3.1 Axis A: Augmented spatio-temporal perception of complex environments

The long-term objective of this research axis is to build accurate and composite models of large-scale environments that mix metric, topological and semantic information. Ensuring the consistency of these various representations during the robot exploration and merging/sharing observations acquired from different viewpoints by several collaborative robots or sensors attached to the infrastructure, are very difficult problems. This is particularly true when different sensing modalities are involved and when the environments are time-varying. A recent trend in Simultaneous Localization And Mapping is to augment low-level maps with semantic interpretation of their content. Indeed, the semantic level of abstraction is the key element that will allow us to build the robot’s environmental awareness (see Axis B). For example, the so-called semantic maps have already been used in mobile robot navigation, to improve path planning methods, mainly by providing the robot with the ability to deal with human-understandable targets. New studies to derive efficient algorithms for manipulating the hybrid representations (merging, sharing, updating, filtering) while preserving their consistency are needed for long-term navigation.

3.2 Axis B: Situation awareness for decision and planning

The long-term objective of this research axis is to design and develop a decision-making module that is able to (i) plan the mission of the robots (global planning), (ii) generate the sub-tasks (local objectives) necessary to accomplish the mission based on Situation Awareness and (iii) plan the robot paths and/or sets of actions to accomplish each subtask (local planning). Since we have to face uncertainties, the decision module must be able to react efficiently in real-time based on the available sensor information (on-board or attached to an IoT infrastructure) in order to guarantee the safety of humans and things. For some tasks, it is necessary to coordinate a multi-robots system (centralized strategy), while for other each robot evolves independently with its own decentralized strategy. In this context, Situation Awareness is at the heart of an autonomous system in order to feed the decision-making process, but also can be seen as a way to evaluate the performance of the global process of perception and interpretation in order to build a safe autonomous system. Situation Awareness is generally divided into three parts: perception of the elements in the environment (see Axis A), comprehension of the situation, and projection of future states (prediction and planning). When planning the mission of the robot, the decision-making module will first assume that the configuration of the multi-robot system is known in advance, for example one robot on the ground and two robots on the air. However, in our long-term objectives, the number of robots and their configurations may evolve according to the application objectives to be achieved, particularly in terms of performance, but also to take into account the dynamic evolution of the environment.

3.3 Axis C: Advanced multi-sensor control of autonomous multi-robot systems

The long-term objective of this research axis is to design multi-sensor (on-board or attached to an IoT infrastructure) based control of potentially multi-robots systems for tasks where the robots must navigate into a complex dynamic environment including the presence of humans. This implies that the controller design must explicitly deal not only with uncertainties and inaccuracies in the models of the environment and of the sensors, but also to consider constraints to deal with unexpected human behavior. To deal with uncertainties and inaccuracies in the model, two strategies will be investigated. The first strategy is to use Stochastic Control techniques that assume known probability distribution on the uncertainties. The second strategy is to use system identification and reinforcement learning techniques to deal with differences between the models and the real systems. To deal with unexpected human behavior, we will investigate Stochastic Model Predictive Control (MPC) techniques and Model Predictive Path Integral (MPPI) control techniques in order to anticipate future events and take optimal control actions accordingly. A particular emphasis will be given to the theoretical analysis (observability, controllability, stability and robustness) of the control laws.

4.1 Environment monitoring with a collaborative robotic system

The first application that we will consider, concerns monitoring the environment using an autonomous multi-robots system composed by ground robots and aerial robots. The ground robots will patrol following a planned trajectory and will collaborate with the aerial drones to perform tasks in structured (e.g. industrial sites), semi-structured (e.g. presence of bridges, dams, buildings) or unstructured environments (e.g. agricultural space, forest space, destroyed space). In order to provide a deported perception to the ground robots an aerial drone will be in operation while the second one will be recharging its batteries on the ground vehicle. Coordinated and safe autonomous take-off and landing of the aerial drones will be a key factor to ensure the continuity of service for a long period of time. Such a multi-robot system can be used to localize survivors in case of disaster or rescue, to localize and track people or animals (for surveillance purpose), to follow the evolution of vegetation (or even invasion of insects or parasites), to follow evolution of structures (bridges, dams, buildings, electrical cables) and to control actions in the environment like for example in agriculture (fertilization, pollination, harvesting, ...), in forest (rescue), in land (planning firefighting). To successfully achieve such an application will require to build a representation of the environment and localize the robots in the map (see Axis A in section 3.1), to re-plan the tasks of each robot when unpredictable events occurs (see Axis B in section 3.2) and to control each robot to execute the tasks (see Axis C in section 3.3). Depending on the application field the scale and the difficulty of the problems to be solved will be increasing. In the Smart Factories field, we have a relatively small size environment, mostly structured and with the possibility to communicate with and highly instrumented (sensors) and connected environment. In the Smart Territories field, we have large semi-structured or unstructured environments that are not instrumented. To set up demonstrations of this application, we intend to collaborate with industrial partners and local institutions. For example, we plan to set up a collaboration with the Parc Naturel Régional des Prealpes d'Azur to monitor the evolution of fir trees infested by bark beetles.

