The Experts below are selected from a list of 1929 Experts worldwide ranked by ideXlab platform
Cristina Urdiales - One of the best experts on this subject based on the ideXlab platform.
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A Biomimetical Dynamic Window Approach to Navigation for Collaborative Control
IEEE Transactions on Human-Machine Systems, 2017Co-Authors: Joaquin Ballesteros, Cristina Urdiales, Antonio Martínez B. Velasco, Gonzalo Ramos-jiménezAbstract:Shared Control is a strategy used in assistive platforms to combine human and robot orders to achieve a goal. Collaborative Control is a specific shared Control approach, in which user's and robot's commands are merged into an emergent one in a continuous way. Robot commands tend to improve efficiency and safety. However, sometimes, assistance can be rejected by users when their commands are too altered. This provokes frustration and stress and, usually, decreases emergent efficiency. To improve acceptance, robot navigation algorithms can be adapted to mimic human behavior when possible. We propose a novel variation of the well-known dynamic window approach (DWA) that we call biomimetical DWA (BDWA). The BDWA relies on a reward function extracted from real traces from volunteers presenting different motor disabilities navigating in a hospital environment using a rollator for support. We have compared the BDWA with other reactive algorithms in terms of similarity to paths completed by people with disabilities using a robotic rollator in a rehabilitation hospital unit. The BDWA outperforms all tested algorithms in terms of likeness to human paths and success rate.
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Yes, We K-an: Modulated Collaborative Control
Collaborative Assistive Robot for Mobility Enhancement (CARMEN), 2012Co-Authors: Cristina UrdialesAbstract:In previous episodes of our hospital series, we saw how Collaborative Control equalized the performance of persons with different cognitive and physical disabilities. In almost every case, they were able to finish mildly complex trajectories. However, it was only a matter of time until we found a person with disabilities so severe that assistance provided by our Control scheme was not enough for her to finish a trajectory. We also observed that Control glitches in complex areas where both robot and human efficiency changed very quickly led to remarkably lower average efficiencies. This was also the case when CBR was used, specially after short trainings where only a reduced number of cases had been learnt and robot Control switched a lot from PFA to CBR. Naturally, these problems could be solved, at least partially, if the amount of assistance provided could be stabilized during a certain time period, condition or situation. However, we did not want to lose the reactive nature of our algorithm, that had worked so fine for us thus far. In fact, there is a pretty classic approach to solve this problem in a strictly analytical way: modulation of the Control function.
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Wheelchair Collaborative Control for disabled users navigating indoors
Artificial Intelligence in Medicine, 2011Co-Authors: Cristina Urdiales, Roberta Annichiaricco, Manuel Fernández-carmona, J. M. Peula, Ulises Cortés, Carlo Caltagirone, Francisco SandovalAbstract:Objective: Mobility is of key importance for autonomous living. Persons with severe disabilities may be assisted by robotic wheelchairs when manual Control is not possible. However, these persons should contribute to Control as much as they can to avoid loss of residual skills and frustration. Traditionally, wheelchair shared Control approaches either give Control to person or robot depending on the situation. Methods and materials: We propose a new shared Control technique where robot and person contribute simultaneously to Control. Their commands are weighted according to their respective local efficiencies and then combined via a reactive navigation strategy. Thus, assistance adapts to the user's needs. We refer to this approach as Collaborative Control. Results: Collaborative Control was tested in a home environment in Fondazione Santa Lucia (Rome) by 18 volunteers presenting different degrees of physical and cognitive disability. All of them successfully finished a complex test path with assistance. Both users and caregivers' opinion on the system was very positive. Acceptance was very good according to the psychosocial impact of assistive devices scale. Conclusions: Collaborative Control adapts to the person's needs and assists him/her when necessary, locally compensating any problem related to specific disabilities. An ANOVA returned a p-value of 0.016, meaning that there is significant improvement in task performance when Collaborative Control is used.
