Flexible human–robot cooperation models for assisted shop-floor tasks

Darvish, Kourosh; Wanderlingh, Francesco; Bruno, Barbara; Simetti, Enrico; Mastrogiovanni, Fulvio; Casalino, Giuseppe

doi:10.1016/j.mechatronics.2018.03.006

Cited by 66 publications

(59 citation statements)

References 36 publications

(55 reference statements)

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“…Approaches in the first class aim at defining cooperation models, i.e., data structures modelling the task to be jointly carried out, while keeping flexibility and human preferences into account [44,45,3,10,15,46,11].…”

Section: Planning Techniques In Human-robot Cooperationmentioning

confidence: 99%

“…However, differently from the approach in [15], the work by Sebastiani and colleagues support on-line changes during plan execution. Finally, the work by Darvish and colleagues represents cooperation models using AND/OR graphs, and allows for a switch among different cooperation sequences at runtime [11], therefore allowing humans to redefine the sequence of tasks among a predefined set of choices. The human does not have to explicitly signal the switch to the robot, whereas the robot adapts to the new cooperation context reactively.…”

Section: Planning Techniques In Human-robot Cooperationmentioning

confidence: 99%

“…The first case originates from motion planning or execution issues, e.g., because of a cluttered workspace [17] or to prevent any harm to the human operator [6,7,11]. If motion issues occur, planHRC does not generate a state compatible with s e j+1 .…”

Section: Reasoning In the Ontology Or The Cooperation Modelmentioning

confidence: 99%

“…As far as the cooperation process is concerned, two high-level directives must be taken into account: D 1 cooperation models (and robot action planning techniques) enforcing the prescribed objectives must be adopted [10,11]; D 2 the robot must be flexible enough to adapt to human operator actions avoiding a purely reactive approach [12,13], and to make its intentions clear [14,15].…”

Section: Introductionmentioning

confidence: 99%

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On the manipulation of articulated objects in human–robot cooperation scenarios

Capitanelli

Maratea

Mastrogiovanni

et al. 2018

Robotics and Autonomous Systems

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Articulated and flexible objects constitute a challenge for robot manipulation tasks, but are present in different real-world settings, including home and industrial environments. Current approaches to the manipulation of articulated and flexible objects employ ad hoc strategies to sequence and perform actions on them depending on a number of physical or geometrical characteristics related to those objects, as well as on an a priori classification of target object configurations.In this paper, we propose an action planning and execution framework, which (i) considers abstract representations of articulated or flexible objects, (ii) integrates action planning to reason upon such configurations and to sequence an appropriate set of actions with the aim of obtaining a target configuration provided as a goal, and (iii) is able to cooperate with humans to collaboratively carry out the plan.On the one hand, we show that a trade-off exists between the way articulated or flexible objects are perceived and how the system represents them. Such a trade-off greatly impacts on the complexity of the planning process. On the other hand, we demonstrate the system's capabilities in allowing humans to interrupt robot action execution, and -in general -to contribute to the whole manipulation process.Results related to planning performance are discussed, and examples with a Baxter dual-arm manipulator performing actions collaboratively with humans are shown.

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Section: Planning Techniques In Human-robot Cooperationmentioning

confidence: 99%

Section: Planning Techniques In Human-robot Cooperationmentioning

confidence: 99%

Section: Reasoning In the Ontology Or The Cooperation Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On the manipulation of articulated objects in human–robot cooperation scenarios

Capitanelli

Maratea

Mastrogiovanni

et al. 2018

Robotics and Autonomous Systems

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“…However, the tenets of the Industry 4.0 paradigm are expected to require relaxing such assumptions. Given the goal of providing customers with personalised and just-intime delivery of products, it is foreseen that warehouse environments will become more dynamic and human-friendly, and will host human-robot collaborative processes to a great extent [18,27,31]. As far as AGVs are concerned, such directives imply higher standards in autonomy, as well as more robust perception and decision making capabilities.…”

mentioning

confidence: 99%

Detection, localisation and tracking of pallets using machine learning techniques and 2D range data

Mohamed

Capitanelli

Mastrogiovanni

et al. 2019

Neural Comput & Applic

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The problem of autonomous transportation in industrial scenarios is receiving a renewed interest due to the way it can revolutionise internal logistics, especially in unstructured environments. This paper presents a novel architecture allowing a robot to detect, localise, and track (possibly multiple) pallets using machine learning techniques based on an on-board 2D laser rangefinder only. The architecture is composed of two main components: the first stage is a pallet detector employing a Faster Region-based Convolutional Neural Network (Faster R-CNN) detector cascaded with a CNN-based classifier; the second stage is a Kalman filter for localising and tracking detected pallets, which we also use to defer commitment to a pallet detected in the first stage until sufficient confidence has been acquired via a sequential data acquisition process. For fine-tuning the CNNs, the architecture has been systematically evaluated using a real-world dataset containing 340 labeled 2DThe research leading to these results has received funding from the POR/FESR Liguria regional funding scheme, under grant agreement n. 56 (AIRONE).

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Industry 4.0 for SMEs

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2020

121

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Flexible human–robot cooperation models for assisted shop-floor tasks

Cited by 66 publications

References 36 publications

On the manipulation of articulated objects in human–robot cooperation scenarios

On the manipulation of articulated objects in human–robot cooperation scenarios

Detection, localisation and tracking of pallets using machine learning techniques and 2D range data

Industry 4.0 for SMEs

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