4.2 Transportation of people and goods with autonomous connected vehicles

The second application that we will consider, concerns the transportation of people and goods with autonomous connected vehicles. ACENTAURI will contribute to the development of Autonomous Connected Vehicles (e.g. Learning, Mapping, Localization, Navigation) and the associated services (e.g. towing, platooning, taxi). We will develop efficient algorithms to select on-line connected sensors coming from the infrastructure in order to extend and enhanced the embedded perception of a connected autonomous vehicle. In cities, there exists situations where visibility is very bad for historical reason or simply occasionally because of traffic congestion, service delivery (trucks, buses) or roadworks. It exists also situation where danger are more important and where a connected system or intelligent infrastructure can help to enhance perception and then reduce the risk of accident (see Axis A in section 3.1). In ACENTAURI, we will also contribute to the development of assistance and service robotics by re-using the same technologies required in autonomous vehicles. By adding the social level in the representation of the environment, and using techniques of proactive and social navigation, we will offer the possibility of the robot to adapt its behavior in presence of humans (see Axis B in section 3.2). ACENTAURI will study sensing technology on SDVs (Self-Driving Vehicles) used for material handling to improve efficiency and safety as products are moved around Smart Factories. These types of robots have the ability to sense and avoid people, as well as unexpected obstructions in the course of doing its work (see Axis C in section 3.3). The ability to automatically avoid these common disruptions is a powerful advantage that keeps production running optimally. To set up demonstrations of this application, we will continue the collaboration with industrial partners (Renault) and with the Communauté d'Agglomération Sophia Antipolis (CASA). Experiments with 2 autonomous Renault Zoe cars will be carried out in a dedicated space lend by CASA. Moreover, we propose, with the help of the Inria Service d'Expérimentation et de Développement (SED), to set up a demonstration of an autonomous shuttle to transport people in the future extended Inria/UCA site.

5.1 Footprint of research activities

The main footprint of our research activities comes from travels and power consumption (computers and computer cluster). Concerning travels, due to the COVID-19 pandemic they have been considerably limited. Concerning power consumption, besides classical actions to reduce the waste of energy, our research focus on efficient optimization algorithms to minimize the computation time of computers onboard of our robotic platforms.

5.2 Impact of research results

We have planned to propose several projects related to the environmental challenges. We give below two examples of the most advanced projects that have been accepted in 2022.

The first concerns the monitoring of forest in collaboration with the Parc Naturel Regional des Préalpes d'Azur, ONF and DRAAF (see project EPISUD in section 10.5).

The second concerns the autonomous vehicles in agricultural application in collaboration with INRAE Clermont-Ferrand in the context of the PEPR "Agrologie et numérique". We aims to develop robotic approaches for the realization of new cultural practices, capable of acting as a lever for agroecological practices (see project NINSAR in section 10.4). .

7.1 New software

7.2 New platforms

Participants: Ezio Malis, Philippe Martinet, Nicolas Chleq, Emmanuel Alao, Quentin Louvel.

ICAV platform

ICAV platform has been funded by PUV@SOPHIA project (CASA, PACA Region and state), self funding, Digital Reference Center from UCA, and Accademy 1 from UCA. We have now two autonomous vehicles, one instrumented vehicle, many sensors (RTK GPS, Lidars, Cameras), Communications devices (C-V2X, IEEE 802.11p), and one standalone localization and mapping system.

