Flock inspired area coverage using wireless boid-like sensor agents
- Chibaya, Colin, Bangay, Shaun D
- Authors: Chibaya, Colin , Bangay, Shaun D
- Date: 2008
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/433440 , vital:72970 , 10.1109/UKSIM.2008.102
- Description: Simulated flocking is achievable using three boid rules [13]. We propose an area coverage model inspired by Reynolds’ flocking algorithm, investigating strategies for achieving quality coverage using flocking rules. Our agents are identical and autonomous, using only local sensory information for indirect communication. Upon deployment, agents are in the default separation mode. The cohesion rule would then guarantee that agents remain within the swarm, covering spaces with explored neighbour spaces. Four experiments are conducted to evaluate our model in terms of coverage quality achieved. We firstly investigate agents’ separation speed before the speed with which isolated agents re-organizes is investigated. The third experiment compares coverage quality achieved using our model with coverage quality achieved using random guessing. Finally, we investigate fault tolerance in the event of agents’ failures. Our model exhibits good separation and cohesion speed, achieving high quality coverage. Additionally, the model is fault tolerant and adaptive to agents’ failures.
- Full Text:
- Authors: Chibaya, Colin , Bangay, Shaun D
- Date: 2008
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/433440 , vital:72970 , 10.1109/UKSIM.2008.102
- Description: Simulated flocking is achievable using three boid rules [13]. We propose an area coverage model inspired by Reynolds’ flocking algorithm, investigating strategies for achieving quality coverage using flocking rules. Our agents are identical and autonomous, using only local sensory information for indirect communication. Upon deployment, agents are in the default separation mode. The cohesion rule would then guarantee that agents remain within the swarm, covering spaces with explored neighbour spaces. Four experiments are conducted to evaluate our model in terms of coverage quality achieved. We firstly investigate agents’ separation speed before the speed with which isolated agents re-organizes is investigated. The third experiment compares coverage quality achieved using our model with coverage quality achieved using random guessing. Finally, we investigate fault tolerance in the event of agents’ failures. Our model exhibits good separation and cohesion speed, achieving high quality coverage. Additionally, the model is fault tolerant and adaptive to agents’ failures.
- Full Text:
The relationship between emergence of the shortest path and information value using ant-like agents
- Chibaya, Colin, Bangay, Shaun D
- Authors: Chibaya, Colin , Bangay, Shaun D
- Date: 2008
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/433318 , vital:72961 , https://doi.org/10.1145/1456659.1456663
- Description: Ant-like agents forage between two points. These agents' probabilistic movements are based on the use of two pheromones; one marking trails towards the goal and another marking trails back to the starting point. Path selection decisions are influenced by the relative levels of attractive and repulsive pheromone in each agent's local environment. Our work in [5] evaluates three pheromone perception strategies, investigating path formation speed, quality, directionality, robustness and adaptability under different parameter settings(degree of randomness, pheromone evaporation rate and pheromone diffusion rate).
- Full Text:
- Authors: Chibaya, Colin , Bangay, Shaun D
- Date: 2008
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/433318 , vital:72961 , https://doi.org/10.1145/1456659.1456663
- Description: Ant-like agents forage between two points. These agents' probabilistic movements are based on the use of two pheromones; one marking trails towards the goal and another marking trails back to the starting point. Path selection decisions are influenced by the relative levels of attractive and repulsive pheromone in each agent's local environment. Our work in [5] evaluates three pheromone perception strategies, investigating path formation speed, quality, directionality, robustness and adaptability under different parameter settings(degree of randomness, pheromone evaporation rate and pheromone diffusion rate).
- Full Text:
A probabilistic movement model for shortest path formation in virtual ant-like agents
- Chibaya, Colin, Bangay, Shaun D
- Authors: Chibaya, Colin , Bangay, Shaun D
- Date: 2007
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/433125 , vital:72945 , https://doi.org/10.1145/1292491.1292493
- Description: We propose a probabilistic movement model for controlling ant-like agents foraging between two points. Such agents are all identical, simple, autonomous and can only communicate indirectly through the environment. These agents secrete two types of pheromone, one to mark trails towards the goal and another to mark trails back to the starting point. Three pheromone perception strategies are proposed (Strategy A, B and C). Agents that use strategy A perceive the desirability of a neighbouring location as the difference between levels of attractive and repulsive pheromone in that location. With strategy B, agents perceive the desirability of a location as the quotient of levels of attractive and repulsive pheromone. Agents using strategy C determine the product of the levels of attractive pheromone with the complement of levels of repulsive pheromone. We conduct experiments to confirm directionality as emergent property of trails formed by agents that use each strategy. In addition, we compare path formation speed and the quality of the formed path under changes in the environment. We also investigate each strategy's robustness in environments that contain obstacles. Finally, we investigate how adaptive each strategy is when obstacles are eventually removed from the scene and find that the best strategy of these three is strategy A. Such a strategy provides useful guidelines to researchers in further applications of swarm intelligence metaphors for complex problem solving.
- Full Text:
- Authors: Chibaya, Colin , Bangay, Shaun D
- Date: 2007
- Subjects: To be catalogued
- Language: English
- Type: text , article
- Identifier: http://hdl.handle.net/10962/433125 , vital:72945 , https://doi.org/10.1145/1292491.1292493
- Description: We propose a probabilistic movement model for controlling ant-like agents foraging between two points. Such agents are all identical, simple, autonomous and can only communicate indirectly through the environment. These agents secrete two types of pheromone, one to mark trails towards the goal and another to mark trails back to the starting point. Three pheromone perception strategies are proposed (Strategy A, B and C). Agents that use strategy A perceive the desirability of a neighbouring location as the difference between levels of attractive and repulsive pheromone in that location. With strategy B, agents perceive the desirability of a location as the quotient of levels of attractive and repulsive pheromone. Agents using strategy C determine the product of the levels of attractive pheromone with the complement of levels of repulsive pheromone. We conduct experiments to confirm directionality as emergent property of trails formed by agents that use each strategy. In addition, we compare path formation speed and the quality of the formed path under changes in the environment. We also investigate each strategy's robustness in environments that contain obstacles. Finally, we investigate how adaptive each strategy is when obstacles are eventually removed from the scene and find that the best strategy of these three is strategy A. Such a strategy provides useful guidelines to researchers in further applications of swarm intelligence metaphors for complex problem solving.
- Full Text:
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