Articles de revista
http://hdl.handle.net/2117/2253
20171017T20:53:25Z

Trajectory generation for unmanned aerial manipulators through quadratic programming
http://hdl.handle.net/2117/108714
Trajectory generation for unmanned aerial manipulators through quadratic programming
Rossi, Roberto; Santamaria Navarro, Àngel; AndradeCetto, Juan; Rocco, Paolo
In this paper a trajectory generation approach using quadratic programming is described for aerial manipulation, i.e. for the control of an aerial vehicle equipped with a robot arm. The proposed approach applies the online active set strategy to generate a feasible trajectory of the joints, in order to accomplish a set of tasks with defined bounds and constraint inequalities. The definition of the problem in the acceleration domain allows to integrate and perform a large set of tasks and, as a result, to obtain smooth motion of the joints. A weighting strategy, associated with a normalization procedure, allows to easily define the relative importance of the tasks. This approach is useful to accomplish different phases of a mission with different redundancy resolution strategies. The performance of the proposed technique is demonstrated through real experiments with all the algorithms running onboard in real time. In particular, the aerial manipulator can successfully perform navigation and interaction phases, while keeping motion within prescribed bounds and avoiding collisions with external obstacles.
© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
20171016T10:26:30Z
Rossi, Roberto
Santamaria Navarro, Àngel
AndradeCetto, Juan
Rocco, Paolo
In this paper a trajectory generation approach using quadratic programming is described for aerial manipulation, i.e. for the control of an aerial vehicle equipped with a robot arm. The proposed approach applies the online active set strategy to generate a feasible trajectory of the joints, in order to accomplish a set of tasks with defined bounds and constraint inequalities. The definition of the problem in the acceleration domain allows to integrate and perform a large set of tasks and, as a result, to obtain smooth motion of the joints. A weighting strategy, associated with a normalization procedure, allows to easily define the relative importance of the tasks. This approach is useful to accomplish different phases of a mission with different redundancy resolution strategies. The performance of the proposed technique is demonstrated through real experiments with all the algorithms running onboard in real time. In particular, the aerial manipulator can successfully perform navigation and interaction phases, while keeping motion within prescribed bounds and avoiding collisions with external obstacles.

Efficient interactive decisionmaking framework for robotic applications
http://hdl.handle.net/2117/108534
Efficient interactive decisionmaking framework for robotic applications
Agostini, Alejandro Gabriel; Torras, Carme; Woergoetter, Florentin
The inclusion of robots in our society is imminent, such as service robots. Robots are now capable of reliably manipulating objects in our daily lives but only when combined with artificial intelligence (AI) techniques for planning and decisionmaking, which allow a machine to determine how a task can be completed successfully. To perform decision making, AI planning methods use a set of planning operators to code the state changes in the environment produced by a robotic action. Given a specific goal, the planner then searches for the best sequence of planning operators, i.e., the best plan that leads through the state space to satisfy the goal. In principle, planning operators can be handcoded, but this is impractical for applications that involve many possible state transitions. An alternative is to learn them automatically from experience, which is most efficient when there is a human teacher. In this study, we propose a simple and efficient decisionmaking framework for this purpose. The robot executes its plan in a stepwise manner and any planning impasse produced by missing operators is resolved online by asking a human teacher for the next action to execute. Based on the observed state transitions, this approach rapidly generates the missing operators by evaluating the relevance of several cause–effect alternatives in parallel using a probability estimate, which compensates for the high uncertainty that is inherent when learning from a small number of samples. We evaluated the validity of our approach in simulated and real environments, where it was benchmarked against previous methods. Humans learn in the same incremental manner, so we consider that our approach may be a better alternative to existing learning paradigms, which require offline learning, a significant amount of previous knowledge, or a large number of samples.
