Pub Date : 2017-11-01DOI: 10.1109/HUMANOIDS.2017.8246953
M. Ciocca, Pierre-Brice Wieber, Thierry Fraichard
Realizing a stable walking motion requires satisfying a set of constraints. Model Predictive Control (MPC) is one of few suitable methods to handle such constraints. The capacity to satisfy constraints, which is usually called feasibility, is classically guaranteed recursively. In our applications, an important aspect is that the MPC scheme has to adapt continuously to the dynamic environment of the robot (e.g. collision avoidance, physical interaction). We aim therefore at guaranteeing recursive feasibility for all possible scenarios, which is called strong recursive feasibility. Recursive feasibility is classically obtained by introducing a terminal constraint at the end of the prediction horizon. Between two standard approaches for legged robot, in our applications we favor a capturable terminal constraint. When the robot is not really planning to stop and considers actually making a new step, recursive feasibility is not guaranteed anymore. We demonstrate numerically that recursive feasibility is actually guaranteed, even when a new step is added in the prediction horizon.
{"title":"Strong recursive feasibility in model predictive control of biped walking","authors":"M. Ciocca, Pierre-Brice Wieber, Thierry Fraichard","doi":"10.1109/HUMANOIDS.2017.8246953","DOIUrl":"https://doi.org/10.1109/HUMANOIDS.2017.8246953","url":null,"abstract":"Realizing a stable walking motion requires satisfying a set of constraints. Model Predictive Control (MPC) is one of few suitable methods to handle such constraints. The capacity to satisfy constraints, which is usually called feasibility, is classically guaranteed recursively. In our applications, an important aspect is that the MPC scheme has to adapt continuously to the dynamic environment of the robot (e.g. collision avoidance, physical interaction). We aim therefore at guaranteeing recursive feasibility for all possible scenarios, which is called strong recursive feasibility. Recursive feasibility is classically obtained by introducing a terminal constraint at the end of the prediction horizon. Between two standard approaches for legged robot, in our applications we favor a capturable terminal constraint. When the robot is not really planning to stop and considers actually making a new step, recursive feasibility is not guaranteed anymore. We demonstrate numerically that recursive feasibility is actually guaranteed, even when a new step is added in the prediction horizon.","PeriodicalId":143992,"journal":{"name":"2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122778396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study presents a novel passive wrist joint with push-activate-rotation (PAR) functionality for a peg-in-hole assembly and a strategy utilizing the passivity and inherent functionality of the wrist. The PAR function involves rotation around a vertical axis, automatically activated by pushing the wrist in the vertical direction. The novel features of the approach are that 1)hardware based passive compliance absorbs uncertainties of the states for the peg, 2) no active rotational joints are required for the manipulator part, 3) F/T sensors are not used; instead, an IMU sensor is, and 4) multiple shapes for the peg or hole are available e.g., rectangular, circular, hexagonal, and triangular prisms. The validity of the approach is shown via several experiments.
{"title":"Peg-in-hole under state uncertainties via a passive wrist joint with push-activate-rotation function","authors":"Toshihiro Nishimura, Yosuke Suzuki, Tokuo Tsuji, Tetsuyou Watanabe","doi":"10.1109/HUMANOIDS.2017.8239539","DOIUrl":"https://doi.org/10.1109/HUMANOIDS.2017.8239539","url":null,"abstract":"This study presents a novel passive wrist joint with push-activate-rotation (PAR) functionality for a peg-in-hole assembly and a strategy utilizing the passivity and inherent functionality of the wrist. The PAR function involves rotation around a vertical axis, automatically activated by pushing the wrist in the vertical direction. The novel features of the approach are that 1)hardware based passive compliance absorbs uncertainties of the states for the peg, 2) no active rotational joints are required for the manipulator part, 3) F/T sensors are not used; instead, an IMU sensor is, and 4) multiple shapes for the peg or hole are available e.g., rectangular, circular, hexagonal, and triangular prisms. The validity of the approach is shown via several experiments.","PeriodicalId":143992,"journal":{"name":"2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126310435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/HUMANOIDS.2017.8246966
Chan Lee, Jinoh Lee, J. Malzahn, N. Tsagarakis, Sehoon Oh
Collaborative robots driven by Series Elastic Actuators (SEA) exploit physical compliance, which enables joint torque sensing to accurately estimate external contact torques based on residual momenta. The deflection measurement precision for the compliant element determines the torque accuracy. It is affected by support clearances, assembly tolerances, stiffness calibration errors as well as unmodelled non-linearity. Moreover, the stiffness value amplifies deflection errors leading to larger torque estimation errors, which limits the benefits of integrated joint torque sensing for enhanced external torque estimation with higher stiffness SEAs. Accordingly, this paper newly proposes a two-staged approach of the residual based external torque estimation for SEA driven robots. The proposed method augments the residual calculation by the motor-side dynamics, uses both, motor and link side angular measurements, but entirely waives the need to model the spring dynamics for the external torque estimation. Consequently, this improves accuracy and sensitivity in wide-ranging stiffness applications. The performance of the two-staged residual is verified by experiments with the SEA of the WALK-MAN humanoid robot.