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Wheelchair Collaborative Control for disabled users navigating indoors
Artificial Intelligence in Medicine, 2011Co-Authors: Cristina Urdiales, Manuel Fern??ndez-Carmona, Ulises Cort??s, Roberta Annichiaricco, Manuel Fernández-carmona, J. M. Peula, Ulises Cortés, Carlo Caltagirone, Francisco SandovalAbstract:Objective: Mobility is of key importance for autonomous living. Persons with severe disabilities may be assisted by robotic wheelchairs when manual Control is not possible. However, these persons should contribute to Control as much as they can to avoid loss of residual skills and frustration. Traditionally, wheelchair shared Control approaches either give Control to person or robot depending on the situation. Methods and materials: We propose a new shared Control technique where robot and person contribute simultaneously to Control. Their commands are weighted according to their respective local efficiencies and then combined via a reactive navigation strategy. Thus, assistance adapts to the user's needs. We refer to this approach as Collaborative Control. Results: Collaborative Control was tested in a home environment in Fondazione Santa Lucia (Rome) by 18 volunteers presenting different degrees of physical and cognitive disability. All of them successfully finished a complex test path with assistance. Both users and caregivers' opinion on the system was very positive. Acceptance was very good according to the psychosocial impact of assistive devices scale. Conclusions: Collaborative Control adapts to the person's needs and assists him/her when necessary, locally compensating any problem related to specific disabilities. An ANOVA returned a p-value of 0.016, meaning that there is significant improvement in task performance when Collaborative Control is used. ?? 2011 Elsevier B.V.
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Biometrically Modulated Collaborative Control for an Assistive Wheelchair
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2010Co-Authors: Cristina Urdiales, Francisco Sandoval, Blanca Fernandez-espejo, Roberta Annicchiaricco, Carlo CaltagironeAbstract:To operate a wheelchair, people with severe physical disabilities may require assistance, which can be provided by robotization. However, medical experts report that an excess of assistance may lead to loss of residual skills, so that it is important to provide just the right amount of assistance. This work proposes a Collaborative Control system based on weighting the robot's and the user's commands by their respective efficiency to reactively obtain an emergent Controller. Thus, the better the person operates, the more Control he/she gains. Tests with volunteers have proven, though, that some users may require extra assistance when they become stressed. Hence, we propose a Controller that can change the amount of support taking into account supplementary biometric data. In this work, we use an off-the-shelf wearable pulse oximeter. Experiments have demonstrated that volunteers could use our wheelchair in a more efficient way due to the proposed biometric modulated Collaborative Control.
Francisco Sandoval - One of the best experts on this subject based on the ideXlab platform.
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Wheelchair Collaborative Control for disabled users navigating indoors
Artificial Intelligence in Medicine, 2011Co-Authors: Cristina Urdiales, Roberta Annichiaricco, Manuel Fernández-carmona, J. M. Peula, Ulises Cortés, Carlo Caltagirone, Francisco SandovalAbstract:Objective: Mobility is of key importance for autonomous living. Persons with severe disabilities may be assisted by robotic wheelchairs when manual Control is not possible. However, these persons should contribute to Control as much as they can to avoid loss of residual skills and frustration. Traditionally, wheelchair shared Control approaches either give Control to person or robot depending on the situation. Methods and materials: We propose a new shared Control technique where robot and person contribute simultaneously to Control. Their commands are weighted according to their respective local efficiencies and then combined via a reactive navigation strategy. Thus, assistance adapts to the user's needs. We refer to this approach as Collaborative Control. Results: Collaborative Control was tested in a home environment in Fondazione Santa Lucia (Rome) by 18 volunteers presenting different degrees of physical and cognitive disability. All of them successfully finished a complex test path with assistance. Both users and caregivers' opinion on the system was very positive. Acceptance was very good according to the psychosocial impact of assistive devices scale. Conclusions: Collaborative Control adapts to the person's needs and assists him/her when necessary, locally compensating any problem related to specific disabilities. An ANOVA returned a p-value of 0.016, meaning that there is significant improvement in task performance when Collaborative Control is used.
