Safety Assessment and Control of Robotic Manipulators Using Danger Field

Lačević, Bakir; Rocco, Paolo; Zanchettin, Andrea Maria

doi:10.1109/tro.2013.2271097

Cited by 100 publications

(75 citation statements)

References 28 publications

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“…The concept of danger field has been already exploited from a control perspective in Lacevic, Rocco, and Zanchettin (2013), the sole common denominator with this work being the use of the danger field as a quantification of safety during human-robot interaction. Apart from this, the present manuscript proposes a Lyapunov-based control function whereby the motion of the robot is driven by the minimisation of such function, while Lacevic et al (2013) is based on a more traditional and industrially relevant architecture for velocity control, where the safety action is projected into the null space of a certain production constraint. This paper also presents a theoretical stability analysis, which is only sketched in a simplified scenario in Lacevic et al (2013).…”

Section: Introductionmentioning

confidence: 99%

“…Apart from this, the present manuscript proposes a Lyapunov-based control function whereby the motion of the robot is driven by the minimisation of such function, while Lacevic et al (2013) is based on a more traditional and industrially relevant architecture for velocity control, where the safety action is projected into the null space of a certain production constraint. This paper also presents a theoretical stability analysis, which is only sketched in a simplified scenario in Lacevic et al (2013). Thanks to the passivity property of the control system proposed in this work, the robot is allowed to come in contact with the environment, which is not allowed in work described in Lacevic et al (2013).…”

Section: Introductionmentioning

confidence: 99%

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Passivity-based control of robotic manipulators for safe cooperation with humans

Zanchettin

Lačević

Rocco

2014

International Journal of Control

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This paper presents a novel approach to the control of articulated robots in unstructured environments. The proposed control ensures several properties. First, the controller guarantees the achievement of a goal position without getting stuck in local minima. Then, the controller makes the closed-loop system passive, which renders the approach attractive for applications where the robot needs to safely interact with humans. Finally, the control law is explicitly shaped by the safety measure – the danger field. The proposed control law has been implemented and validated in a realistic experimental scenario, demonstrating the effectiveness in driving the robot to a given configuration in a cluttered environment, without any offline planning phase. Furthermore, the passivity of the system enables the robot to easily accommodate external forces on the tool, when a physical contact between the robot and the environment is established

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Passivity-based control of robotic manipulators for safe cooperation with humans

Zanchettin

Lačević

Rocco

2014

International Journal of Control

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“…From the safety perspective, our method is inspired by the research of Kulić and Croft (2006) and Lacevic et al (2013). However, Kulić and Croft (2006) do not consider task constraints, and thus neglects loss productivity as a consequence.…”

Section: Control Principalsmentioning

confidence: 99%

“…The method thus holds from a pure safety perspective, but not a human-robot collaborative perspective. The method presented by Lacevic et al (2013) considers task constraints, but their Danger Field only observes and analyzes the motion of the robot. The humans motion and movement patterns are not included.…”

Section: Control Principalsmentioning

confidence: 99%

A Proactive Strategy for Safe Human-Robot Collaboration based on a Simplified Risk Analysis

Sanderud¹,

Thomessen²,

Osumi³

et al. 2015

MIC

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In an increasing demand for human-robot collaboration systems, the need for safe robots is crucial. This paper presents a proactive strategy to enable an awareness of the current risk for the robot. The awareness is based upon a map of historically occupied space by the operator. The map is built based on a risk evaluation of each pose presented by the operator. The risk evaluation results in a risk field that can be used to evaluate the risk of a collaborative task. Based on this risk field, a control algorithm that constantly reduces the current risk within its task constraints was developed. Kinematic redundancy was exploited for simultaneous task performance within task constraints, and risk minimization. Sphere-based geometric models were used both for the human and robot. The strategy was tested in simulation, and implemented and experimentally tested on a NACHI MR20 7-axes industrial robot.

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“…The Danger Field, which quantifies the danger produced by a moving robot in a certain region of the space, has been already successfully applied to traditional manipulators as an ingredient for a reactive control strategy in [10], as well as a passivity field to be composed with a standard artificial potential field to simultaneously achieve obstacle avoidance and convergence to a desired goal position, see [11].…”

Section: Introductionmentioning

confidence: 99%

Reactive Constrained Model Predictive Control for Redundant Mobile Manipulators

Avanzini

Zanchettin

Rocco

2015

Intelligent Autonomous Systems 13

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Research interest in redundant mobile manipulators has been constantly increasing during the last decade. The opportunities offered by the redundant degrees of freedom, together with the exploitation of the mobile base, would allow such robots to complete their main task while complying with additional tasks or constraints. These features would make it easier for robots to work in a partly unstructured and dynamic environment, thus increasing production flexibility. In this work, a reactive constraint-based control strategy for mobile manipulators is proposed, which accomplishes a positioning task while simultaneously avoiding unknown and unpredictable obstacles. Differently from other approaches, the trajectory is computed exclusively online, by exploiting the MPC method, without the need of a pre-planned path. Experimental verification on a KUKA youBot shows the applicability of the approach.

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Safety Assessment and Control of Robotic Manipulators Using Danger Field

Cited by 100 publications

References 28 publications

Passivity-based control of robotic manipulators for safe cooperation with humans

Passivity-based control of robotic manipulators for safe cooperation with humans

A Proactive Strategy for Safe Human-Robot Collaboration based on a Simplified Risk Analysis

Reactive Constrained Model Predictive Control for Redundant Mobile Manipulators

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