{"title":"A two-staged residual for resilient external torque estimation with series elastic actuators","authors":"Chan Lee, Jinoh Lee, J. Malzahn, N. Tsagarakis, Sehoon Oh","doi":"10.1109/HUMANOIDS.2017.8246966","DOIUrl":"https://doi.org/10.1109/HUMANOIDS.2017.8246966","url":null,"abstract":"Collaborative robots driven by Series Elastic Actuators (SEA) exploit physical compliance, which enables joint torque sensing to accurately estimate external contact torques based on residual momenta. The deflection measurement precision for the compliant element determines the torque accuracy. It is affected by support clearances, assembly tolerances, stiffness calibration errors as well as unmodelled non-linearity. Moreover, the stiffness value amplifies deflection errors leading to larger torque estimation errors, which limits the benefits of integrated joint torque sensing for enhanced external torque estimation with higher stiffness SEAs. Accordingly, this paper newly proposes a two-staged approach of the residual based external torque estimation for SEA driven robots. The proposed method augments the residual calculation by the motor-side dynamics, uses both, motor and link side angular measurements, but entirely waives the need to model the spring dynamics for the external torque estimation. Consequently, this improves accuracy and sensitivity in wide-ranging stiffness applications. The performance of the two-staged residual is verified by experiments with the SEA of the WALK-MAN humanoid robot.","PeriodicalId":143992,"journal":{"name":"2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128514507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/HUMANOIDS.2017.8246903
Qingxiaoyang Zhu, Vittorio Perera, Mirko Wächter, T. Asfour, M. Veloso
The progress in humanoid robotics research has led to robots that are able to perform complex tasks with a certain level of autonomy by integrating perception, action, planning, and learning capabilities. However, robot capabilities are still limited in regard to how they externalize their internal state and world state, i.e. their sensorimotor experience, and how they explain which tasks they performed and how they performed these tasks. In other words, their capability in conveying information to the user in a way similar to what humans do is limited. To this end, we present a verbalization system that generates natural language explanations of the robot's past navigation and manipulation experience. We propose a threelayered model to represent robot experience which doubles as a retrievable episodic memory. Through the memory system, the robot can select a matching experience given a user query. In order to generate flexible narrations, we use verbalization parameters to capture user preferences. We show that our verbalization algorithm is capable of producing appropriate results based on these verbalization parameters. The proposed verbalization system is able to generate explanations for navigation as well as grasping and manipulation tasks. The resulting system is evaluated in a pick-and-place kitchen scenario.