© <year>. This manuscript version is made available under the CCBYNCND 4.0 license http://creativecommons.org/licenses/byncnd/4.0/
20171009T14:32:08Z
Agostini, Alejandro Gabriel
Torras, Carme
Woergoetter, Florentin
The inclusion of robots in our society is imminent, such as service robots. Robots are now capable of reliably manipulating objects in our daily lives but only when combined with artificial intelligence (AI) techniques for planning and decisionmaking, which allow a machine to determine how a task can be completed successfully. To perform decision making, AI planning methods use a set of planning operators to code the state changes in the environment produced by a robotic action. Given a specific goal, the planner then searches for the best sequence of planning operators, i.e., the best plan that leads through the state space to satisfy the goal. In principle, planning operators can be handcoded, but this is impractical for applications that involve many possible state transitions. An alternative is to learn them automatically from experience, which is most efficient when there is a human teacher. In this study, we propose a simple and efficient decisionmaking framework for this purpose. The robot executes its plan in a stepwise manner and any planning impasse produced by missing operators is resolved online by asking a human teacher for the next action to execute. Based on the observed state transitions, this approach rapidly generates the missing operators by evaluating the relevance of several cause–effect alternatives in parallel using a probability estimate, which compensates for the high uncertainty that is inherent when learning from a small number of samples. We evaluated the validity of our approach in simulated and real environments, where it was benchmarked against previous methods. Humans learn in the same incremental manner, so we consider that our approach may be a better alternative to existing learning paradigms, which require offline learning, a significant amount of previous knowledge, or a large number of samples.

Combining localphysical and globalstatistical models for sequential deformable shape from motion
http://hdl.handle.net/2117/108518
Combining localphysical and globalstatistical models for sequential deformable shape from motion
Agudo Martínez, Antonio; MorenoNoguer, Francesc
In this paper, we simultaneously estimate camera pose and nonrigid 3D shape from a monocular video, using a sequential solution that combines local and global representations. We model the object as an ensemble of particles, each ruled by the linear equation of the Newton's second law of motion. This dynamic model is incorporated into a bundle adjustment framework, in combination with simple regularization components that ensure temporal and spatial consistency. The resulting approach allows to sequentially estimate shape and camera poses, while progressively learning a global lowrank model of the shape that is fed back into the optimization scheme, introducing thus, global constraints. The overall combination of local (physical) and global (statistical) constraints yields a solution that is both efficient and robust to several artifacts such as noisy and missing data or sudden camera motions, without requiring any training data at all. Validation is done in a variety of real application domains, including articulated and nonrigid motion, both for continuous and discontinuous shapes. Our online methodology yields significantly more accurate reconstructions than competing sequential approaches, being even comparable to the more computationally demanding batch methods.
The final publication is available at link.springer.com
20171009T11:25:13Z
Agudo Martínez, Antonio
MorenoNoguer, Francesc
In this paper, we simultaneously estimate camera pose and nonrigid 3D shape from a monocular video, using a sequential solution that combines local and global representations. We model the object as an ensemble of particles, each ruled by the linear equation of the Newton's second law of motion. This dynamic model is incorporated into a bundle adjustment framework, in combination with simple regularization components that ensure temporal and spatial consistency. The resulting approach allows to sequentially estimate shape and camera poses, while progressively learning a global lowrank model of the shape that is fed back into the optimization scheme, introducing thus, global constraints. The overall combination of local (physical) and global (statistical) constraints yields a solution that is both efficient and robust to several artifacts such as noisy and missing data or sudden camera motions, without requiring any training data at all. Validation is done in a variety of real application domains, including articulated and nonrigid motion, both for continuous and discontinuous shapes. Our online methodology yields significantly more accurate reconstructions than competing sequential approaches, being even comparable to the more computationally demanding batch methods.

