MOD* Lite: An Incremental Path Planning Algorithm Taking Care of Multiple Objectives

The need for determining a path from an initial location to a target one is a crucial task in many applications, such as virtual simulations, robotics, and computer games. Almost all of the existing algorithms are designed to find optimal or suboptimal solutions considering only a single objective, namely path length. However, in many real life application path length is not the sole criteria for optimization, there are more than one criteria to be optimized that cannot be transformed to each other. In this paper, we introduce a novel multiobjective incremental algorithm, multiobjective D* lite (MOD* lite) built upon a well-known path planning algorithm, D* lite. A number of experiments are designed to compare the solution quality and execution time requirements of MOD* lite with the multiobjective A* algorithm, an alternative genetic algorithm we developed multiobjective genetic path planning and the strength Pareto evolutionary algorithm.

[1]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[2]  David Furcy,et al.  Lifelong Planning A , 2004, Artif. Intell..

[3]  Bo Gao,et al.  Pareto-optimal coordination of multiple robots with safety guarantees , 2011, Autonomous Robots.

[4]  Sven Koenig,et al.  Efficient Incremental Search for Moving Target Search , 2009, IJCAI.

[5]  Chelsea C. White,et al.  Multiobjective A* , 1991, JACM.

[6]  Zbigniew Tarapata,et al.  Selected Multicriteria Shortest Path Problems: An Analysis of Complexity, Models and Adaptation of Standard Algorithms , 2007, Int. J. Appl. Math. Comput. Sci..

[7]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[8]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[9]  Simon X. Yang,et al.  Dynamic Task Assignment and Path Planning of Multi-AUV System Based on an Improved Self-Organizing Map and Velocity Synthesis Method in Three-Dimensional Underwater Workspace , 2013, IEEE Transactions on Cybernetics.

[10]  Faruk Polat,et al.  A Multi-objective Incremental Path Planning Algorithm for Mobile Agents , 2012, 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.

[11]  Faruk Polat,et al.  Limited-Damage A*: A path search algorithm that considers damage as a feasibility criterion , 2011, Knowl. Based Syst..

[12]  Sven Koenig,et al.  Moving target D* Lite , 2010, AAMAS.

[13]  Demin Xu,et al.  UAV online path planning based on dynamic multiobjective evolutionary algorithm , 2011, Proceedings of the 30th Chinese Control Conference.

[14]  Yan-tao Tian,et al.  Multi-objective path planning for unrestricted mobile , 2009, 2009 IEEE International Conference on Automation and Logistics.

[15]  Beno Benhabib,et al.  A Multirobot Path-Planning Strategy for Autonomous Wilderness Search and Rescue , 2015, IEEE Transactions on Cybernetics.

[16]  Ian M. Mitchell,et al.  Continuous path planning with multiple constraints , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[17]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[18]  Emilio Frazzoli,et al.  Sampling-based algorithms for optimal motion planning , 2011, Int. J. Robotics Res..

[19]  Anthony Stentz,et al.  The Focussed D* Algorithm for Real-Time Replanning , 1995, IJCAI.

[20]  Sven Koenig,et al.  Generalized Fringe-Retrieving A*: faster moving target search on state lattices , 2010, AAMAS.

[21]  Abdul Rauf Baig,et al.  Optimization of Route Planning using Simulated Ant Agent System , 2010 .

[22]  Bo Gao,et al.  Game theory-based negotiation for multiple robots task allocation , 2013, Robotica.

[23]  Eliot Winer,et al.  Path Planning of Unmanned Aerial Vehicles using B-Splines and Particle Swarm Optimization , 2009, J. Aerosp. Comput. Inf. Commun..

[24]  Anthony Stentz,et al.  Optimal and efficient path planning for partially-known environments , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[25]  Maxim Likhachev,et al.  D*lite , 2002, AAAI/IAAI.

[26]  Hamid Haj Seyyed Javadi,et al.  Using Particle Swarm Optimization for Robot Path Planning in Dynamic Environments with Moving Obstacles and Target , 2009, 2009 Third UKSim European Symposium on Computer Modeling and Simulation.

[27]  Gerrit K. Janssens,et al.  Evolutionary Algorithms for the Multiobjective Shortest Path Problem , 2007 .

[28]  Carsten Maple,et al.  K-Order Surrounding Roadmaps Path Planner for Robot Path Planning , 2014, J. Intell. Robotic Syst..

[29]  Richard E. Korf,et al.  Real-Time Heuristic Search , 1990, Artif. Intell..

[30]  Panagiotis Tsiotras,et al.  Incremental Multi-Scale Search Algorithm for Dynamic Path Planning With Low Worst-Case Complexity , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[31]  Oscar Castillo,et al.  Multiple Objective Genetic Algorithms for Path-planning Optimization in Autonomous Mobile Robots , 2006, Soft Comput..

[32]  Carlos A. Coello Coello,et al.  An updated survey of GA-based multiobjective optimization techniques , 2000, CSUR.