{"title":"Autonomous narration of humanoid robot kitchen task experience","authors":"Qingxiaoyang Zhu, Vittorio Perera, Mirko Wächter, T. Asfour, M. Veloso","doi":"10.1109/HUMANOIDS.2017.8246903","DOIUrl":"https://doi.org/10.1109/HUMANOIDS.2017.8246903","url":null,"abstract":"The progress in humanoid robotics research has led to robots that are able to perform complex tasks with a certain level of autonomy by integrating perception, action, planning, and learning capabilities. However, robot capabilities are still limited in regard to how they externalize their internal state and world state, i.e. their sensorimotor experience, and how they explain which tasks they performed and how they performed these tasks. In other words, their capability in conveying information to the user in a way similar to what humans do is limited. To this end, we present a verbalization system that generates natural language explanations of the robot's past navigation and manipulation experience. We propose a threelayered model to represent robot experience which doubles as a retrievable episodic memory. Through the memory system, the robot can select a matching experience given a user query. In order to generate flexible narrations, we use verbalization parameters to capture user preferences. We show that our verbalization algorithm is capable of producing appropriate results based on these verbalization parameters. The proposed verbalization system is able to generate explanations for navigation as well as grasping and manipulation tasks. The resulting system is evaluated in a pick-and-place kitchen scenario.","PeriodicalId":143992,"journal":{"name":"2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132351599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/HUMANOIDS.2017.8246941
Nicole Poltorak, A. Drimus
This paper addresses the use of convolutional neural networks for image analysis resulting in an engagement metric that can be used to assess the quality of human robot interactions. We propose a method based on a pretrained convolutional network able to map emotions onto a continuous [0-1] interval, where 0 represents disengaged and 1 fully engaged. The network shows a good accuracy at recognizing the engagement state of humans given positive emotions. A time based analysis of interaction experiments between small humanoid robots and humans provides time series of engagement estimates, which are further used to understand the nature of the interaction as well as the overall mood and interest of the participant during the experiment. The method allows a real-time implementation and supports a quantitative and qualitative assessment of a human robot interaction with respect to a positive engagement and is applicable to humanoid robotics as well as other related contexts.
{"title":"Human-robot interaction assessment using dynamic engagement profiles","authors":"Nicole Poltorak, A. Drimus","doi":"10.1109/HUMANOIDS.2017.8246941","DOIUrl":"https://doi.org/10.1109/HUMANOIDS.2017.8246941","url":null,"abstract":"This paper addresses the use of convolutional neural networks for image analysis resulting in an engagement metric that can be used to assess the quality of human robot interactions. We propose a method based on a pretrained convolutional network able to map emotions onto a continuous [0-1] interval, where 0 represents disengaged and 1 fully engaged. The network shows a good accuracy at recognizing the engagement state of humans given positive emotions. A time based analysis of interaction experiments between small humanoid robots and humans provides time series of engagement estimates, which are further used to understand the nature of the interaction as well as the overall mood and interest of the participant during the experiment. The method allows a real-time implementation and supports a quantitative and qualitative assessment of a human robot interaction with respect to a positive engagement and is applicable to humanoid robotics as well as other related contexts.","PeriodicalId":143992,"journal":{"name":"2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124382120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/HUMANOIDS.2017.8246888
Steffen Schütz, A. Nejadfard, K. Mianowski, Patrick Vonwirth, K. Berns
An increasing number of biologically-inspired control approaches for bipedal walking are demonstrating impressive capabilities in simulations. Although many of them share the main assumptions about the simulated biped, so far no physical system has been presented that meets the requirements. Thus, in this paper we are presenting the Compliant Robotic Leg (Carl) — a fully actuated bio-inspired planar robotic leg that incorporates mono-as well as biarticular actuation. Its development is motivated by the vision to validate the Bioinspired Behavior Based Bipedal Locomotion Control (B4lc) on a physical platform. This paper provides an overview of the driving requirements for the different subsystems and the derived implementation. Two experiments were performed to proof the functionality of the system. Specifically, some key requirements — transparency, impact tolerance, force and impedance rendering — are demonstrated. To our knowledge, this is the first fully actuated robotic leg including biarticular elements that demonstrates distributed high-fidelity force and impedance control at the actuator level.