Nonlinear economic model predictive control of water distribution networks
http://hdl.handle.net/2117/108514
Nonlinear economic model predictive control of water distribution networks
Wang, Ye; Puig Cayuela, Vicenç; Cembrano Gennari, Gabriela
This paper addresses a nonlinear economic model predictive control (EMPC) strategy for water distribution networks (WDNs). A WDN could be considered as a nonlinear system described by differentialalgebraic equations (DAEs) when flow and hydraulic head equations are considered. As in other process industries, the main operational goal of WDNs is the minimisation of the economic costs associated to pumping and water treatment, while guaranteeing water supply with required flows and pressures at all the control/demand nodes in the network. Other operational goals related to safety and reliability are usually sought. From a control point of view, EMPC is a suitable control strategy for WDNs since the optimal operation of the network cannot be established a priori by fixing reference volumes in the tanks. Alternatively, the EMPC strategy should determine the optimal filling/emptying sequence of the tanks taking into account that electricity price varies between day and night and that the demand also follows a 24hour repetitive pattern. On the other hand, as a result of the ON/OFF operation of parallel pumps in pumping stations, a twolayer control scheme has been used: a nonlinear EMPC strategy with hourly control interval is chosen in the upper layer and a pump scheduling approach with oneminute sampling time in the lower layer. Finally, closedloop simulation results of applying the proposed control strategy to the DTown water network are shown.
© <year>. This manuscript version is made available under the CCBYNCND 4.0 license http://creativecommons.org/licenses/byncnd/4.0/
20171009T10:33:50Z
Wang, Ye
Puig Cayuela, Vicenç
Cembrano Gennari, Gabriela
This paper addresses a nonlinear economic model predictive control (EMPC) strategy for water distribution networks (WDNs). A WDN could be considered as a nonlinear system described by differentialalgebraic equations (DAEs) when flow and hydraulic head equations are considered. As in other process industries, the main operational goal of WDNs is the minimisation of the economic costs associated to pumping and water treatment, while guaranteeing water supply with required flows and pressures at all the control/demand nodes in the network. Other operational goals related to safety and reliability are usually sought. From a control point of view, EMPC is a suitable control strategy for WDNs since the optimal operation of the network cannot be established a priori by fixing reference volumes in the tanks. Alternatively, the EMPC strategy should determine the optimal filling/emptying sequence of the tanks taking into account that electricity price varies between day and night and that the demand also follows a 24hour repetitive pattern. On the other hand, as a result of the ON/OFF operation of parallel pumps in pumping stations, a twolayer control scheme has been used: a nonlinear EMPC strategy with hourly control interval is chosen in the upper layer and a pump scheduling approach with oneminute sampling time in the lower layer. Finally, closedloop simulation results of applying the proposed control strategy to the DTown water network are shown.

Robot socialaware navigation framework to accompany people walking sidebyside
http://hdl.handle.net/2117/108289
Robot socialaware navigation framework to accompany people walking sidebyside
Ferrer, Gonzalo; Garrell Zulueta, Anais; Herrero Cotarelo, Fernando; Sanfeliu Cortés, Alberto
We present a novel robot socialaware navigation framework to walk sidebyside with people in crowded urban areas in a safety and natural way. The new system includes the following key issues: to propose a new robot socialaware navigation model to accompany a person; to extend the Social Force Model,
The final publication is available at link.springer.com
20171002T14:07:58Z
Ferrer, Gonzalo
Garrell Zulueta, Anais
Herrero Cotarelo, Fernando
Sanfeliu Cortés, Alberto
We present a novel robot socialaware navigation framework to walk sidebyside with people in crowded urban areas in a safety and natural way. The new system includes the following key issues: to propose a new robot socialaware navigation model to accompany a person; to extend the Social Force Model,

Modal space: a physicsbased model for sequential estimation of timevarying shape from monocular video
http://hdl.handle.net/2117/108275
Modal space: a physicsbased model for sequential estimation of timevarying shape from monocular video
Agudo Martínez, Antonio; Martinez Montiel, José Maria; Agapito, Lourdes; Calvo, Begoña
This paper describes two sequential methods for recovering the camera pose together with the 3D shape of highly deformable surfaces from a monocular video. The nonrigid 3D shape is modeled as a linear combination of mode shapes with timevarying weights that define the shape at each frame and are estimated onthefly. The lowrank constraint is combined with standard smoothness priors to optimize the model parameters over a sliding window of image frames. We propose to obtain a physicsbased shape basis using the initial frames on the video to code the timevarying shape along the sequence, reducing the problem from trilinear to bilinear. To this end, the 3D shape is discretized by means of a soup of elastic triangular finite elements where we apply a force balance equation. This equation is solved using modal analysis via a simple eigenvalue problem to obtain a shape basis that encodes the modes of deformation. Even though this strategy can be applied in a wide variety of scenarios, when the observations are denser, the solution can become prohibitive in terms of computational load. We avoid this limitation by proposing two efficient coarsetofine approaches that allow us to easily deal with dense 3D surfaces. This results in a scalable solution that estimates a small number of parameters per frame and could potentially run in real time. We show results on both synthetic and real videos with ground truth 3D data, while robustly dealing with artifacts such as noise and missing data.
