Navigating Agents in Uncertain Environments Using Particle

Swarm Optimization

Adham AtyabiSupervisor: Dr. Somnuk Phon-Amnuaisuk

Co-supervisor: Dr. Chin Kuan Ho

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Navigation

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Problem Statement

This study addresses robot search problems in hostile scenarios. The idea is to use a team of cooperative agents in dynamic and uncertain

environments in which agents tasks are time dependent. Agents’ task is to navigate to survivors' location in the environment. The dynamic nature of the environment causes unpredictability which makes the

environment’s map unreliable. Unreliability of the map makes the predefined planning methods brittle. In such environments, conventional search techniques such as PSO suffer from the

lack of search diversity which leads to a poor search results. In addition, these techniques often have premature convergence problem resulting

in the stagnation of agents. Such stagnation affects the overall performance of the team.

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Objective

To identify, design and evaluate strategies for implementing a new Particle Swarm Optimization (PSO) for robot/agent navigation in uncertain environments.

To handle uncertainty in the perception level of the robots/agents through cooperation of autonomous agents.

To provide better exploration and exploitation in searches. Even though various solutions are suggested to handle the lack of diversity in PSO, our experiments illustrate that controlling the diversity does not guarantee high performance. Although controlling diversity provide balance between exploration and exploitation acts of agents, in such environment (dynamic, uncertain, time dependent), it is necessary to provide an intelligent balance between these two behaviours in a way that each behaviour only be used when it is truly the best behaviour.

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Literature review-1 Navigation in Robotics

1. 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.

2. HEURISTICAL APPROACHES Artificial Neural Network (ANN) Genetic Algorithms (GA) Particle Swarm Optimization (PSO) Ant Colony (ACO)

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

(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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Literature review-2 Difficulties in Conventional Navigating Techniques

• 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 show 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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Literature review-3 Robotic Swarm

• 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).

• Evaluation in robotic learning is costly even more than the processing of the learning algorithm itself (Pugh and Martinoli, 2006,2007).

• 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 (Pugh and Martinoli, 2006,2007).

• 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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Literature review-4 Particle Swarm Optimization

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).

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:1) Evaluation, where quantitative fitness can be determined for some particle location; 2) Comparison, where the best performer out of multiple particles can be selected; 3) Imitation, where the qualities of better particles are mimicked by others.

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Literature review-5 Particle Swarm Optimization

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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MethodologyParticle Swarm Optimization with Area Extension

1. To improve Velocity2. To handle Direction and Fitness criteria3. To handle Cooperation4. To handle diversity of search5. To handle Lack of reliable perception (Pugh and Martinoli,

2006; Bogatyreva and Shillerov, 2005):

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AEPSO / CAEPSO

1. New velocity heuristic which solved the premature convergence

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

3. Environment Reduction heuristic and Different communications ranges condition which provide dynamic neighborhood and sub-swarms

4. Help Request Signal which provide cooperation between different sub-swarms

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

6. Leave Force which provide the high level of noise resistance.7. Speculation mechanism which provide the high level of noise resistance.

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New Velocity Adjustment Equation

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Environment Reduction Heuristic

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 layers of their current neighborhood

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Communication Methodology and Help Request Signal Heuristics

• 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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Credit Assignment and Boundary Condition Heuristics

• 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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Uncertainty (Random Noise and Illusion Effect)

• 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 data’s) or even sensation elements (sensors) weaknesses.

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

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Cooperative Learning

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

1. Training2. 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.1. Use same initialization as the training2. Use different initialization

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Speculation Mechanism and Leave Force Heuristics

• Speculation mechanism is based on using an extra memory in robots called Mask.

• Masks can take values by:1. Illusion effect.2. Robots self observation.3. Self Speculation.4. Neighbor’s observation.5. Neighbors Speculation.• Leave Force is an extra punishment which will force robots to

decrease 10% of their current area’s credit after certain iteration.

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Employed Constraints

• Static Scenario. The scenario is used to examine:1. The feasibility of Basic PSO as movement controller of a swarm of robots in survivors rescuing

missions. 2. The effects of various parameter adjustment, swarm size, and population density on basic PSO3. To comprise the results of basic PSO, random search, linear search and AEPSO.