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Wheelchair Collaborative Control for disabled users navigating indoors
Artificial Intelligence in Medicine, 2011Co-Authors: Cristina Urdiales, Manuel Fern??ndez-Carmona, Ulises Cort??s, Roberta Annichiaricco, Manuel Fernández-carmona, J. M. Peula, Ulises Cortés, Carlo Caltagirone, Francisco SandovalAbstract:Objective: Mobility is of key importance for autonomous living. Persons with severe disabilities may be assisted by robotic wheelchairs when manual Control is not possible. However, these persons should contribute to Control as much as they can to avoid loss of residual skills and frustration. Traditionally, wheelchair shared Control approaches either give Control to person or robot depending on the situation. Methods and materials: We propose a new shared Control technique where robot and person contribute simultaneously to Control. Their commands are weighted according to their respective local efficiencies and then combined via a reactive navigation strategy. Thus, assistance adapts to the user's needs. We refer to this approach as Collaborative Control. Results: Collaborative Control was tested in a home environment in Fondazione Santa Lucia (Rome) by 18 volunteers presenting different degrees of physical and cognitive disability. All of them successfully finished a complex test path with assistance. Both users and caregivers' opinion on the system was very positive. Acceptance was very good according to the psychosocial impact of assistive devices scale. Conclusions: Collaborative Control adapts to the person's needs and assists him/her when necessary, locally compensating any problem related to specific disabilities. An ANOVA returned a p-value of 0.016, meaning that there is significant improvement in task performance when Collaborative Control is used. ?? 2011 Elsevier B.V.
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Biometrically Modulated Collaborative Control for an Assistive Wheelchair
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2010Co-Authors: Cristina Urdiales, Francisco Sandoval, Blanca Fernandez-espejo, Roberta Annicchiaricco, Carlo CaltagironeAbstract:To operate a wheelchair, people with severe physical disabilities may require assistance, which can be provided by robotization. However, medical experts report that an excess of assistance may lead to loss of residual skills, so that it is important to provide just the right amount of assistance. This work proposes a Collaborative Control system based on weighting the robot's and the user's commands by their respective efficiency to reactively obtain an emergent Controller. Thus, the better the person operates, the more Control he/she gains. Tests with volunteers have proven, though, that some users may require extra assistance when they become stressed. Hence, we propose a Controller that can change the amount of support taking into account supplementary biometric data. In this work, we use an off-the-shelf wearable pulse oximeter. Experiments have demonstrated that volunteers could use our wheelchair in a more efficient way due to the proposed biometric modulated Collaborative Control.
Carlo Caltagirone - One of the best experts on this subject based on the ideXlab platform.
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Wheelchair Collaborative Control for disabled users navigating indoors
Artificial Intelligence in Medicine, 2011Co-Authors: Cristina Urdiales, Roberta Annichiaricco, Manuel Fernández-carmona, J. M. Peula, Ulises Cortés, Carlo Caltagirone, Francisco SandovalAbstract:Objective: Mobility is of key importance for autonomous living. Persons with severe disabilities may be assisted by robotic wheelchairs when manual Control is not possible. However, these persons should contribute to Control as much as they can to avoid loss of residual skills and frustration. Traditionally, wheelchair shared Control approaches either give Control to person or robot depending on the situation. Methods and materials: We propose a new shared Control technique where robot and person contribute simultaneously to Control. Their commands are weighted according to their respective local efficiencies and then combined via a reactive navigation strategy. Thus, assistance adapts to the user's needs. We refer to this approach as Collaborative Control. Results: Collaborative Control was tested in a home environment in Fondazione Santa Lucia (Rome) by 18 volunteers presenting different degrees of physical and cognitive disability. All of them successfully finished a complex test path with assistance. Both users and caregivers' opinion on the system was very positive. Acceptance was very good according to the psychosocial impact of assistive devices scale. Conclusions: Collaborative Control adapts to the person's needs and assists him/her when necessary, locally compensating any problem related to specific disabilities. An ANOVA returned a p-value of 0.016, meaning that there is significant improvement in task performance when Collaborative Control is used.
