Based Approach for Multi-Robot Geometric Task-and-Motion Planning

A MIP-Based Approach for Multi-Robot Geometric Task-and-Motion Planning

Task Allocation for Multi-Robot Task and Motion Planning: a case for Object Picking in Cluttered Workspaces

Contingent Task and Motion Planning under Uncertainty for Human–Robot Interactions

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