• Dynamic scenario. The scenario is used to examine the feasibility of AEPSO in dynamic environments.

• Time dependent scenario. The scenario is used to examine the impact of 1. Uncertainty (random noise)2. Time dependency

• Cooperative learning scenario: The scenario is used to examine the effects of1. Homogeneity/Heterogeneity2. Various Past-Knowledge quality and Initialization setup3. Uncertainty4. Time dependency

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Simulations

• Simulation 1. Used to examine feasibility of Basic PSO in static environment. Examined elements i) parameter adjustment ii) swarm size iii) population density.

• Simulation 2. The simulation is used to examine the feasibility of AEPSO in static and dynamic environments using population density scenario.

• Simulation 3. The simulation is used to examine the feasibility of AEPSO in static and dynamic environments under constraints such as population density, Uncertainty (random noise) and Time dependency.

• Simulation 4: The simulation is used to examine the feasibility of AEPSO and CAEPSO in static environment under constraints such as Uncertainty (Illusion effect), Homogeneity/Heterogeneity, Various Past-Knowledge quality, various Initialization setup, and Time dependency.

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Parameter Adjustments in Simulations

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PSO’s variation

PSO Type Used Inertia Weight Acceleration Coefficients

AEPSO LDIWa: w1= 0.2, w2=1 FACb:C1=0.5, C2=2.5

Basic PSO1 LDIW: w1= 0.2, w2=1 FAC:C1=0.5, C2=2.5

Basic PSO2 FIWc: w=0.729844 FAC: C1=0.5, C2=2.5

Basic PSO3 RANDIWd: w[0, 1] FAC:C1=0.5, C2=2.5

a: Linearly Decreasing Inertia Weightb: Fixed Acceleration Coefficients c: Fixed Inertia Weight d: Random Inertia Weight

221 max

)(max)( w

iter

titerwww

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Simulations’ Results

In this study, results are presented based on:

1. How fast teams were able to accomplish the missions

2. How many survivors have been rescued by agents

3. How many survivors have been eliminated in missions

4. Trajectory trace of robots.

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Simulation 1:Effects of Parameter Adjustment on Basic PSO

(a) (b)

Comparison of PSO's results in static environment.

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Simulation 1:Effects of Swarm-Size on Basic PSO

(a) (b)

Effect of swarm-size on basic PSO in static environment.

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Simulation 1:Effects of Population Density on Basic PSO

(a)

(b)

Effect of population density on various basic PSOs in static environment.

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Basic PSO vs. AEPSO

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Simulation 2:AEPSO vs. PSO

(a) (b)

Comparison of AEPSO's performance in static and dynamic environments.

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Simulation 3:AEPSO vs. Time dependency and Random Noise

AEPSO's effectiveness in accomplishing missions in various environments.

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Achievements-1

AEPSO performed better search compare with variation of Basic PSO, Random search and Linear search.

AEPSO performed well in dynamic environment and the results were reliable in noisy environment.

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AEPSO vs. CAEPSO

AEPSO

CAEPSO

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Achievements-2

CAEPSO achieved to reliable results with homogeneous robots due to its ability to reduce the effect of illusion from the environment

Results shows that CAEPSO achieved to 99% performance with same initialization and it also achieved to 97% performance with new initialization (homogeneous scenario).

More accurate past knowledge helped agents controlled by CAEPSO to accomplish their missions faster.

CAEPSO achieved to 95% performance with heterogeneous robots. CAEPSO was able to reduce the illusion effect from the environment and

improve its movements.

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Advantages of AEPSO/CAEPSO

AEPSO showed better movements compare with Basic PSO.

AEPSO achieved to reliable results with only 5 robots which is a big advantage compare with surveys.

AEPSO/CAEPSO proved their efficiency in complex scenarios based on navigation in hostile situations.

CAEPSO achieved to reliable results in homogeneous scenario with new and same initialization constrains.

CAEPSO achieved to reliable results with heterogeneous robots.

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Contributions

AEPSO performed better local search compare with other techniques (Basic PSO, Random Search, Linear Search).

AEPSO and CAEPSO are robust to Noise and Time dependency.

Cooperation between agents allowed CAEPSO to perform well.

