Multi-Robot Weighted Coverage Path Planning: a Solution based on the DARP Algorithm

International audienceCovering a given area with a team of mobile robots in a minimum time is a well-studied problem with many real-world applications. A rarely studied subject, however, is the case of a weighted plane: due to the necessity of taking time-consuming measurements or having to traverse different kinds of terrains, the coverage time may vary over the environment and the path planning needs to be adapted accordingly. In this paper, we present an adapted version of a state-of-the-art mCPP (multi-robot coverage path planning) approach, the DARP algorithm, to make it suitable to deal with weighted environments. In particular, we propose several modifications to DARP that allow overcoming some of its limitations and, as a result, obtain an increased convergence rate and decreased convergence time with respect to the original version. Furthermore, as proved by extensive simulations, these improvements are also noticed in the unweighted version of the problem

Cooperative Localization and SLAM Based on the Extended Information Filter

Virtual realit

Cooperative Visual-Inertial Sensor Fusion: Fundamental Equations

International audienceThis paper provides a new theoretical and basic result in the framework of cooperative visual-inertial sensor fusion. Specifically, the case of two aerial vehicles is investigated. Each vehicle is equipped with inertial sensors (accelerometer and gyroscope) and with a monocular camera. By using the monocular camera, each vehicle can observe the other vehicle. No additional camera observations (e.g., of external point features in the environment) are considered. First, the entire observable state is analytically derived. This state includes the relative position between the two aerial vehicles (which includes the absolute scale), the relative velocity and the three Euler angles that express the rotation between the two vehicle frames. Then, the basic equations that describe this system are analytically obtained. In other words, both the dynamics of the observable state and all the camera observations are expressed only in terms of the components of the observable state and in terms of the inertial measurements. These are the fundamental equations that fully characterize the problem of fusing visual and inertial data in the cooperative case. The last part of the paper describes the use of these equations to achieve the state estimation through an EKF. In particular, a simple manner to limit communication among the vehicles is discussed. Results obtained through simulations show the performance of the proposed solution, and in particular how it is affected by limiting the communication between the two vehicles

Cooperative Visual-Inertial Sensor Fusion: Fundamental Equations and State Determination in Closed-Form

International audienceThis paper investigates the visual and inertial sensor fusion problem in the cooperative case and provides new theoretical and basic results.Specifically, the case of two agents is investigated. Each agent is equipped with inertial sensors (accelerometer and gyroscope) and with a monocular camera. By using the monocular camera, each agent can observe the other agent. No additional camera observations (e.g., of external point features in the environment) are considered.First, the entire observable state is analytically derived. This state contains the relative position between the two agents (which includes the absolute scale), the relative velocity, the three Euler angles that express the rotation between the two local frames and all the accelerometer and gyroscope biases.Then, the basic equations that describe this system are analytically obtained. %In other words, both the dynamics of the observable state and all the camera observations are expressed only in terms of the components of the observable state and in terms of the inertial measurements. These are the fundamental equations that fully characterize the problem of fusing visual and inertial data in the cooperative case. The last part of the paper describes the use of these equations to obtain a closed-form solution that provides the observable state in terms of the visual and inertial measurements provided in a short time interval. The impact of the presence of the bias on the performance of this closed-form solution is also investigated and a simple and effective method to obtain the gyroscope bias is proposed.Extensive simulations clearly show that the proposed method is successful. It is worth noting that it is possible to automatically retrieve the absolute scale and simultaneously calibrate the gyroscopes not only without any prior knowledge, but also without external point features in the environment

Distributed Information Filters for MAV Cooperative Localization

International audienceThis paper introduces a new approach to the problem of simultaneously localizing a team of micro aerial vehicles (MAV) equipped with inertial sensors able to monitor their motion and with exteroceptive sensors. The method estimates a delayed state containing the trajectories of all the MAVs. The estimation is based on an Extended Information Filter whose implementation is distributed over the team members. The paper introduces two contributions. The former is a trick which allows exploiting the information contained in the inertial sensor data in a distributed manner. The latter is the use of a projection filter which allows exploiting the information contained in the geometrical constraints which arise as soon as the MAV orientations are characterized by unitary quaternions. The performance of the proposed strategy is evaluated with synthetic data. In particular, the benefit of the previous two contributions is pointed out

Cognitive-based Adaptive Control for Cooperative Multi-Robot Coverage

International audienceIn this paper, the problem of positioning a team of mobile robots for a surveillance task in a non-convex environment with obstacles is considered. The robots are equipped with global positioning capabilities (for instance they are equipped with GPS) and visual sensors able to monitor the surrounding environment. Furthermore, they are able to communicate one with each other. The goal is to maximize the area monitored by the team, by identifying the best configuration of the team members. Due to the non-convex nature of the problem, an analytical solution can not be obtained. The proposed method is based on a new cognitive-based, adaptive optimization algorithm (CAO). This method allows getting coordinated and scalable controls to accomplish the task, even when the obstacles are unknown and the team is heterogeneous, i.e. each robot is equipped with a different type of visual sensor. Extensive simulations are presented to show the efficiency of the proposed approach

Adaptive-based Distributed Cooperative Multi-Robot Coverage

International audienceIn this paper we present a solution to the problem of positioning a team of mobile robots for a surveillance task in a non-convex environment with unknown obstacles. The problem is addressed taking into account several physical and environmental constraints like limited sensor capabilities, obstacle-avoidance, etc. The goal is to maximize the area monitored by the team, by identifying the best configuration of the team members. Due to the non-convex nature of the problem, an analytical solution cannot be obtained. The proposed method is based on a new cognitive-based, adaptive optimization algorithm (CAO). This method allows getting coordinated and scalable controls to accomplish the task. Furthermore, we propose a different formulation of the problem in order to obtain a distributed solution which allows us to consider also limited communication capabilities. Extensive simulations are presented to evaluate the efficiency of the proposed algorithm and to compare centralized and distributed approach

Adaptive-based, Scalable Design for Autonomous Multi-Robot Surveillance

International audienceIn this paper the problem of positioning a team of mobile robots for a surveillance task in a non-convex environment with obstacles is considered. The robots are equipped with global positioning capabilities (for instance they are equipped with GPS) and visual sensors able to monitor the surrounding environment. The goal is to maximize the area monitored by the team, by identifying the best configuration of the team members. Due to the non-convex nature of the problem, an analytical solution cannot be obtained. The proposed method is based on a new cognitive-based, adaptive optimization algorithm (CAO). This method allows getting coordinated and scalable controls to accomplish the task, even when the obstacles are unknown. Extensive simulations are presented to show the efficiency of the proposed approach

Real-time Collision Risk Estimation based on Stochastic Reachability Spaces

International audienceEstimating the risk of collision with other road users is one of the most important modules to ensure safety in autonomous driving scenarios. In this paper, we propose new probabilistic models to obtain Stochastic Reachability Spaces for vehicles and pedestrians detected in the scene. We then exploit these probabilistic predictions of the road-users' future positions, along with the expected ego-vehicle trajectory, to estimate the probability of collision risk in real-time. The proposed stochastic models only depend on the velocity, acceleration, tracked bounding box, and the class of the detected object. This information can easily be obtained through off-the-shelf 3D object detection frameworks. As a result, the proposed approach for collision risk estimation is widely applicable to a variety of autonomous vehicle platforms. To validate our approach, initially we test the stochastic motion prediction on the KITTI dataset. Further experiments in the CARLA simulator, by reproducing realistic collision scenarios, have the goal of demonstrating the effectiveness of the collision risk assessment and are compared with an alternative approach