Enhancing Exploration in Graph-like Worlds

Lecture Notes in Electrical Engineering, 2015

This paper addresses the problem of decentralized exploration and mapping of unknown environment by a multiple robot team. The exploration methodology relies on individual decision rules and communication of topological maps to achieve efficient and fast mapping, minimizing overlap of explored space. This distributed solution allows scalability of the proposed methods. Each robot broadcasts a graph representing the topological map, with information of exploration status of each region. Therefore, this kind of information can be transmitted to robots that are not in the communication range, through other robots in a multi-hop network. This work has been tested in simulation, and the results demonstrate the performance improvements and robustness that arise from our multirobot approach to exploration.

2007

In this paper, we used an alternative to the frequently used quadrangular grid map as representation of the world. This alternative uses an hexagonal grid instead a quadrangular grid to represent the world where simulated robots explore. Both representations used an occupancy grid map system, where the world is divided by many cells (hexagons or squares) and only one element can be in the cell at the same time. We made several experiments to observe how exploration efficiency is affected using both representations and four different exploration algorithms. Experiments were made in two scenarios. The first scenario tested the exploration efficiency of a single robot using different number of random obstacles. The second scenario tested the exploration efficiency using different number of cooperative robots leaving the number of random obstacles as a constant. We used SimER1 software[1] to make all exploration experiments in a simulation environment. Experiments results showed that hexagonal grid map representation was better than quadrangular grid map representation if the world is very big or if the exploration algorithm is not very efficient. Improvement in efficiency could be small if the exploration algorithm is very efficient, cooperation among exploration robots is very good, the area to explore is small or if the number of exploration robots is big. Nevertheless, even if improvement in efficiency is small, the variance in hexagonal representation is lesser. Reduction of variance in consecutive exploration experiments is an advantage because we are interested in explorations that always have a good degree of efficiency.

Automatika ‒ Journal for Control, Measurement, Electronics, Computing and Communications, 2014

Original scientific paper This paper addresses the problem of exploration of an unknown environment by developing effective exploration strategies for a team of mobile robots equipped with continuously rotating 3D scanners. The main aim of the new strategies is to reduce the exploration time of unknown environment. Unlike most of other published works, to save time, the laser scanners rotate and scan the environment while robots are in motion. Furthermore, the new strategies are able to explore large outdoor environments as a considerable reduction of the required computations, especially those required for path planning, have been achieved. Moreover, another new exploration strategy has been developed so that robots continuously replan the order to visit the remaining unexplored areas according to the new data (i.e. updated map) collected by the robot in question or by the other team members. This new extension led to further enhancements over the above mentioned ones, but with slightly higher computational costs. Finally, to assess our new exploration strategies with different levels of environment complexity, new set of experiments were conducted in environments where obstacles are distributed according to the Hilbert curve. The results of these experiments show the effectiveness of the proposed technique to effectively distribute the robots over the environment. More importantly, we show how the optimal number of robots is related to the environment complexity.

Distributed Autonomous Robotic Systems 4, 2000

Ra, 1998

This paper describes a technique whereby a group of mobile autonomous agents explores an unknown graph-like environment and constructs a topological map of it. The key idea is that the mobile agents start at a common node of the environment, explore independently (using a previously published algorithm) and agree to meet after a speci ed number of moves to exchange information in order to augment each other`s partial map of the world. Just after each exchange, robots have the same map of the world, which is a superset of each robot`s map just before the exchange. The process of harmonizing each other`s maps involves a combination of reasoning (for the areas that consist of paths common to all partial maps before the exchange) and physical movements of the robots, to check whether certain nodes in one map are identical to nodes in the rest of the maps.

