Nicolas Jouandeau

A Survey and Analysis of Multi-Robot Coordination

In the field of mobile robotics, the study of multi-robot systems (MRSs) has grown significantly in size and importance in recent years. Having made great progress in the development of the basic problems concerning single-robot control, many researchers shifted their focus to the study of multi-robot coordination. This paper presents a systematic survey and analysis of the existing literature on coordination, especially in multiple mobile robot systems (MMRSs). A series of related problems have been reviewed, which include a communication mechanism, a planning strategy and a decision-making structure. A brief conclusion and further research perspectives are given at the end of the paper.

A Parallel Monte-Carlo Tree Search Algorithm

Monte-Carlo Tree Search is a powerful paradigm for the game of Go. In this contribution we present a parallel Master-Slave algorithm for Monte-Carlo Tree Search and test it on a network of computers using various configurations: from 12,500 to 100,000 playouts, from 1 to 64 slaves, and from 1 to 16 computers. On our own architecture we obtain a speedup of 14 for 16 slaves. With a single slave and five seconds per move our algorithm scores 40.5% against GNU Go , with sixteen slaves and five seconds per move it scores 70.5%. At the end we give the potential speedups of our algorithm for various playout times.

Solving breakthrough with Race Patterns and Job-Level Proof Number Search

breakthrough is a recent race-based board game usually played on a 8×8 board. We describe a method to solve 6×5 boards based on (1) race patterns and (2) an extension of (JLPNS).

Walking patterns for real time path planning simulation of humanoids

We present here a detailed description of the walking algorithm that was designed for 3D simulation of locomotion and path planning of humanoid robots. The walking patterns described were implemented on NAO humanoid models that are used in the 3D simulation league of RoboCup to play soccer. The locomotion algorithm is based on the well known 3D-LIP model that consists of defining walking primitives of the center of mass, keeping its height constant and assuming no torque at the support foot. This paper proposes to detail how to connect the walking primitives, especially at the start of the walk. The second added value of this work resides in the rotation walking primitives that are generated differently from the linear translation walking primitives. This enables the robot to achieve fast rotation on the spot or about a center located on the longitudinal axis. The paper also addresses the issue of re-entrance, i.e. how to take into account a new walking request in real time without waiting for the end of the current walk.

Parallel Nested Monte-Carlo search

We address the parallelization of a Monte-Carlo search algorithm. On a cluster of 64 cores we obtain a speedup of 56 for the parallelization of Morpion Solitaire. An algorithm that behaves better than a naive one on heterogeneous clusters is also detailed.

Multi-robot heuristic goods transportation

In this paper, we consider the issue of transporting a certain number of goods by a team of mobile robots. The target is to minimize the total transportation time and keep a low energy consumption of the intelligent agents on assuring security and quality during the transportation process. The pivotal issue needs to be solved is how to assign tasks to individual robots in a more reasonable and efficient way. We present a novel solution by using an empirical-based heuristic planning strategy for the goods transportation by multiple robots. In contrast to previous approaches, this strategy is designed to plan the transportation task for each individual robot by estimating the production rate of goods based on multi-robot coordination. Our approach has been implemented and evaluated in simulation. The experimental results demonstrate that the completion time of the whole transportation mission can be significantly reduced and the energy consumption of robots can be kept at a low level of our heuristic planning strategy compared with the previous approach.

Material Symmetry to Partition Endgame Tables

Many games display some kind of material symmetry. That is, some sets of game elements can be exchanged for another set of game elements, so that the resulting position will be equivalent to the original one, no matter how the elements were arranged on the board. Material symmetry is routinely used in card game engines when they normalize their internal representation of the cards.

Monte-Carlo Tree Reductions for Stochastic Games

Monte-Carlo Tree Search (MCTS) is a powerful paradigm for perfect information games. When considering stochastic games, the tree model that represents the game has to take chance and a huge branching factor into account. As effectiveness of MCTS may decrease in such a setting, tree reductions may be useful. Chance-nodes are a way to deal with random events. Move-groups are another way to deal efficiently with a large branching factor by regrouping nodes. Group-nodes are regrouping only reveal moves and enable a choice between reveal moves and classical moves. We present various policies to use such reductions for the stochastic game Chinese Dark Chess. Move-groups, chance-nodes and group-nodes are compared.

S-MASA: A stigmergy based algorithm for multi-target search

We explore the on-line problem of coverage where multiple agents have to find a target whose position is unknown, and without a prior global information about the environment. In this paper a novel algorithm for multi-target search is described, it is inspired from water vortex dynamics and based on the principle of pheromone-based communication. According to this algorithm, called S-MASA (Stigmergic Multi Ant Search Area), the agents search nearby their base incrementally using turns around their center and around each other, until the target is found, with only a group of simple distributed cooperative Ant like agents, which communicate indirectly via depositing/detecting markers. This work improves the search performance in comparison with random walk and S-random walk (stigmergic random walk) strategies, we show the obtained results using computer simulations.

Small and large MCTS playouts applied to Chinese Dark Chess stochastic game

Monte-Carlo Tree Search is a powerful paradigm for deterministic perfect-information games. We present various changes applied to this algorithm to deal with the stochastic game CHINESE DARK Chess. We experimented with group nodes and chance nodes using various configurations: with different playout policies, with different playout lengths, with true or estimated wins. Results show that extending playout length over the real draw condition is beneficial to group nodes and to chance nodes. It also shows that using an evaluation function can reduce the number of draw games with group nodes and can be increased with chance nodes.