A Task Allocation Approach for Human-Robot Collaboration in Product Defects Inspection Scenarios

Karami, Hossein; Darvish, Kourosh; Mastrogiovanni, Fulvio

doi:10.1109/ro-man47096.2020.9223455

Cited by 16 publications

(11 citation statements)

References 33 publications

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“…A framework referred as ConcHRC [ 67 ], which represents an extended version of the previous FlexHRC framework [ 68 ], allows the human operator to interact with several robots simultaneously for carrying out specific tasks. The architecture is composed of three layers: perception, representation and action.…”

Section: Robotic Systems and Human-robot Perceptionmentioning

confidence: 99%

Human-Robot Perception in Industrial Environments: A Survey

Bonci

Cheng

Indri

et al. 2021

Sensors

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Perception capability assumes significant importance for human–robot interaction. The forthcoming industrial environments will require a high level of automation to be flexible and adaptive enough to comply with the increasingly faster and low-cost market demands. Autonomous and collaborative robots able to adapt to varying and dynamic conditions of the environment, including the presence of human beings, will have an ever-greater role in this context. However, if the robot is not aware of the human position and intention, a shared workspace between robots and humans may decrease productivity and lead to human safety issues. This paper presents a survey on sensory equipment useful for human detection and action recognition in industrial environments. An overview of different sensors and perception techniques is presented. Various types of robotic systems commonly used in industry, such as fixed-base manipulators, collaborative robots, mobile robots and mobile manipulators, are considered, analyzing the most useful sensors and methods to perceive and react to the presence of human operators in industrial cooperative and collaborative applications. The paper also introduces two proofs of concept, developed by the authors for future collaborative robotic applications that benefit from enhanced capabilities of human perception and interaction. The first one concerns fixed-base collaborative robots, and proposes a solution for human safety in tasks requiring human collision avoidance or moving obstacles detection. The second one proposes a collaborative behavior implementable upon autonomous mobile robots, pursuing assigned tasks within an industrial space shared with human operators.

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Section: Robotic Systems and Human-robot Perceptionmentioning

confidence: 99%

Human-Robot Perception in Industrial Environments: A Survey

Bonci

Cheng

Indri

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…An AND/OR graph is a graph which represents a problemsolving process [2]. Below we provide a brief overview of AND/OR graphs; a detailed exposition can be found in [15]. Definition 7.…”

Section: And/or Graph Networkmentioning

confidence: 99%

“…The AND/OR graph representation provides a framework for the planning and scheduling of task sequences [21]. The feasibility of the tasks is then checked using a suitable motion planner [15]. Moreover, an AND/OR graph inherently requires fewer nodes than the corresponding complete state transition graph, reducing the search complexity of the AND/OR space [21].…”

Section: B Task Planning With And/or Graph Networkmentioning

confidence: 99%

A Task-Motion Planning Framework Using Iteratively Deepened AND/OR Graph Networks

Karami,

Thomas,

Mastrogiovanni

2021

Preprint

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We present an approach for Task-Motion Planning (TMP) using Iterative Deepened AND/OR Graph Networks (TMP-IDAN) that uses an AND/OR graph network based novel abstraction for compactly representing the task-level states and actions. While retrieving a target object from clutter, the number of object re-arrangements required to grasp the target is not known ahead of time. To address this challenge, in contrast to traditional AND/OR graph-based planners, we grow the AND/OR graph online until the target grasp is feasible and thereby obtain a network of AND/OR graphs. The AND/OR graph network allows faster computations than traditional task planners. We validate our approach and evaluate its capabilities using a Baxter robot and a state-of-the-art robotics simulator in several challenging non-trivial cluttered table-top scenarios. The experiments show that our approach is readily scalable to increasing number of objects and different degrees of clutter.

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“…We now provide a brief overview of AND/OR graphs [5,16]. An AND/OR graph is a graph which represents a problem-solving process [3].…”

Section: Task-motion Planning and And/or Graphsmentioning

confidence: 99%

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

Karami¹,

Thomas²,

Mastrogiovanni³

2021

Preprint

Self Cite

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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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A Task Allocation Approach for Human-Robot Collaboration in Product Defects Inspection Scenarios

Cited by 16 publications

References 33 publications

Human-Robot Perception in Industrial Environments: A Survey

Human-Robot Perception in Industrial Environments: A Survey

A Task-Motion Planning Framework Using Iteratively Deepened AND/OR Graph Networks

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

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