{"title":"Carl — A compliant robotic leg featuring mono- and biarticular actuation","authors":"Steffen Schütz, A. Nejadfard, K. Mianowski, Patrick Vonwirth, K. Berns","doi":"10.1109/HUMANOIDS.2017.8246888","DOIUrl":"https://doi.org/10.1109/HUMANOIDS.2017.8246888","url":null,"abstract":"An increasing number of biologically-inspired control approaches for bipedal walking are demonstrating impressive capabilities in simulations. Although many of them share the main assumptions about the simulated biped, so far no physical system has been presented that meets the requirements. Thus, in this paper we are presenting the Compliant Robotic Leg (Carl) — a fully actuated bio-inspired planar robotic leg that incorporates mono-as well as biarticular actuation. Its development is motivated by the vision to validate the Bioinspired Behavior Based Bipedal Locomotion Control (B4lc) on a physical platform. This paper provides an overview of the driving requirements for the different subsystems and the derived implementation. Two experiments were performed to proof the functionality of the system. Specifically, some key requirements — transparency, impact tolerance, force and impedance rendering — are demonstrated. To our knowledge, this is the first fully actuated robotic leg including biarticular elements that demonstrates distributed high-fidelity force and impedance control at the actuator level.","PeriodicalId":143992,"journal":{"name":"2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121475467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/HUMANOIDS.2017.8246946
Iori Kumagai, Fumihito Sugai, Shunichi Nozawa, Youhei Kakiuchi, K. Okada, M. Inaba, F. Kanehiro
A robot system that can process environmental measurements and motion planning during locomotion is necessary to continuously perform various tasks. To achieve such a system, which we call the Perception-during-Traversing Model, the accuracy of environmental recognition must be improved and computational costs must be reduced; these are tradeoff relationships. In this paper, we propose a construction framework for a humanoid robot to solve the trade-off problems and achieve the Perception-during-Traversing Model system. The key idea of the proposed framework is subdividing and re-integrating the localization and recognition processes in a complementary manner based on task-oriented frequency and accuracy requirements. Moreover, we apply our framework to the humanoid robot JAXON, and demonstrate that it can execute various tasks continuously by the Perception-during-Traversing Model. The most important contribution of our framework is enabling the humanoid robot to localize itself accurately and measure the environment densely enough to execute tasks using its on-board computers; this provides a practical solution to the trade-off between recognition quality and computational costs.
{"title":"Complementary integration framework for localization and recognition of a humanoid robot based on task-oriented frequency and accuracy requirements","authors":"Iori Kumagai, Fumihito Sugai, Shunichi Nozawa, Youhei Kakiuchi, K. Okada, M. Inaba, F. Kanehiro","doi":"10.1109/HUMANOIDS.2017.8246946","DOIUrl":"https://doi.org/10.1109/HUMANOIDS.2017.8246946","url":null,"abstract":"A robot system that can process environmental measurements and motion planning during locomotion is necessary to continuously perform various tasks. To achieve such a system, which we call the Perception-during-Traversing Model, the accuracy of environmental recognition must be improved and computational costs must be reduced; these are tradeoff relationships. In this paper, we propose a construction framework for a humanoid robot to solve the trade-off problems and achieve the Perception-during-Traversing Model system. The key idea of the proposed framework is subdividing and re-integrating the localization and recognition processes in a complementary manner based on task-oriented frequency and accuracy requirements. Moreover, we apply our framework to the humanoid robot JAXON, and demonstrate that it can execute various tasks continuously by the Perception-during-Traversing Model. The most important contribution of our framework is enabling the humanoid robot to localize itself accurately and measure the environment densely enough to execute tasks using its on-board computers; this provides a practical solution to the trade-off between recognition quality and computational costs.","PeriodicalId":143992,"journal":{"name":"2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116366079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/HUMANOIDS.2017.8239554
Ahmed Aboudonia, Nicola Scianca, D. Simone, L. Lanari, G. Oriolo
We consider the problem of gait generation for a humanoid robot that must walk to an assigned Cartesian goal. As a first solution, we consider a rather straightforward adaptation of our previous work: an external block produces high-level velocities, which are then tracked by a double-stage intrinsically stable MPC scheme where the orientation of the footsteps is chosen before determining their location and the CoM trajectory. The second solution, which represents the main contribution of the paper, is conceptually different: no high-level velocity is generated, and footstep orientations are chosen at the same time of the other decision variables in a singlestage MPC. This is made possible by carefully redesigning the motion constraints so as to preserve linearity. Preliminary results on a simulated NAO confirm that the single-stage method outperforms the conventional double-stage scheme.
