Navigate swarms with AEPSO/CAEPSO

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Adham Atyabi Supervisor: Dr. Somnuk Phon-Amnuaisuk Co-supervisor: Dr. Chin Kuan Ho. Navigate swarms with AEPSO/CAEPSO. What is Navigation?. Navigation to steer a course through a medium to steer or manage (a boat) in sailing http://www.merriam-ebster.com/dictionary/navigated - PowerPoint PPT Presentation

Transcript of Navigate swarms with AEPSO/CAEPSO

  • Adham Atyabi

    Supervisor: Dr. Somnuk Phon-Amnuaisuk

    Co-supervisor: Dr. Chin Kuan Ho

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  • Navigation

    to steer a course through a medium

    to steer or manage (a boat) in sailing

    http://www.merriam-ebster.com/dictionary/navigated

    Different navigational techniques have evolved over the ages, all involve in locating one's position compared to known locations or patterns.

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  • (R.Siegwart, I.R. Nourbakhsh, 2004)

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  • The problem is Hostile robotic scenario based on cooperative robots trying to navigate bombs location and disarm them.

    The robots have limited knowledge about the bombs location (only know the likelihood of bombs in the area).

    The likelihood information is uncertain (because of noise and Illusion effects).

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  • To identify design and evaluate strategies for implementing a new Particle Swarm Optimization (PSO) for robot navigation in hazard scenarios and hostile situations.

    To solve the uncertainty in the perception level of the robots/agents in cooperative learning scenario.

    To reduce the proportion of involved robots in the navigation tasks with the aim of reducing costs.

    To solve the initial location dependency in navigation scenarios.

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  • Navigation

    Particle Swarm Optimization (PSO)

    Area Extension (AEPSO) PSO

    Robotic

    Scenarios& Results

    Conclusion

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  • CLASSICAL APPROACHES

    The current developed classic methods are variations of a few general approaches:

    Roadmap (Retraction, Skeleton, or Highway approach)

    Cell Decomposition (CD)

    Potential fields (PF)

    mathematical programming.

    HEURISTICAL APPROACHES

    Artificial Neural Network (ANN)

    Genetic Algorithms (GA)

    Particle Swarm Optimization (PSO)

    Ant Colony (ACO)

    Tabu Search (TS)

    Heuristic algorithms do not guarantee to find a solution, but if they do, are likely to do so much faster than classical methods.

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    (Latombe , 1991, Keil and Sack, 1985, Masehian and Sedighzadeh, 2007, Pugh et al, 2007, Ramakrishnan and Zein-Sabatto, 2001, Hettiarachchi, 2006, Hu et.al, 2007, Liu et.al 2006, Mohamad et. Al 2006, Mclurkin and Yamins, 2005, Ying-Tung et. Al 2004)

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  • Navigation techniques performances are highly dependent to their initialization and reliability of their map.

    According to the literatures, in real robotic domains, a small difference in the starting location of the robots or goals may shows high effect on the overall performance.

    Due to the dynamic, noisy and unpredictable nature of real-world robotic applications, it is quite difficult to implement navigation technique based on a well-known predefined map.

    (Pugh and Zhang,2005, Pugh and Martinoli,2006,2007; Gu et al. , 2003)

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  • Navigation

    Particle Swarm Optimization (PSO)

    Area Extension (AEPSO) PSO

    Robotic

    Scenarios& Results

    Conclusion

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  • PSO is an Evolutionary Algorithm inspired from animal social behaviors. (Kennedy, 1995, Ribeiro and Schlansker, 2005; Chang et al., 2004; Pugh and Martinoli, 2006; Sousa et al., 2003; Nomura,2007)

    PSO outperformed other Evolutionary Algorithms such as GA in some problems (Vesterstrom and Riget, 2002; Ratnaweera et al., 2004; Pasupuleti and Battiti,2006).

    Particle Swarm Optimization (PSO) is an optimization technique which models a set of potential problem solutions as a swarm of particles moving about in a virtual search space. (Kennedy, 1995 )

    The method was inspired by the movement of flocking birds and their interactions with their neighbors in the group. (Kennedy, 1995 )

    PSO achieves optimization using three primary principles:

    Evaluation, where quantitative fitness can be determined for some particle location;

    Comparison, where the best performer out of multiple particles can be selected;

    Imitation, where the qualities of better particles are mimicked by others.

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  • Every particle in the population begins with a randomized position X(i,j) and randomized velocity V(i,j) in the n-dimensional search space. where i represent the particle index and j represents the dimension in the search space

    Each particle remembers the position at which it achieved its highest performance (p).