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Wheelchair Collaborative Control for disabled users navigating indoors
Artificial Intelligence in Medicine, 2011Co-Authors: Cristina Urdiales, Manuel Fern??ndez-Carmona, Ulises Cort??s, Roberta Annichiaricco, Manuel Fernández-carmona, J. M. Peula, Ulises Cortés, Carlo Caltagirone, Francisco SandovalAbstract:Objective: Mobility is of key importance for autonomous living. Persons with severe disabilities may be assisted by robotic wheelchairs when manual Control is not possible. However, these persons should contribute to Control as much as they can to avoid loss of residual skills and frustration. Traditionally, wheelchair shared Control approaches either give Control to person or robot depending on the situation. Methods and materials: We propose a new shared Control technique where robot and person contribute simultaneously to Control. Their commands are weighted according to their respective local efficiencies and then combined via a reactive navigation strategy. Thus, assistance adapts to the user's needs. We refer to this approach as Collaborative Control. Results: Collaborative Control was tested in a home environment in Fondazione Santa Lucia (Rome) by 18 volunteers presenting different degrees of physical and cognitive disability. All of them successfully finished a complex test path with assistance. Both users and caregivers' opinion on the system was very positive. Acceptance was very good according to the psychosocial impact of assistive devices scale. Conclusions: Collaborative Control adapts to the person's needs and assists him/her when necessary, locally compensating any problem related to specific disabilities. An ANOVA returned a p-value of 0.016, meaning that there is significant improvement in task performance when Collaborative Control is used. ?? 2011 Elsevier B.V.
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Biometrically Modulated Collaborative Control for an Assistive Wheelchair
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2010Co-Authors: Cristina Urdiales, Francisco Sandoval, Blanca Fernandez-espejo, Roberta Annicchiaricco, Carlo CaltagironeAbstract:To operate a wheelchair, people with severe physical disabilities may require assistance, which can be provided by robotization. However, medical experts report that an excess of assistance may lead to loss of residual skills, so that it is important to provide just the right amount of assistance. This work proposes a Collaborative Control system based on weighting the robot's and the user's commands by their respective efficiency to reactively obtain an emergent Controller. Thus, the better the person operates, the more Control he/she gains. Tests with volunteers have proven, though, that some users may require extra assistance when they become stressed. Hence, we propose a Controller that can change the amount of support taking into account supplementary biometric data. In this work, we use an off-the-shelf wearable pulse oximeter. Experiments have demonstrated that volunteers could use our wheelchair in a more efficient way due to the proposed biometric modulated Collaborative Control.
Dinei A. F. Florêncio - One of the best experts on this subject based on the ideXlab platform.
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IROS - A Collaborative Control system for telepresence robots
2012 IEEE RSJ International Conference on Intelligent Robots and Systems, 2012Co-Authors: Douglas Guimarães Macharet, Dinei A. F. FlorêncioAbstract:Interest in telepresence robots is at an all time high, and several companies are already commercializing early or basic versions. There seems to be a huge potential for their use in professional applications, where they can help address some of the challenges companies have found in integrating a geographically distributed work force. However, teleoperation of these robots is typically a difficult task. This difficulty can be attributed to limitations on the information provided to the operator and to communication delay and failures. This may compromise the safety of the people and of the robot during its navigation through the environment. Most commercial systems currently Control this risk by reducing size and weight of their robots. Research effort in addressing this problem is generally based on “assisted driving”, which typically adds a “collision avoidance” layer, limiting or avoiding movements that would lead to a collision. In this article, we bring assisted driving to a new level, by introducing concepts from Collaborative driving to telepresence robots. More specifically, we use the input from the operator as a general guidance to the target direction, then couple that with a variable degree of autonomy to the robot, depending on the task and the environment. Previous work has shown collision avoidance makes operation easier and reduce the number of collisions. In addition (and in contrast to traditional collision avoidance systems), our approach also reduces the time required to complete a circuit, making navigation easier, safer, and faster. The methodology was evaluated through a Controlled user study (N=18). Results show that the use of the proposed Collaborative Control helped reduce the number of collisions (none in most cases) and also decreased the time to complete the designated task.