IEEE Transactions on Robotics, 2011

In this paper we present an algorithm for the exploration of an unknown graph by multiple robots, which is never worse than depth-first search with a single robot. On trees, we prove that the algorithm is optimal for two robots. For k robots, the algorithm has an optimal dependence on the size of the tree, but not on its radius. We believe that the algorithm performs well on any tree, and this is substantiated by simulations. For trees with e edges and radius r, the exploration time is less than 2e k + (1 + k r) k−1 2 k! r k−1 = 2e k + O((k + r) k−1) (for r > k, ≤ 2e k + 2r k−1), improving a recent method with time O(e log k + r) [2], and almost reaching the lower bound max(2e k , 2r). The model underlying undirected graph exploration is a set of rooms connected by opaque passages; thus the algorithm is appropriate for scenarios like indoor navigation or cave exploration. In this framework, communication can be realized by bookkeeping devices being dropped by the robots at explored vertices, the states of which are read and changed by further visiting robots. Simulations have been performed in both tree and graph exploration to corroborate the mathematical results.

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017

In this paper, we present a complete algorithm for exploration of unknown environments containing disjoint obstacles with multiple robots. We propose a distributed approach considering two main cases. In the first one, the obstacles are distinguishable, i.e., each obstacle is uniquely identifiable, which can be imagined as each obstacle having a different color, and in the second case, the obstacles are not distinguishable. Two possible applications of our algorithms are: 1) Search of a static object in an unknown environment. 2) Damage verification in unknown environments composed by multiple elements (e.g. buildings). The main contributions of this work are the following: 1) The algorithms guarantee exploring the whole environment in finite time even though each robot does not have full information about the part of the environment explored by other robots. 2) The method only requires limited communication between the robots. In both cases distinguishable and indistinguishable obstacles, the robots communicate only at rendezvous. 3) The algorithm scales well to hundreds of robots and obstacles. We tested in several simulations the performance of our algorithms for both cases, in terms of the distance traveled by the robots.

Journal of Intelligent & Robotic Systems

In this paper, we present a complete algorithm for exploration of unknown environments containing disjoint obstacles with multiple robots. We propose a distributed approach considering several variants. The robots are modeled as points or discs, the obstacles are distinguishable or they are not distinguishable, the point robots only communicate at rendezvous, the discshaped robots can communicate if they are visible to each other, finally the free subset of the configuration space has one or several connected components. Two possible applications of our algorithms are: 1) Search of a static object in an unknown environment. 2) Damage verification in unknown environments composed by multiple elements (e.g. buildings). The main contributions of this work are the following: 1) The algorithms guarantee exploring the whole environment in finite time even though the robots are no capable of building an exact map of the environment, they cannot estimate their positions and each robot does not have full information about the part of the environment explored by other robots. 2) The method only requires limited communication between the robots. 3) We combine and extend the velocity obstacle method with our proposed approach to explore the environment using disc-shaped robots that are able to avoid collisions with both moving and static obstacles. 4) We propose an exploration strategy such that even if the configuration space has several connected components this strategy guarantees covering the largest possible portion of the environment with an omnidirectional sensor detecting the visibility regions. 5) The algorithm scales well to hundreds of robots and obstacles. We tested in several simulations the performance of our algorithms using different performance metrics.

2000

In this paper we consider the problem of exploring an unknown environment by a team of robots. As in single-robot exploration the goal is to minimize the overall exploration time. The key problem to be solved therefore is to choose appropriate target points for the individual robots so that they simultaneously explore different regions of their environment. We present a probabilistic approach for the coordination of multiple robots which, in contrast to previous approaches, simultaneously takes into account the costs of reaching a target point and the utility of target points. The utility of target points is given by the size of the unexplored area that a robot can cover with its sensors upon reaching a target position. Whenever a target point is assigned to a specific robot, the utility of the unexplored area visible from this target position is reduced for the other robots. This way, a team of multiple robots assigns different target points to the individual robots. The technique has been implemented and tested extensively in real-world experiments and simulation runs. The results given in this paper demonstrate that our coordination technique significantly reduces the exploration time compared to previous approaches.

IEEE/ASME Transactions on Mechatronics, 2009

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.