Mobile robots, be it autonomous or teleoperated, require stable communication with the base station to exchange valuable information. Given the stochastic elements in radio signal propagation, such as shadowing and fading, and the possibilities of unpredictable events or hardware failures, communication loss often presents a significant mission risk, both in terms of probability and impact, especially in Urban Search and Rescue (USAR) operations. Depending on the circumstances, disconnected robots are either abandoned, or attempt to autonomously back-trace their way to the base station. Although recent results in Communication-Aware Motion Planning can be used to effectively manage connectivity with robots, there are no results focusing on autonomously re-establishing the wireless connectivity of a mobile robot without back-tracing or using detailed a priori information of the network. In this paper, we present a robust and online radio signal mapping method using Gaussian Random Fields, and propose a Resilient Communication-Aware Motion Planner (RCAMP) that integrates the above signal mapping framework with a motion planner. RCAMP considers both the environment and the physical constraints of the robot, based on the available sensory information. We also propose a self-repair strategy using RCMAP, that takes both connectivity and the goal position into account when driving to a connection-safe position in the event of a communication loss. We demonstrate the proposed planner in a set of realistic simulations of an exploration task in single or multi-channel communication scenarios.

This paper presents a novel method for sensor-based exploration of unknown environments by a general robotic system equipped with multiple sensors. 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 efficiently guide the search for informative view configurations. Different exploration strategies may be obtained by instantiating the general SET method with different sampling techniques. Two such strategies are presented and compared by simulations in non-trivial 2D and 3D worlds. A completeness analysis of SET is given in the paper.

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.

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.

This paper characterizes the information space of a robot moving in the plane with limited sensing. The robot has a landmark detector, which provides the cyclic order of the landmarks around the robot, and it also has a touch sensor, that indicates when the robot is in contact with the environment boundary. The robot cannot measure any precise distances or angles, and does not have an odometer or a compass. We propose to characterize the information space associated with such robot through the swap cell decomposition. We show how to construct such decomposition through its dual, called the swap graph, using two kinds of feedback motion commands based on the landmarks sensed.

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

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.

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.

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.