Paolo Stegagno

Mutual localization in multi-robot systems using anonymous relative measurements

We propose a decentralized method to perform mutual localization in multi-robot systems using anonymous relative measurements, i.e. measurements that do not include the identity of the measured robot. This is a challenging and practically relevant operating scenario that has received little attention in the literature. Our mutual localization algorithm includes two main components: a probabilistic multiple registration stage, which provides all data associations that are consistent with the relative robot measurements and the current belief, and a dynamic filtering stage, which incorporates odometric data into the estimation process. The design of the proposed method proceeds from a detailed formal analysis of the implications of anonymity on the mutual localization problem. Experimental results on a team of differential-drive robots illustrate the effectiveness of the approach, and in particular its robustness against false positives and negatives that may affect the robot measurement process. We also provide an experimental comparison that shows how the proposed method outperforms more classical approaches that may be designed building on existing techniques. The source code of the proposed method is available within the MLAM ROS stack.

A Semi-autonomous UAV Platform for Indoor Remote Operation with Visual and Haptic Feedback

We present the development of a semi-autonomous quadrotor UAV platform for indoor teleoperation using RGB-D technology as exceroceptive sensor. The platform integrates IMU and Dense Visual Odometry pose estimation in order to stabilize the UAV velocity and track the desired velocity commanded by a remote operator though an haptic interface. While being commanded, the quadrotor autonomously performs a persistent pan-scanning of the surrounding area in order to extend the intrinsically limited field of view. The RGB-D sensor is used also for collision-safe navigation using a probabilistically updated local obstacle map. In the operator visual feedback, pan-scanning movement is real time compensated by an IMU-based adaptive filtering algorithm that lets the operator perform the drive experience in a oscillation-free frame. An additional sensory channel for the operator is provided by the haptic feedback, which is based on the obstacle map and velocity tracking error in order to convey information about the environment and quadrotor state. The effectiveness of the platform is validated by means of experiments performed without the aid of any external positioning system.

Mutual localization in a multi-robot system with anonymous relative position measures

We address the mutual localization problem for a multi-robot system, under the assumption that each robot is equipped with a sensor that provides a measure of the relative position of nearby robots without their identity. Anonymity generates a combinatorial ambiguity in the inversion of the measure equations, leading to a multiplicity of admissible relative pose hypotheses. To solve the problem, we propose a two-stage localization system based on MultiReg, an innovative algorithm that computes on-line all the possible relative pose hypotheses, whose output is processed by a data associator and a multiple EKF to isolate and refine the best estimates. The performance of the mutual localization system is analyzed through experiments, proving the effectiveness of the method and, in particular, its robustness with respect to false positives (objects that look like robots) and false negatives (robots that are not detected) of the measure process.

Distributed target localization and encirclement with a multi robot system

This paper presents a control scheme for localizing and encircling a target using a multi-robot system. The task is achieved in a distributed way, in that each robot only uses local information gathered by on-board relative-position sensors assumed to be noisy, anisotropic, and unable to detect the identity of the measured object. Communication between the robots is provided by limited-range transceivers. Experimental results with stationary and moving targets support the theoretical analysis.

Decentralized multi-robot encirclement of a 3D target with guaranteed collision avoidance

We present a control framework for achieving encirclement of a target moving in 3D using a multi-robot system. Three variations of a basic control strategy are proposed for different versions of the encirclement problem, and their effectiveness is formally established. An extension ensuring maintenance of a safe inter-robot distance is also discussed. The proposed framework is fully decentralized and only requires local communication among robots; in particular, each robot locally estimates all the relevant global quantities. We validate the proposed strategy through simulations on kinematic point robots and quadrotor UAVs, as well as experiments on differential-drive wheeled mobile robots.

3-D mutual localization with anonymous bearing measurements

We present a decentralized algorithm for estimating mutual 3-D poses in a group of mobile robots, such as a team of UAVs. Our algorithm uses bearing measurements reconstructed, e.g., by a visual sensor, and inertial measurements coming from the robot IMU. Since identification of a specific robot in a group would require visual tagging and may be cumbersome in practice, we simply assume that the bearing measurements are anonymous. The proposed localization method is a non-trivial extension of our previous algorithm for the 2-D case [1], and exhibits similar performance and robustness. An experimental validation of the algorithm has been performed using quadrotor UAVs.

Probabilistic mutual localization in multi-agent systems from anonymous position measures

Recent research on multi-agent systems has produced a plethora of decentralized controllers that implicitly assume various degrees of agent localization. However, many practical arrangements commonly taken to allow and achieve localization imply some form of centralization, from the use of physical tagging to allow the identification of the single agent to the adoption of global positioning systems based on cameras or GPS. These devices clearly decrease the system autonomy and range of applicability, and should be avoided if possible. Following this guideline, this work addresses the mutual localization problem with anonymous relative position measures, presenting a robust solution based on a probabilistic framework. The proposed localization system exhibits higher accuracy and lower complexity (O(n2)) than our previous method [1]. Moreover, with respect to more conventional solutions that could be conceived on the basis of the current literature, our method is theoretically suitable for tasks requiring frequent, many-to-many encounters among agents (e.g., formation control, cooperative exploration, multiple-view environment sensing). The proposed localization system has been validated by means of an extensive experimental study.

On the solvability of the Mutual Localization problem with Anonymous Position Measures

This paper formulates and investigates a novel problem called Mutual Localization with Anonymous Position Measures. This is an extension of Mutual Localization with Position Measures, with the additional assumption that the identities of the measured robots are not known. A necessary and sufficient condition for the uniqueness of the solution is presented, which requires O(n2= log n) to be verified and is based on the notion of rotational symmetry in R2. We also derive the relationship between the number of robots and the number of possible solutions, and classify the solutions in a number of equivalence classes which is linear in n. A control law is finally proposed that effectively breaks symmetric formations so as to guarantee unique solvability of the problem is also proposed; its performance is illustrated through simulations.

Obstacle detection, tracking and avoidance for a teleoperated UAV

In this paper, we present a collision-free indoor navigation algorithm for teleoperated multirotor Unmanned Aerial Vehicles (UAVs). Assuming an obstacle rich environment, the algorithm keeps track of detected obstacles in the local surroundings of the robot. The detection part of the algorithm is based on measurements from an RGB-D camera and a Bin-Occupancy filter capable of tracking an unspecified number of targets. We use the estimate of the robots velocity to update the obstacles state when they leave the direct field of view of the sensor. The avoidance part of the algorithm is based on the Model Predictive Control approach. By predicting the possible future obstacles states, it filters the operator commands to prevent collisions. The method is validated on a platform equipped with its own computational unit, which makes it self-sufficient in terms of external CPUs.

Relative localization and identification in a heterogeneous multi-robot system

We develop a localization method for a single-UAV/multi-UGV heterogeneous system of robots. Considering the natural supervisory role of the UAV and the challenging (but realistic) assumption that the UAV-to-UGV measurements do not include the identities of the UGVs, we have adopted the PHD filter as a multi-target tracking technique. However, the standard version of this filter does not take into account odometric information coming from the targets, nor does it solve the problem of estimating their identities. Hence, we design ID-PHD, a modification of the PHD filter that is able to reconstruct the identities of the targets by incorporating odometric data. The proposed localization method has been successfully validated through experiments. Some preliminary results of a localization-based control scheme for the multi-robot system are also presented.