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Conclusion and Future work

• In this study, we introduced AEPSO as a new modified version of Basic PSO and we also investigated its effectiveness on static, dynamic, time dependent and uncertain problem domains.

• It is necessary to be mentioned that the small number of particles (only 5 robots) gave a great advantage to AEPSO (due to being able to reduce the costs).

• Robots were able to solve problems with high level of complexities based on using poor level of knowledge (training knowledge) and high level of cooperation and experience sharing.

• It would be useful to compare CAEPSO results with a behaviour-based version of q-learning in a Cooperative Learning scenario with Heterogeneous robots.

• It would be helpful to examine AEPSO and CAEPSO performance on team of real robots in real world.

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Publications1. Atyabi, A., Amnuaisuk, S. P., Ho, C. K., (2008a), Effectiveness of a cooperative learning

version of AEPSO in homogeneous and heterogeneous multi robot learning scenario. The 2008 World Congress on Computational Intelligence (WCCI 2008), pp. 3889 - 3896.

2. Atyabi, A., Amnuaisuk, S. P., (2007), Particle swarm optimization with area extension (AEPSO). IEEE Congress of Evolutionary Computation CEC'07, Stamford University, Singapore, pp. 1970-1976.

3. Atyabi, A., Amnuaisuk, S. P., Ho, C. K., (2007a), Effects of communication range, noise and help request signal on particle swarm optimization with area extension (AEPSO). The 2007 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology ACM, pp. 85-88.

4. Atyabi, A., amnuaisuk, S. P., Ho, C. K., (2007b), Particle Swarm Optimizations: A Critical Review, Conf IKT07, Third conference of Information and Knowledge Technology, Ferdowsi University, Iran.

5. Atyabi, A., amnuaisuk, S. P., Ho, C. K., (2008b), Robotic Navigation with PSO, Applied Soft Computing journal, Elsevier, submitted.

6. Atyabi, A., Amnuaisuk, S. P., Ho, C. K., (2008c), Applying area extension PSO in robotic swarm. Journal of Intelligent and Robotic Systems, Springer, submitted.

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Simulation 1- Basic PSO

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Simulation-2 AEPSO Vs. Basic PSO, Random Search, Linear Search

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Simulation-3 AEPSO Vs. Uncertainty, Dynamism, Time Dependency

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Simulation 2:AEPSO vs. Linear Search and random Search

(a) (b)

Comparison of AEPSO's results in static and dynamic environments, linear search, and random search.

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Simulation 3:AEPSO vs. Time dependency and Random Noise

(a) Uncertain dynamic environment (b) Uncertain static environment

Experimental results of AEPSO with different communication ranges in uncertain environments.

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Simulation 3:AEPSO vs. Time dependency and Random Noise

Static environment

Effect of different deserting policies on accomplishing the missions

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Homogeneous scenario(A1,A2,A3,A4)In this simulation, The experiments are focused on the initialization dependency and past knowledge efficiency in CAEPSO.

1: Initialization dependency:

A1: agents experience the same initialization as they used in their training for their testing phase.

A2: agents experience different initialization (new initialization).

2: Impact of Past knowledge quality:

A3: agents use four different tables as past knowledge.

A4: agent's past knowledge in each experiment during the testing phase refers to the knowledge gathered from same environment during the training phase.

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Simulation 4:Experiment A1 and A2: Effectiveness of Initialization

(a) New Initialization - A2 Experiment (b) Same Initialization - A1 Experiment

Comparison of AEPSO’s and CAEPSO's results in terms of rescued survivors with new and same initializations in various experiments.

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Simulation 4:Experiment A3 and A4: Effectiveness of Past Knowledge

(a) Accomplished missions (b) Eliminated survivors

Comparison of CAEPSO's results in A3 and A4 experiments with same initializations during the testing phases.

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Heterogeneous scenario(S1,S2,S3,S4)

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Simulation 4:Experiments with Heterogeneous Agents

(a) Training Phase/ AEPSO (b) Testing Phase/ CAEPSO

Comparison of CAEPSO's results in terms of accomplished missions with various scenarios in both training and testing phase.

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Simulation 4: Experiments with Heterogeneous Agents

(a) Training Phase (b) Testing Phase

Comparison of CAEPSO's results in terms of amount of rescued survivors with various scenarios in both training and testing phase.