The final publication is available at link.springer.com
20171002T12:56:11Z
Agudo Martínez, Antonio
Martinez Montiel, José Maria
Agapito, Lourdes
Calvo, Begoña
This paper describes two sequential methods for recovering the camera pose together with the 3D shape of highly deformable surfaces from a monocular video. The nonrigid 3D shape is modeled as a linear combination of mode shapes with timevarying weights that define the shape at each frame and are estimated onthefly. The lowrank constraint is combined with standard smoothness priors to optimize the model parameters over a sliding window of image frames. We propose to obtain a physicsbased shape basis using the initial frames on the video to code the timevarying shape along the sequence, reducing the problem from trilinear to bilinear. To this end, the 3D shape is discretized by means of a soup of elastic triangular finite elements where we apply a force balance equation. This equation is solved using modal analysis via a simple eigenvalue problem to obtain a shape basis that encodes the modes of deformation. Even though this strategy can be applied in a wide variety of scenarios, when the observations are denser, the solution can become prohibitive in terms of computational load. We avoid this limitation by proposing two efficient coarsetofine approaches that allow us to easily deal with dense 3D surfaces. This results in a scalable solution that estimates a small number of parameters per frame and could potentially run in real time. We show results on both synthetic and real videos with ground truth 3D data, while robustly dealing with artifacts such as noise and missing data.

Energy management strategy for fuel cellsupercapacitor hybrid vehicles based on prediction of energy demand
http://hdl.handle.net/2117/107433
Energy management strategy for fuel cellsupercapacitor hybrid vehicles based on prediction of energy demand
Carignano, Mauro; Costa Castelló, Ramon; Roda Serrat, Vicente; Nigro, Norberto; Junco, Sergio; Feroldi, Diego Hernan
Offering high efficiency and producing zero emissions Fuel Cells (FCs) represent an excellent alternative to internal combustion engines for powering vehicles to alleviate the growing pollution in urban environments. Due to inherent limitations of FCs which lead to slow transient response, FCbased vehicles incorporate an energy storage system to cover the fast power variations. This paper considers a FC/supercapacitor platform that configures a hard constrained powertrain providing an adverse scenario for the energy management strategy (EMS) in terms of fuel economy and drivability. Focusing on palliating this problem, this paper presents a novel EMS based on the estimation of shortterm future energy demand and aiming at maintaining the state of energy of the supercapacitor between two limits, which are computed online. Such limits are designed to prevent active constraint situations of both FC and supercapacitor, avoiding the use of friction brakes and situations of nonpower compliance in a short future horizon. Simulation and experimentation in a case study corresponding to a hybrid electric bus show improvements on hydrogen consumption and power compliance compared to the widely reported Equivalent Consumption Minimization Strategy. Also, the comparison with the optimal strategy via Dynamic Programming shows a room for improvement to the realtime strategies.
20170906T08:59:56Z
Carignano, Mauro
Costa Castelló, Ramon
Roda Serrat, Vicente
Nigro, Norberto
Junco, Sergio
Feroldi, Diego Hernan
Offering high efficiency and producing zero emissions Fuel Cells (FCs) represent an excellent alternative to internal combustion engines for powering vehicles to alleviate the growing pollution in urban environments. Due to inherent limitations of FCs which lead to slow transient response, FCbased vehicles incorporate an energy storage system to cover the fast power variations. This paper considers a FC/supercapacitor platform that configures a hard constrained powertrain providing an adverse scenario for the energy management strategy (EMS) in terms of fuel economy and drivability. Focusing on palliating this problem, this paper presents a novel EMS based on the estimation of shortterm future energy demand and aiming at maintaining the state of energy of the supercapacitor between two limits, which are computed online. Such limits are designed to prevent active constraint situations of both FC and supercapacitor, avoiding the use of friction brakes and situations of nonpower compliance in a short future horizon. Simulation and experimentation in a case study corresponding to a hybrid electric bus show improvements on hydrogen consumption and power compliance compared to the widely reported Equivalent Consumption Minimization Strategy. Also, the comparison with the optimal strategy via Dynamic Programming shows a room for improvement to the realtime strategies.