    Each particle is also a member of some neighborhood of particles, and remembers which particle achieved the best overall position in that neighborhood (g).

    Vij(t)= last Velocity + Cognitive component + Social component

    Vij(t)= w*Vij(t-1) + C1*R1*(pij-xij(t-1)) + C2*R2*(gi-Xij(t-1))

    X(t)= X(t-1)+ V(t)

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  • Single objective domains

    Improvement on neighborhood topology, velocity equation, global best and personal best.

    Multi objective domains:

    Niching PSO, Mutation, Parallelism, Re-initialization, Clearing memory, Using Sub-Swarms

    (Brits, Engelbrecht, and Van Den Bergh, 2002,2003; Yoshida, et al.,2001; Stacey, Jancic and Grundy,2003;Chang, et al., 2005; Vestestrom, Riget, 2002; Qin et al., 2004; Pasupuleti and Battiti, 2006; Ratnaweera et al., 2004;Peram et al., 2003; Parsopoulos and Vrahatis, 2002)

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  • Navigation

    Particle Swarm Optimization (PSO)

    Area Extension (AEPSO) PSO

    Robotic

    Scenarios & Results

    Conclusion

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  • The amount of robots used in literatures are 20 to 300 robots (Lee at al.,2005; Hettiarachchi, 2006; Werfel et al., 2005; Chang et al., 2005; Ahmadabadi et al., 2001; Mondada et al. 2004).

    Robots can use more knowledge (e.g. robots have knowledge about the location of goals and their teammates) (luke et al., 2005; Ahmadabadi et al., 2001; Yamaguchi et al., 1997; Martinson and Arkin, 2003).

    It is commune to train robots individually (Ahmadabadi et al., 2001; Yamaguchi et al, 1997; Hayas et al., 1994).

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  • Parallel Learning in Heterogeneous Multi-Robot Swarms-2007,2006.

    Evaluation in robotic learning is costly even more than the processing of the learning algorithm itself.

    On real robots, sensors and actuators may have slightly different performances due to variations in manufacturing. As a result, multiple robots of the same model may actually perceive and interact with their environment differently, creating a heterogeneous swarm.

    Path planning for mobile robot using the particle swarm optimization with mutation operator-2004.

    Obstacle avoidance with multi-objective optimization by PSO in dynamic environment-2005.

    Robot Path Planning using Particle Swarm Optimization of Ferguson Splines-2006.

    Obstacle-avoidance Path Planning for Soccer Robots Using Particle Swarm Optimization- 2006.

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  • Navigation

    Particle Swarm Optimization (PSO)

    Area Extension (AEPSO) PSO

    Robotic

    Scenarios& Results

    Conclusion

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  • To handle dynamic Velocity

    To handle Direction and Fitness criteria

    To handle Cooperation

    To handle diversity of search:

    To handle Lack of reliable perception (Pugh and Martinoli, 2006; Bogatyreva and Shillerov, 2005):

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  • New velocity heuristic which solved the premature convergence

    Credit Assignment heuristic which solve the cul-de-sacs problem

    Hot Zone/Area heuristic.Different communications ranges condition which provide dynamic neighborhood and sub-swarms

    Help Request Signal which provide cooperation between different sub-swarms

    Boundary Condition heuristic which solve the lack of diversity in basic PSO

    Leave Force which provide the high level of noise resistance.

    Speculation mechanism which provide the high level of noise resistance.

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  • The idea is based on dividing the environment to sub virtual fixed areas with various credits.

    Areas credit defined the proportion of goals and obstacles positioned in the area.

    particles know the credit of first and second layer of its current neighborhood

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  • Robots can only communicate with those who are in their communication range.

    Various communication ranges were used (500, 250, 125, 5 pixels).

    This heuristic has major effect on the sub swarm size.

    Help request signal can provide a chain of connections.

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  • Reward and Punishment

    Suspend factor

    In AEPSO, robots would be suspend each time that they cross boundary lines.

    By this conditions they can escape from the areas that they are stuck in it and it is as useful as reinitializing the robot states in the environment.

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  • The Illusion idea is inspired from our real world perceptions errors and mistakes which can be easily imagined as corrupted data which could be caused by the lack of communication (satellite datas) or even sensation elements (sensors) weaknesses.

    Illusion effect forced approximately over 50% noise to the environment.

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  • It is commune to do the experiences in 2 phases (; Ahmadabadi et al., 2001).

    Training

    Testing

    In the training phase, the suggested training method is important (Individual training or Team based training)

    In the testing phase, there are two different suggestions.

    Use same initialization as the training

    Use different initialization

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  • Speculation mechanism is based on