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DSSA+: distributed collision avoidance algorithm in an environment where both course and speed changes are allowed

Hirayama K., Miyake K., Shiotani T., Okimoto T.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

2019

Vol. 13, no. 1

117--123

Distributed Stochastic Search Algorithm (DSSA) is one of state-of-the-art distributed algorithms for the ship collision avoidance problem. In DSSA, whenever a ship encounters with any number of other ships (neighboring ships), she will select her course with a minimum cost after coordinating their decisions with her neighboring ships. The original DSSA assumes that ships can change only their courses while keeping their speed considering kinematic properties of ships in general. However, considering future possibilities to address more complex situations that may cause ship collision or to deal with collision of other vehicles (such as mobile robots or drones), the options of speed changes are necessary for DSSA to make itself more flexible and extensive. In this paper, we present DSSA+, as a generalization of DSSA, in which speed change are naturally incorporated as decision variables in the original DSSA. Experimental evaluations are provided to show how powerful this generalization is.

Leximin Multiple Objective DCOPs on Factor Graphs for Preferences of Agents

Matsui T., Silaghi M., Okimoto T., Hirayama K., Yokoo M., Matsuo H.

Fundamenta Informaticae

2018

Vol. 158, nr 1/3

63--91

Distributed Constraint Optimization Problem (DCOP) has been studied as a fundamental component of multiagent systems. With DCOPs, various applications on multiagent systems are formalized as constraint optimization problems where variables and functions are distributed among agents. Leximin AMODCOP has been proposed as a class of Multiple Objective DCOPs, where multiple objectives for individual agents are optimized based on the leximin operator. This problem also relates to Asymmetric DCOPs based on its the criteria of fairness among agents. Previous studies explore only Leximin AMODCOPs on constraint graphs limited to functions with unary or binary scopes. We address the Leximin AMODCOPs on factor graphs that directly represent n-ary functions. A dynamic programming method on factor graphs is investigated as an exact solution method. In addition, for relatively dense problems, we also investigate several approximate/inexact algorithms.