Realtime 3D reconstruction of nonrigid shapes with a single moving camera
http://hdl.handle.net/2117/104455
Realtime 3D reconstruction of nonrigid shapes with a single moving camera
Agudo Martínez, Antonio; MorenoNoguer, Francesc; Calvo, Begoña; Martinez Montiel, José Maria
This paper describes a realtime sequential method to simultaneously recover the camera motion and the 3D shape of deformable objects from a calibrated monocular video. For this purpose, we consider the NavierCauchy equations used in 3D linear elasticity and solved by finite elements, to model the timevarying shape per frame. These equations are embedded in an extended Kalman filter, resulting in sequential Bayesian estimation approach. We represent the shape, with unknown material properties, as a combination of elastic elements whose nodal points correspond to salient points in the image. The global rigidity of the shape is encoded by a stiffness matrix, computed after assembling each of these elements. With this piecewise model, we can linearly relate the 3D displacements with the 3D acting forces that cause the object deformation, assumed to be normally distributed. While standard finiteelementmethod techniques require imposing boundary conditions to solve the resulting linear system, in this work we eliminate this requirement by modeling the compliance matrix with a generalized pseudoinverse that enforces a prefixed rank. Our framework also ensures surface continuity without the need for a postprocessing step to stitch all the piecewise reconstructions into a global smooth shape. We present experimental results using both synthetic and real videos for different scenarios ranging from isometric to elastic deformations. We also show the consistency of the estimation with respect to 3D ground truth data, include several experiments assessing robustness against artifacts and finally, provide an experimental validation of our performance in real time at frame rate for small maps
© <year>. This manuscript version is made available under the CCBYNCND 4.0 license http://creativecommons.org/licenses/byncnd/4.0/
20170515T14:52:26Z
Agudo Martínez, Antonio
MorenoNoguer, Francesc
Calvo, Begoña
Martinez Montiel, José Maria
This paper describes a realtime sequential method to simultaneously recover the camera motion and the 3D shape of deformable objects from a calibrated monocular video. For this purpose, we consider the NavierCauchy equations used in 3D linear elasticity and solved by finite elements, to model the timevarying shape per frame. These equations are embedded in an extended Kalman filter, resulting in sequential Bayesian estimation approach. We represent the shape, with unknown material properties, as a combination of elastic elements whose nodal points correspond to salient points in the image. The global rigidity of the shape is encoded by a stiffness matrix, computed after assembling each of these elements. With this piecewise model, we can linearly relate the 3D displacements with the 3D acting forces that cause the object deformation, assumed to be normally distributed. While standard finiteelementmethod techniques require imposing boundary conditions to solve the resulting linear system, in this work we eliminate this requirement by modeling the compliance matrix with a generalized pseudoinverse that enforces a prefixed rank. Our framework also ensures surface continuity without the need for a postprocessing step to stitch all the piecewise reconstructions into a global smooth shape. We present experimental results using both synthetic and real videos for different scenarios ranging from isometric to elastic deformations. We also show the consistency of the estimation with respect to 3D ground truth data, include several experiments assessing robustness against artifacts and finally, provide an experimental validation of our performance in real time at frame rate for small maps

A bayesian approach to simultaneously recover camera pose and nonrigid shape from monocular images
http://hdl.handle.net/2117/104426
A bayesian approach to simultaneously recover camera pose and nonrigid shape from monocular images
MorenoNoguer, Francesc; Porta Pleite, Josep Maria
In this paper we bring the tools of the Simultaneous Localization and Map Building (SLAM) problem from a rigid to a deformable domain and use them to simultaneously recover the 3D shape of nonrigid surfaces and the sequence of poses of a moving camera. Under the assumption that the surface shape may be represented as a weighted sum of deformation modes, we show that the problem of estimating the modal weights along with the camera poses, can be probabilistically formulated as a maximum a posteriori estimate and solved using an iterative least squares optimization. In addition, the probabilistic formulation we propose is very general and allows introducing different constraints without requiring any extra complexity. As a proof of concept, we show that local inextensibility constraints that prevent the surface from stretching can be easily integrated.