ICAV platform is composed of

• ICAV1 is an old generation of ZOE. It has been bought fully robotized and intrumented. It is equiped with Velodyne Lidar VLP16, low cost IMU and GPS, three cameras and one embedded computer.
• ICAV2 is a new generation of ZOE which has been instrumented and robotized in 2021. It is equiped with Velodyne Lidar VLP16, low cost IMU and GPS, three cameras, two solidstate Lidars RS-M1, one embedded computer and one NVIDIA Jetson AGX Xavier.
• ICAV3 will be instrumented with different LIDARS and multi cameras system (LADYBUG5+)
• A ground truth RTK system. An RTK GPS base station has been installed and a local server configured inside the Inria Center. Each vehicle is equiped with an RTK GPS receiver and connected to a local server in order to compute a centimeter localization accuracy.
• A standalone localization and mapping system. This system is composed of a Velodyne Lidar VLP16, low cost IMU and GPS, and one NVIDIA Jetson AGX Xavier.
• A communication system V2X based on the technology C-V2X and IEEE 802.11p.
• Different lidar sensors (Ouster OS2-128, RS-LIDAR16, RS-LIDAR32, RS-Ruby), and one multi-cameras system (LADYBUG5+)

The main applications of this platform are:

• datasets acquisition
• localization, Mapping, Depth estimation, Semantization
• autonomous navigation (path following, parking, platooning, ...), proactive navigation in shared space
• situation awareness and decision making
• V2X communication
• autonomous landing of UAVs on the roof

 

 

 

 

ICAV2 has been used by Maria Kabtoul in order to demonstrate the effectiveness of autonomous navigation of a car in a crowd.

Indoor autonomous mobile platform

The mobile robot platform has been funded by the MOBIDEEP project in order to demonstrate autonomous navigation capabilities in emcumbered and crowded environment. This platform is composed of:

• one omnidirectional mobile robot (SCOOT MINI with mecanum wheels from AGIL-X)
• one NVIDIA Jetson AGX Xavier for deep learning algorithm implementation
• one general labtop
• one Robosense RS-LIDAR16
• one Ricoh Z1 360° camera
• one Sony RGB-D D455 camera

The main applications of this platform are:

• indoor datasets acquisition
• localization, Mapping, depth estimation, Semantization
• proactive navigation in shared space
• pedestrian detection and tracking

This platform is used in MOBI-DEEP project for integration of different work from the consortium. It is used to demonstrate new results on social navigation.

E-Wheeled platform

E-WHEELED is an AMDT Inria project (2019-22) coordinated by Philippe Martinet. The aim is to provide mobility to things by implementing connectivity techniques. It makes available an Inria expert engineer (Nicolas Chleq) in ACENTAURI in order to demonstrate the Proof of Concept using a small size demonstrator. Due to the COVID19, the project has been delayed.

Automous parking using multi sensor based approach

Participants: David Perez Gonzalez (LS2N), Olivier Kermorgant (LS2N), Salvador Dominguez Quijada (LS2N), Philippe Martinet.

Autonomous parking has been mainly addressed from a path planning point of view, and not often in a generic way to deal with all kind for all types of parking maneuvers (perpendicular, diagonal for both forward and backward motions and parallel for backward motions). An alternative way is to address the parking problem from a control of view. The main problems to be solved are to find an empty spot, to parametrize the autonomous parking framework for the type of parking, and to adapt the behavior in regard with the dynamic evolution of the environment. Generally, not all that point are taken into account in the state of the art solution. To address all the mentioned problems, we have proposed a single common Multi-Sensor-Based Predictive Control framework 9 in the context of the PhD of David Perez Morales.

The contribution of this work is the formalization of parking operations under a common MSBPC framework allowing the vehicle to park autonomously into perpendicular and diagonal parking spots with both forward and backward motions and into parallel ones with backward motions. By considering an additional auxiliary subtask and a predictive approach, the presented technique is capable of performing multiple maneuvers (if necessary) in order to park successfully in constrained workspaces. The auxiliary subtask is a key since it allows to account for the potential motions that go essentially against the final goal (i.e. drive the vehicle away from the parking spot) but that at the end allows to park successfully.

Autonomous navigation in human populated environment

Participants: Maria Kabtoul, Anne Spalanzani, Philippe Martinet.

The work 6 is focused on developing a navigation system for autonomous vehicles operating around pedestrians. The suggested solution is a proactive framework capable of anticipating pedestrian reactions and exploiting their cooperation to optimize the performance while ensuring pedestrians safety and comfort. A cooperation-based model for pedestrian behaviors around a vehicle is proposed. The model starts by evaluating the pedestrian tendency to cooperate with the vehicle by a time-varying factor. This factor is then used in combination with the space measurements to predict the future trajectory.