{"title":"Humanoid gait generation for walk-to locomotion using single-stage MPC","authors":"Ahmed Aboudonia, Nicola Scianca, D. Simone, L. Lanari, G. Oriolo","doi":"10.1109/HUMANOIDS.2017.8239554","DOIUrl":"https://doi.org/10.1109/HUMANOIDS.2017.8239554","url":null,"abstract":"We consider the problem of gait generation for a humanoid robot that must walk to an assigned Cartesian goal. As a first solution, we consider a rather straightforward adaptation of our previous work: an external block produces high-level velocities, which are then tracked by a double-stage intrinsically stable MPC scheme where the orientation of the footsteps is chosen before determining their location and the CoM trajectory. The second solution, which represents the main contribution of the paper, is conceptually different: no high-level velocity is generated, and footstep orientations are chosen at the same time of the other decision variables in a singlestage MPC. This is made possible by carefully redesigning the motion constraints so as to preserve linearity. Preliminary results on a simulated NAO confirm that the single-stage method outperforms the conventional double-stage scheme.","PeriodicalId":143992,"journal":{"name":"2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123260259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/HUMANOIDS.2017.8246919
Takahiro Ito, Ko Ayusawa, E. Yoshida, Hiroshi Kobayashi
In a super-aged society such as Japan, wearable assistive devices that aim at reducing caregiver burden as well as improving the autonomy of the elderly are attracting strong interests. Humanoid robots can be used to evaluate the supportive effects of assistive devices by measuring joint torques, as that cannot be directly obtained from human subjects. While our previous work proposed a humanoid-based method for estimating static supportive torques of powerful and active supportive devices, this paper proposes a novel framework for evaluating their supportive effects during dynamic motion. Assuming that humans move with minimum exertion when taking full advantage of a device's assistive power, we propose using a controller on a humanoid to track retargeted human motions during power assistance. The effectiveness of the proposed method has been validated by experimentally assessing an assistive device (Muscle Suit) actuated by pneumatic artificial muscles.
{"title":"Human motion reproduction by torque-based humanoid tracking control for active assistive device evaluation","authors":"Takahiro Ito, Ko Ayusawa, E. Yoshida, Hiroshi Kobayashi","doi":"10.1109/HUMANOIDS.2017.8246919","DOIUrl":"https://doi.org/10.1109/HUMANOIDS.2017.8246919","url":null,"abstract":"In a super-aged society such as Japan, wearable assistive devices that aim at reducing caregiver burden as well as improving the autonomy of the elderly are attracting strong interests. Humanoid robots can be used to evaluate the supportive effects of assistive devices by measuring joint torques, as that cannot be directly obtained from human subjects. While our previous work proposed a humanoid-based method for estimating static supportive torques of powerful and active supportive devices, this paper proposes a novel framework for evaluating their supportive effects during dynamic motion. Assuming that humans move with minimum exertion when taking full advantage of a device's assistive power, we propose using a controller on a humanoid to track retargeted human motions during power assistance. The effectiveness of the proposed method has been validated by experimentally assessing an assistive device (Muscle Suit) actuated by pneumatic artificial muscles.","PeriodicalId":143992,"journal":{"name":"2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123214956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-11-01DOI: 10.1109/HUMANOIDS.2017.8246876
J. S. Terry, Levi Rupert, Marc D. Killpack
When using model predictive control (MPC) to perform low-level control of humanoid robot manipulators, computational tractability can be a limiting factor. This is because using complex models can have a negative impact on control performance, especially as the number of degrees of freedom increases. In an effort to address this issue, we compare three different methods for linearizing the dynamics of a robot arm for MPC. The methods we compare are a Taylor Series approximation method (TS), a Fixed-State approximation method (FS), and a Coupling-Torque approximation method (CT). In simulation we compare the relative control performance when these models are used with MPC. Through these comparisons we show that the CT approximation method is best for reducing model complexity without reducing the performance of MPC. We also demonstrate the CT approximation method on two real robots, a robot with series elastic actuators and a soft, inflatable robot.
{"title":"Comparison of linearized dynamic robot manipulator models for model predictive control","authors":"J. S. Terry, Levi Rupert, Marc D. Killpack","doi":"10.1109/HUMANOIDS.2017.8246876","DOIUrl":"https://doi.org/10.1109/HUMANOIDS.2017.8246876","url":null,"abstract":"When using model predictive control (MPC) to perform low-level control of humanoid robot manipulators, computational tractability can be a limiting factor. This is because using complex models can have a negative impact on control performance, especially as the number of degrees of freedom increases. In an effort to address this issue, we compare three different methods for linearizing the dynamics of a robot arm for MPC. The methods we compare are a Taylor Series approximation method (TS), a Fixed-State approximation method (FS), and a Coupling-Torque approximation method (CT). In simulation we compare the relative control performance when these models are used with MPC. Through these comparisons we show that the CT approximation method is best for reducing model complexity without reducing the performance of MPC. We also demonstrate the CT approximation method on two real robots, a robot with series elastic actuators and a soft, inflatable robot.","PeriodicalId":143992,"journal":{"name":"2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128044883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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