An extensive evaluation on synthetic and real data, demonstrates that our method has several advantages over current nonrigid shape from motion approaches. In particular, we show that our solution is robust to large amounts of noise and outliers and that it does not need to track points over the whole sequence nor to use an initialization close from the ground truth.
© <year>. This manuscript version is made available under the CCBYNCND 4.0 license http://creativecommons.org/licenses/byncnd/4.0/
20170515T12:40:46Z
MorenoNoguer, Francesc
Porta Pleite, Josep Maria
In this paper we bring the tools of the Simultaneous Localization and Map Building (SLAM) problem from a rigid to a deformable domain and use them to simultaneously recover the 3D shape of nonrigid surfaces and the sequence of poses of a moving camera. Under the assumption that the surface shape may be represented as a weighted sum of deformation modes, we show that the problem of estimating the modal weights along with the camera poses, can be probabilistically formulated as a maximum a posteriori estimate and solved using an iterative least squares optimization. In addition, the probabilistic formulation we propose is very general and allows introducing different constraints without requiring any extra complexity. As a proof of concept, we show that local inextensibility constraints that prevent the surface from stretching can be easily integrated.
An extensive evaluation on synthetic and real data, demonstrates that our method has several advantages over current nonrigid shape from motion approaches. In particular, we show that our solution is robust to large amounts of noise and outliers and that it does not need to track points over the whole sequence nor to use an initialization close from the ground truth.

Sensor localization from distance and orientation constraints
http://hdl.handle.net/2117/104414
Sensor localization from distance and orientation constraints
Porta Pleite, Josep Maria; Rull Sanahuja, Aleix; Thomas, Federico
The sensor localization problem can be formalized using distance and orientation constraints, typically in 3D. Local methods can be used to refine an initial location estimation, but in many cases such estimation is not available and a method able to determine all the feasible solutions from scratch is necessary. Unfortunately, existing methods able to find all the solutions in distance space can not take into account orientations, or they can only deal with one or twodimensional problems and their extension to 3D is troublesome. This paper presents a method that addresses these issues. The proposed approach iteratively projects the problem to decrease its dimension, then reduces the ranges of the variable distances, and backprojects the result to the original dimension, to obtain a tighter approximation of the feasible sensor locations. This paper extends previous works introducing accurate range reduction procedures which effectively integrate the orientation constraints. The mutual localization of a fleet of robots carrying sensors and the position analysis of a sensor moved by a parallel manipulator are used to validate the approach.
20170515T11:31:33Z
Porta Pleite, Josep Maria
Rull Sanahuja, Aleix
Thomas, Federico
The sensor localization problem can be formalized using distance and orientation constraints, typically in 3D. Local methods can be used to refine an initial location estimation, but in many cases such estimation is not available and a method able to determine all the feasible solutions from scratch is necessary. Unfortunately, existing methods able to find all the solutions in distance space can not take into account orientations, or they can only deal with one or twodimensional problems and their extension to 3D is troublesome. This paper presents a method that addresses these issues. The proposed approach iteratively projects the problem to decrease its dimension, then reduces the ranges of the variable distances, and backprojects the result to the original dimension, to obtain a tighter approximation of the feasible sensor locations. This paper extends previous works introducing accurate range reduction procedures which effectively integrate the orientation constraints. The mutual localization of a fleet of robots carrying sensors and the position analysis of a sensor moved by a parallel manipulator are used to validate the approach.