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A Collaborative Control system for telepresence robots
IEEE International Conference on Intelligent Robots and Systems, 2012Co-Authors: Douglas Guimarães Macharet, Dinei A. F. FlorêncioAbstract:Interest in telepresence robots is at an all time high, and several companies are already commercializing early or basic versions. There seems to be a huge potential for their use in professional applications, where they can help address some of the challenges companies have found in integrating a geographically distributed work force. However, teleoperation of these robots is typically a difficult task. This difficulty can be attributed to limitations on the information provided to the operator and to communication delay and failures. This may compromise the safety of the people and of the robot during its navigation through the environment. Most commercial systems currently Control this risk by reducing size and weight of their robots. Research effort in addressing this problem is generally based on “assisted driving”, which typically adds a “collision avoidance” layer, limiting or avoiding movements that would lead to a collision. In this article, we bring assisted driving to a new level, by introducing concepts from Collaborative driving to telepresence robots. More specifically, we use the input from the operator as a general guidance to the target direction, then couple that with a variable degree of autonomy to the robot, depending on the task and the environment. Previous work has shown collision avoidance makes operation easier and reduce the number of collisions. In addition (and in contrast to traditional collision avoidance systems), our approach also reduces the time required to complete a circuit, making navigation easier, safer, and faster. The methodology was evaluated through a Controlled user study (N=18). Results show that the use of the proposed Collaborative Control helped reduce the number of collisions (none in most cases) and also decreased the time to complete the designated task.
Yang Xiao - One of the best experts on this subject based on the ideXlab platform.
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distributed Collaborative Control for industrial automation with wireless sensor and actuator networks
IEEE Transactions on Industrial Electronics, 2010Co-Authors: Jiming Chen, Peng Cheng, Yang XiaoAbstract:Wireless sensor and actuator networks (WSANs) bring many benefits to industrial automation systems. When a Control system is integrated by a WSAN, and particularly if the network scale is large, distributed communication and Control methods are quite necessary. However, unreliable wireless and multihop communications among sensors and actuators cause challenges in designing such systems. This paper proposes and evaluates a new distributed estimation and Collaborative Control scheme for industrial Control systems with WSANs. Extensive results show that the proposed method effectively achieves Control objectives and maintains robust against inaccurate system parameters. We also discuss how to dynamically extend the scale of a WSAN with only local adjustments of sensors and actuators.
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FGCN (1) - Distributed Collaborative Control Using Wireless Sensor and Actuator Networks
2008 Second International Conference on Future Generation Communication and Networking, 2008Co-Authors: Jiming Chen, Yang XiaoAbstract:Wireless sensor and actuator networks (WSANs) in which actuators perform actuation based on sensory information exhibit great potential in environment Control, building automation, agriculture, etc. In this paper, we focus on the problem of designing Control methods for actuators to Control the environment and meet user requirements based only on local sensory information. A novel distributed Collaborative Control strategy is proposed and shows good performance under system noises and communication unreliability.
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Distributed Collaborative Control Using Wireless Sensor and Actuator Networks
2008 Second International Conference on Future Generation Communication and Networking, 2008Co-Authors: Jiming Chen, Yang XiaoAbstract:Wireless sensor and actuator networks (WSANs) in which actuators perform actuation based on sensory information exhibit great potential in environment Control, building automation, agriculture, etc. In this paper, we focus on the problem of designing Control methods for actuators to Control the environment and meet user requirements based only on local sensory information. A novel distributed Collaborative Control strategy is proposed and shows good performance under system noises and communication unreliability.