Luigi Freda

The Sensor-based Random Graph Method for Cooperative Robot Exploration

We present a decentralized cooperative exploration strategy for a team of mobile robots equipped with range finders. A roadmap of the explored area, with the associate safe region, is built in the form of a sensor-based random graph (SRG). This is expanded by the robots by using a randomized local planner that automatically realizes a tradeoff between information gain and navigation cost. The nodes of the SRG represent view configurations that have been visited by at least one robot, and are connected by arcs that represent safe paths. These paths have been actually traveled by the robots or added to the SRG to improve its connectivity. Decentralized cooperation and coordination mechanisms are used so as to guarantee exploration efficiency and avoid conflicts. Simulations and experiments are presented to show the performance of the proposed technique.

Frontier-Based Probabilistic Strategies for Sensor-Based Exploration

We present a frontier-based modification of the SRT (Sensor-based Random Tree) method, a previously proposed probabilistic strategy for sensor-based exploration of unknown environments by a mobile robot. The idea is to improve the efficiency of the method by biasing the randomized generation of configurations towards unexplored areas. Effective implementations of this strategy are proposed for SRT-Ball and SRT-Star, two instances of the general SRT method corresponding to different perception attitudes and sensing equipments. Comparative simulations are presented to show the benefits of the proposed technique.

The SRT method: randomized strategies for exploration

We present a method for sensor-based exploration of unknown environments by a mobile robot. The method is based on the randomized incremental generation of a data structure called sensor-based random tree (SRT), which represents a roadmap of the explored area with an associated safe region. Different exploration strategies may be obtained by instantiating the general method with different perception techniques. Two such techniques are discussed: the first, conservative and particularly suited to noisy sensors, results in an exploration strategy called SRT-Ball. The second perception technique is more confident, and the corresponding strategy is called SRT-Star. The two strategies are critically compared by simulations as well as by experiments on the MagellanPro robot.

Vision-based interception of a moving target with a nonholonomic mobile robot

A novel vision-based scheme is presented for driving a nonholonomic mobile robot to intercept a moving target. The proposed method has a two-level structure. On the lower level, the pan-tilt platform carrying the on-board camera is controlled so as to keep the target as close as possible to the center of the image plane. On the higher level, the relative position of the target is retrieved from its image coordinates and the camera pan-tilt angles through simple geometry, and used to compute a control law which drives the robot to the target. Various possible choices are discussed for the high-level robot controller, and the associated stability properties are rigorously analysed. The proposed visual interception method is validated through simulations as well as experiments on the mobile robot MagellanPro.

A Randomized Strategy for Cooperative Robot Exploration

We present a cooperative exploration strategy for mobile robots. The method is based on the randomized incremental generation of a collection of data structures called sensor-based random trees, each representing a roadmap of an explored area with an associated safe region. Decentralized cooperation and coordination mechanisms are introduced so as to improve the exploration efficiency and to avoid conflicts. Simulations in various environments are presented to show the performance of the proposed technique.

A Randomized Method for Integrated Exploration

We present an integrated exploration strategy for mobile robots. The method is based on the randomized incremental generation of a data structure called sensor-based random tree (SRT), which represents a roadmap of the explored area with an associated safe region. A continuous localization procedure based on natural features of the safe region is integrated in the scheme. Both the information gain and the localization potential are taken into account when evaluating candidate configurations for exploration. Simulations and experiments on the MagellanPro robot show the performance of the proposed technique

Visual servoing of a wheeled mobile robot for intercepting a moving object

We present a vision-based scheme for driving a nonholonomic mobile robot to intercept a moving target. Our method relies on a two-level approach. On the lower level, the pan-tilt platform which carries the on-board camera is controlled so as to keep the target at the center of the image plane. On the higher level, the robot operates under the assumption that the camera system achieves perfect tracking. In particular, the relative position of the ball is retrieved from the pan/tilt angles through simple geometry, and used to compute a control law driving the robot to the target. Various possible choices are discussed for the high-level robot controller. The proposed visual interception method is validated through simulations as well as experiments on the mobile robot MagellanPro.

Sensor-based Exploration for general robotic systems

We present a method for sensor-based exploration of unknown environments by a robotic system equipped with rangefinders. The method is based on the incremental generation of a configuration-space data structure called sensor-based exploration tree (SET). The expansion of the SET is driven by information at the world level, where the perception process takes place. In particular, the frontiers of the explored region are used to guide the search for informative view configurations. Various exploration strategies may be obtained by instantiating the general SET method with different sampling techniques. Two of these are compared by simulations in 2D and 3D worlds.

Learning Combinatorial Map Information from Permutations of Landmarks

This paper considers a robot that moves in the plane and that is able to sense only the cyclic order of landmarks with respect to its current position. No metric information is available regarding the robot or landmark positions; moreover, the robot does not have a compass or odometers (i.e., knowledge of coordinates). We carefully study the information space of the robot, and establish its capabilities in terms of mapping the environment and accomplishing tasks, such as navigation and patrolling. When the robot moves exclusively inside the perimeter of the set of landmarks, the information space may be succinctly characterized as an order type that provides information powerful enough to determine which points lie inside the convex hulls of subsets of landmarks. In addition, if the robot is allowed to move outside the perimeter of the set of landmarks, the information space is described with a swap cell decomposition, that is, an aspect graph in which each aspect is a cyclic permutation of landmarks. Finally, we show how to construct such decomposition through its dual, using two kinds of feedback motion commands based on the landmarks sensed.

Deployment of ground and aerial robots in earthquake-struck Amatrice in Italy (brief report)

We provide key facts about the TRADR project deployment of ground and aerial robots in Amatrice, Italy, after the major earthquake in August 2016. The robots were used to collect data for 3D textured models of the interior and exterior of two badly damaged churches of high national heritage value.