The model is exploited in the navigation system to control both the velocity and the local steering of the vehicle. Firstly, the longitudinal velocity is proactively controlled. Two criteria are considered to control the longitudinal velocity. The first is a safety criterion using the minimum distance between an agent and the vehicle’s body. The second is proactive criterion using the cooperation measure of the surrounding agents. The latter is essential to exploit any cooperative behavior and avoid the freezing of the vehicle in dense scenarios. Finally, the optimal control is derived using the gradient of a cost function combining the two previous criteria.

Minimizing risk injury in motion planning

Participants: Luis Guardini, Anne Spalanzani (CHROMA), Philippe Martinet, Christian Laugier (CHROMA), Anh-Lam Do (Renault).

In motion planning, generally the main goal is to find a safe path free from any collision. If this appears clear and moreless easy in static environment, it is no more the case when we consider dynamic human populated environment. The main problems remains to take into account unexpected event which occur most of time from the human side and few time from natural phenomena, or to face situations where the environment is unknown and/or not fully observable. In all these situations, it is difficult to warranty fully global safety among one horizon of prediction, except being very conservative. Then it exists a risk of collision that must be taken into account. Among the research work done recenty, some have considered the evaluation of the injury risk associated with a particular and global situation, and the Probability of Collision with Injury Risk (PCIR) has been defined, making collision mitigation an important element in motion planning.

Despite the rich number of functionalities of Advanced Driver-Assistance Systems (ADAS), there is still a gap on finding a way to evaluate the best decision globally.

This work presents a novel motion planning framework 8 to generate emergency maneuvers in complex and risky scenarios using active mitigation. The classical MPPI algorithm is improved to be used in a probabilistic dynamic cost map under limited perception range. A cost map with global probability of injury to all road users is used as a constraint to the problem in order to compute target selection based on the global minimum risk considering all road users. Real experiments introduce the use of virtual objects by merging simulation and real sensor data to safely produce collision and mitigation experiments. Results show that the proposed algorithm can perform correctly, by finding collision free trajectories in complex scenarios and compute viable target selection that minimizes global injury risk when collision is inevitable.

Representation of the environment in autonomous driving applications

Participants: Ziming Liu, Ezio Malis, Philippe Martinet.

Visual odometry is an important task for the localization of autonomous robots and several approaches have been proposed in the literature. We consider visual odometry approaches that use stereo images since the depth of the observed scene can be correctly estimated. These approaches do not suffer from the scale factor estimation problem of monocular visual odometry approaches that can only estimate the translation up to a scale factor. Traditional model-based visual odometry approaches are generally divided into two steps. Firstly, the depths of the observed scene are estimated from the disparity obtained by matching left and right images. Then, the depths are used to obtain the camera pose. The depths can be computed for selected features like in sparse methods or for all possible pixels in the image like in dense direct methods 1.

Recently, more and more end-to-end deep learning visual odometry approaches have been proposed, including supervised and unsupervised models. However, it has been shown that hybrid visual odometry approaches can achieve better results by combining a neural network for depth estimation with a model-based visual odometry approach. Moreover, recent works have shown that deep learning based approaches can perform much better than the traditional stereo matching approaches and provide more accurate depth estimation.

State-of-the-art supervised stereo depth estimation networks can be trained with the ground truth disparity (depth) maps. However, ground truth depth maps are extremely difficult (almost impossible for dense depth maps) to be obtained for all pixels in real and variable environments. Therefore, some works explore to train a model on simulated environments and reduce its gap with the real world data. However, there are only few works focusing on unsupervised stereo matching. Besides using the ground truth depth maps or unsupervised training, we can also build temporal images reconstruction loss with ground truth camera pose which is easy to be obtained.

The main contributions of our work published in 7 are (i) a novel pose-supervised network (that is named PDENet) for dense stereo depth estimation, that can achieve the SOTA results without ground truth depth maps and (ii) a dense hybrid stereo visual odometry system, which combines the deep stereo depth estimation network with a robust model-based Dense Pose Estimation module (that is named DPE).

