Voronoi-based space partitioning for coordinated multi-robot exploration

Recent multi-robot exploration algorithms usually rely on occupancy grids as their core world representation. However, those grids are not appropriate for environments that are very large or whose boundaries are not well delimited from the beginning of the exploration. In contrast, polygonal representations do not have such limitations. Previously, the authors have proposed a new exploration algorithm based on partitioning unknown space into as many regions as available robots by applying K-Means clustering to an occupancy grid representation, and have shown that this approach leads to higher robot dispersion than other approaches, which is potentially beneficial for quick coverage of wide areas. In this paper, the original K-Means clustering applied over grid cells, which is the most expensive stage of the aforementioned exploration algorithm, is substituted for a Voronoi-based partitioning algorithm applied to polygons. The computational cost of the exploration algorithm is thus significantly reduced for large maps. An empirical evaluation and comparison of both partitioning approaches is presented.This work is partially supported by the Government of Spain under MCYT DPI2004-07993-C03-03. Ling Wu is supported by a FPI scholarship from the Spanish Ministry of Education and Science

Improved Trade-based Multi-robot Coordination

International audienceTeam work is essential to multiple mobile robot systems. An important question is, which robot should implement which action? In our previous work, we presented a trade-based task allocation approach for coordinated multi-robot exploration, which simulates the relationship between buyers and sellers in a business system, to achieve dynamic task allocation by using a mechanism of unsolicited bid. This paper still addresses the problem of coordinating multi-robot exploration while presents an improved trade-based approach to raise the efficiency of task allocation by using the Hungarian method. The proposed approach has been implemented and evaluated in simulation. The experimental results demonstrate the total exploration time can be significantly reduced by the improved trade-based approach compared to previous approaches

ACS-PRM: Adaptive Cross Sampling Based Probabilistic Roadmap for Multi-robot Motion Planning

International audienceIn this paper we present a novel approach for multi-robot motion planning by using a probabilistic roadmap (PRM) based on adaptive cross sampling (ACS). The proposed approach, we call ACS-PRM, consists of three steps, which are C-space sampling, roadmap building and motion planning. Firstly, an adequate number of points should be generated in C-space on an occupancy grid map by using an adaptive cross sampling method. Secondly, a roadmap should be built while the potential targets and the milestones are extracted by second learning the result of sampling. Finally, the motion of robots should be planned by querying the constructed roadmap. In contrast to previous approaches, our ACS-PRM approach is designed to plan separate kinematic paths for multiple robots to minimize the problem of congestion and collision in an effective way so as to improve the planning efficiency. Our approach has been implemented and evaluated in simulation. The experimental results demonstrate the total planning time can be significantly reduced by our ACS-PRM approach compared with previous approaches

DECENTRALIZED MULTI-ROBOT PLANNING TO EXPLORE AND PERCEIVE

In a recent French robotic contest, the objective was to develop a multi-robot system able to autonomously map and explore an unknown area while also detecting and localizing objects. As a participant in this challenge, we proposed a new decentralized Markov decision process (Dec-MDP) resolution based on distributed value functions (DVF) to compute multi-robot exploration strategies. The idea is to take advantage of sparse interactions by allowing each robot to calculate locally a strategy that maximizes the explored space while minimizing robots interactions. In this paper, we propose an adaptation of this method to improve also object recognition by integrating into the DVF the interest in covering explored areas with photos. The robots will then act to maximize the explored space and the photo coverage, ensuring better perception and object recognition

Modelling and Verification of Multiple UAV Mission Using SMV

Model checking has been used to verify the correctness of digital circuits, security protocols, communication protocols, as they can be modelled by means of finite state transition model. However, modelling the behaviour of hybrid systems like UAVs in a Kripke model is challenging. This work is aimed at capturing the behaviour of an UAV performing cooperative search mission into a Kripke model, so as to verify it against the temporal properties expressed in Computation Tree Logic (CTL). SMV model checker is used for the purpose of model checking

From Monocular SLAM to Autonomous Drone Exploration

Micro aerial vehicles (MAVs) are strongly limited in their payload and power capacity. In order to implement autonomous navigation, algorithms are therefore desirable that use sensory equipment that is as small, low-weight, and low-power consuming as possible. In this paper, we propose a method for autonomous MAV navigation and exploration using a low-cost consumer-grade quadrocopter equipped with a monocular camera. Our vision-based navigation system builds on LSD-SLAM which estimates the MAV trajectory and a semi-dense reconstruction of the environment in real-time. Since LSD-SLAM only determines depth at high gradient pixels, texture-less areas are not directly observed so that previous exploration methods that assume dense map information cannot directly be applied. We propose an obstacle mapping and exploration approach that takes the properties of our semi-dense monocular SLAM system into account. In experiments, we demonstrate our vision-based autonomous navigation and exploration system with a Parrot Bebop MAV