Model Predictive Path Integral Control Framework for Partially Observable Navigation: A Quadrotor Case Study

Recently, Model Predictive Path Integral (MPPI) control algorithm has been extensively applied to autonomous navigation tasks, where the cost map is mostly assumed to be known and the 2D navigation tasks are only performed. In this paper, we propose a generic MPPI control framework that can be used for 2D or 3D autonomous navigation tasks in either fully or partially observable environments, which are the most prevalent in robotics applications. This framework exploits directly the 3D-voxel grid acquired from an on-board sensing system for performing collision-free navigation. We test the framework, in realistic RotorS-based simulation, on goal-oriented quadrotor navigation tasks in a cluttered environment, for both fully and partially observable scenarios. Preliminary results demonstrate that the proposed framework works perfectly, under partial observability, in 2D and 3D cluttered environments.Comment: The withdrawal reason is that the co-authors do not want to associate their name to the article on arXi

HiDAnet: RGB-D Salient Object Detection via Hierarchical Depth Awareness

RGB-D saliency detection aims to fuse multi-modal cues to accurately localize salient regions. Existing works often adopt attention modules for feature modeling, with few methods explicitly leveraging fine-grained details to merge with semantic cues. Thus, despite the auxiliary depth information, it is still challenging for existing models to distinguish objects with similar appearances but at distinct camera distances. In this paper, from a new perspective, we propose a novel Hierarchical Depth Awareness network (HiDAnet) for RGB-D saliency detection. Our motivation comes from the observation that the multi-granularity properties of geometric priors correlate well with the neural network hierarchies. To realize multi-modal and multi-level fusion, we first use a granularity-based attention scheme to strengthen the discriminatory power of RGB and depth features separately. Then we introduce a unified cross dual-attention module for multi-modal and multi-level fusion in a coarse-to-fine manner. The encoded multi-modal features are gradually aggregated into a shared decoder. Further, we exploit a multi-scale loss to take full advantage of the hierarchical information. Extensive experiments on challenging benchmark datasets demonstrate that our HiDAnet performs favorably over the state-of-the-art methods by large margins

Sampling-Based MPC for Constrained Vision Based Control

International audienceVisual servoing control schemes, such as Image-Based (IBVS), Pose Based (PBVS) or Hybrid-Based (HBVS) have been extensively developed over the last decades making possible their uses in a large number of applications. It is well-known that the main problems to be handled concern the presence of local minima or singularities, the visibility constraint, the joint limits, etc. Recently, Model Predictive Path Integral (MPPI) control algorithm has been developed for autonomous robot navigation tasks. In this paper, we propose a MPPI-VS framework applied for the control of a 6-DoF robot with 2D point, 3D point, and Pose Based Visual Servoing techniques. We performed intensive simulations under various operating conditions to show the potential advantages of the proposed control framework compared to the classical schemes. The effectiveness, the robustness and the capability in coping easily with the system constraints of the control framework are shown

Model Predictive Path Integral Control Framework for Partially Observable Navigation: A Quadrotor Case Study

International audienceRecently, Model Predictive Path Integral (MPPI) control algorithm has been extensively applied to autonomous navigation tasks, where the cost map is mostly assumed to be known and the 2D navigation tasks are only performed. In this paper, we propose a generic MPPI control framework that can be used for 2D or 3D autonomous navigation tasks in either fully or partially observable environments, which are the most prevalent in robotics applications. This framework exploits directly the 3D-voxel grid acquired from an on-board sensing system for performing collision-free navigation. We test the framework, in realistic RotorS-based simulation, on goal-oriented quadrotor navigation tasks in a cluttered environment, for both fully and partially observable scenarios. Preliminary results demonstrate that the proposed framework works perfectly, under partial observability, in 2D and 3D cluttered environments.The supplementary video attached to this work is available at: https://bit.ly/2PAbES

Real-time visual predictive controller for image-based trajectory tracking of a mobile robot

International audienceThis paper deals with the design of a real-time controller for trajectory tracking in the image plane. The Image-Based Visual Servoing (IBVS) task is addressed by a visual predictive approach. The trajectory tracking is formulated into a nonlinear optimization problem in the image plane. The unavoidable constraints in experiments are easily taken into account in the design of the predictive control law. The global model, combining the mobile robot and camera model, is used to predict the behavior of the process. The flatness property of this global model is proved in the general case, that is whatever the camera posture. The flat model permits to reduce the computational time by a factor 2. Experiments are performed on a non holonomic mobile robot with a deported perspective camera. Experimental results show the efficiency and the robustness of the real-time control approach. Visibility constraints are added to point out the capability of the control to avoid obstacles

Visual Predictive Control for Manipulators with Catadioptric Camera

International audienceThis paper deals with Image Based Visual Ser-voing (IBSV) by a Visual Predictive Control (VPC) approach. Based on Nonlinear Model Predictive Control (NMPC), the visual servoing problem is formulated into a nonlinear constrained minimization problem in the image plane. A global model describing the behavior of the robotic system equipped with the camera is used to predict the evolution of the visual feature on a future horizon. The main interest of this method is the capability to easily take into account different constraints like mechanical limitations and/or visibility contraints. Simulation experiments are performed on a planar manipulator with an omnidirectional camera. Comparisons with the classical control law based on the interaction matrix highlight the efficiency and the robustness of the proposed approach, especially in difficult initial configurations and large displacements

A Flat Model Predictive Controller For Trajectory Tracking In Image Based Visual Servoing

International audienceImage-Based Visual Servoing (IBVS) is a control strategy using visual information to control the motion of robotic systems. Classical IBVS can not take into account either the mechanical constraints (joint and actuator limitations) or the visibility constraints, very important in this scheme. Model Predictive Control (MPC) is well adapted to deal with these drawbacks. However, applied to fast systems (e.g. mobile robots), the computational time is a great challenge for real time applications. One way to reduce this time is to use the concept of differential flatness. In this paper, a new IBVS strategy based on a flat MPC approach is proposed. The capabilities of this approach in terms of trajectory tracking and obstacle avoidance are pointed out. Applied to mobile robot trajectory tracking, a simulation experiment shows the efficiency and the robustness of this new control scheme. The computational time required by the proposed solution is compared with the nonlinear solution and easily enables a real-time application