This year we address the problem of increasing the precision of the proposed. Indeed, machine learning methods generate hallucinated depths even in areas where it is impossible to estimate the depth due to several reasons, like occlusions, homogeneous areas, etc. Generally, this produces wrong depth estimation that leads to errors in odometry estimation. To avoid this problem, we propose a new approach to generate multiple masks that will be combined to discard wrong pixels and therefore increase the accuracy of visual odometry. Our key contribution is to use the multiple masks not only in the odometry computation but also to improve the learning of the neural network for depth map generation. Experiments on several datasets show that masked dense direct stereo visual odometry provides much more accurate results than previous approaches in the literature. This work has been submitted to ICRA (International Conference on Robotics and Automation).

Situation Awareness and Decision Making for autonomous driving in urban environment

Participants: Priyanga Kalyanakumar, Philippe Martinet, Ezio Malis.

Autonomous vehicles more commonly known as self-driving cars, have received a lot of attention in recent years. Many stakeholders such as Google, Uber, Tesla and so forth have invested in development and testing their own autonomous driving cars. The challenge to make an autonomous car which is not only limited to performance like any classic cars but also takes into account safety of the passengers and pedestrians. Despite achieving multiple milestones with regards to development of technologies, autonomous driving is still an active research area and still requires more experimentation and making architecture before becoming entirely autonomous. The intriguing area of self-driving car motivates the author to research more about this self-driving technology. This thesis aims to achieve key aspects of Situation Awareness in which the autonomous vehicles has the ability to recognize the existing situation and predict how the situation evolve in the future. We designed an architecture to understand the driving scene along with the behavior of vehicles and predict their future trajectories. A novel ontology-based semantic knowledge with driver behavior model is proposed to enhance the performance of the future trajectory prediction using the Long Short-Term Memory encoder decoder with attention approach. The designed ontology describes semantic knowledge about entities and features and their relation in the driving context. The driver behavior model identifies the vehicle behavior using Machine Learning algorithms. By utilizing the ontology, behavior model and vehicle trajectory information as input the model shows an satisfactory performance in predicting the future position and velocity of the vehicles.

Autonomous navigation in human populated environment

Participants: Enrico Fiasché, Philippe Martinet, Ezio Malis.

Recently, robots are widely used around humans, in homes and in public, due to their advantages. In order to allow the robot and human to share the same space under natural interaction, the ability for robot to generate a socially acceptable trajectory is the key step. Autonomous robots find difficult to navigate in a crowded environment due to high level of uncertainty caused by dynamic agents present in the environment. This uncertainty leads to the so called Freezing Robot Problem (FRP). Many of the state-of-the-art techniques used to solve this problem, involving collaboration between humans and robots, are studied.

This work focuses on the control aspects of the autonomous navigation in human populated environment, using models that are able to predict the future outputs over a certain prediction time horizon, considering the social model for the pedestrian. Rather than of letting the robot move passively to avoid people, the aim of the master thesis is to ensure safe and secure navigation in which the robot has a proactive role agents and AV collaboration. In particular, the master thesis studied and analyzed the Model Predictive Control (MPC) technique, focusing on the performances. Different types of Control Parameterization were used and compared in order to meet real-time feasibility. The Social Force Model (SFM) is used to predict the pedestrian motions. Simulations and experiments were carried out and evaluated in different types of social scenarios, demonstrating that the proactive navigation provided by the model is able to generate secure and safe trajectories avoiding the Freezing Robot Problem.

UAV/AGV collaboration for continuous service

Participants: Matteo Azzini, Ezio Malis, Philippe Martinet.

A collaborative multi-robot system made up of ground and aerial vehicles presents numerous advantages for applications including surveillance, precision agriculture, or wildfire detection and fighting. Alternating autonomous UAVs (Unmanned Aerial Vehicle) could solve the problem of low battery life presented by such aerial vehicles, utilising the ground vehicle as moving recharging station, while not compromising the tasks being carried out by the robots on which the platforms sit, thus improving the autonomy and lifetime of the overall systems.

Previous study already designed an autonomous take-off and landing system, taking into account also external disturbances. The novelty in this work is to present a system based on the fusion of data coming from the camera with gimbal mechanism, IMU, gyroscope, GPS and UWB in order to develop a framework able to perform a continuous and robust relative localization of the aerial vehicle with respect to the moving landing platform. Furthermore, a gimbal mechanism control will be presented based on the localization information previously mentioned. Finally, the UWB technology has been tested in order to find a configuration that allow to extract not only the position, but also the attitude of the UAV with respect to the AGV.

Complete closed-form and accurate solution to pose estimation from 3D correspondences

Participants: Ezio Malis.

Computing the pose from 3D data acquired in two different frames is of high importance for several robotic tasks like odometry, SLAM and place recognition. The pose is generally obtained by solving a least-squares problem given points-to-points, points-to-planes or points to lines correspondences. The non-linear least-squares problem can be solved by iterative optimization or, more efficiently, in closed-form by using solvers of polynomial systems.

In this work, a complete and accurate closed-form solution for a weighted least-squares problem has been studied. Adding weights for each correspondence allow to increase robustness to outliers. Contrary to existing methods, the proposed approach is complete since it is able to solve the problem in any non-degenerate case and it is accurate since it is guaranteed to find the global optimal estimate of the weighted least-squares problem. The key contribution of this work is the combination of a robust u-resultant solver with the quaternion parametrisation of the rotation, that was not considered in previous works. Simulations and experiments on real data demonstrate the superior accuracy and robustness of the proposed algorithm compared to previous approaches. This work has been submitted to RA-L.

Uncertainty-aware Navigation in Crowded Environment

Participants: Emmanuel Alao, Philippe Martinet.

Robots are now widely used around humans, in homes and public places like the museums, all due to their many benefits. These autonomous robots are called social or service robots and they always find it difficult to navigate in crowded environments; largely because of the high level of uncertainty in observing and predicting human behaviours in a highly dynamic environment. Uncertainty is propagated during prediction and might grow to levels that renders the whole environment unsafe for the robot leading to the so called Freezing Robot Problem (FRP).

This work 3 presents our proposed approach for solving navigation problems in a crowd with uncertainty by formalizing the problem as a stochastic model predictive problem enclosed in a dynamic channel. We first proposed a model that reduces the estimation errors in the state of the pedestrians in the robot local frame by accounting for the motion of the robot. Then we proceed to show how we formalize the control problem as a stochastic NMPC to reduce the occurrence of the freezing robot problem (FRP) by adding a proxemics objective function, a probability constraint and a dynamic channel to make the robot follow a collision free path in the crowd.

Using numerical optimization methods enables the planner to compute new control commands in realtime. The implementation results shows that the method performs better than the deterministic MPC.

Control Parameterization in Nonlinear Model Predictive Control

Participants: Franco Fusco, Guillaume Allibert, Philippe Martinet.

Model Predictive Control (MPC) is a very effective control technique when the prediction of the future behavior is possible. For instance, this allows to prevent from safety when navigating in a dynamic environment, and to anticipate the control according to the future behavior of the control system (taking into account lags, delays, perturbations, ...).

The computation time and complexity increase drastically according the length of the horizon of prediction and the number of degree of freedom of the control input. To decrease the numerical complexity of nonlinear MPC problems without necessarily affecting the performances significantly, use of control parameterization has been proposed in the literature. Tradeoffs between computational load and performances should be sought in order to meet real-time feasibility requirements. However, making the problem more tractable for the hardware should not necessarily imply a decrease in performances.

In this work, we review the use of parameterizations 4 and propose a Sequential Quadratic Programming algorithm for nonlinear MPC 5. The performances of the solver in simulation is benchmarked, showing that parameterizations allow to attain good performances with (significantly) lower computation times than state-of-the-art solvers.

9.1 Bilateral contracts with industry

ACENTAURI is responsible of two research contracts with Naval Group (2022-2024).

10.1 International initiatives

10.2 International research visitors

10.3 European initiatives

10.4 National initiatives

10.5 Regional initiatives

11.1 Promoting scientific activities

11.2 Teaching - Supervision - Juries

The team has received 3 master 2 students, 1 master 1 student and 1 intern.

11.3 Popularization

12.1 Major publications

• 1 articleA. I.Andrew I. Comport, E.Ezio Malis and P.Patrick Rives. Real-time Quadrifocal Visual Odometry.The International Journal of Robotics Research2922010, 245-266
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12.2 Publications of the year

12.3 Cited publications

• 9 articleD.David Pérez-Morales, O.Olivier Kermorgant, S.Salvador Dom\'inguez-Quijada and P.Philippe Martinet. Multi-Sensor-Based Predictive Control For Autonomous Parking.IEEE Transactions on Robotics2August 2022, 835-851
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