13 research outputs found

    Self-limitation, dynamic and flexible approaches for particle swarm optimisation

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    Swarm Intelligence (SI) is one of the prominent techniques employed to solve optimisation problems. It has been applied to problems pertaining to engineering, schedule, planning, networking and design. However, this technique has two main limitations. First, the SI technique may not be suitable for the online applications, as it does not have the same aspects of limitations as an online platform. Second, setting the parameter for SI techniques to produce the most promising outcome is challenging. Therefore, this research has been conducted to overcome these two limitations. Based on the literature, Particle Swarm Optimisation (PSO) was selected as the main SI for this research, due to its proven performances, abilities and simplicity. Five new techniques were created based on the PSO technique in order to address the two limitations. The first two techniques focused on the first limitation, while the other three techniques focused on the latter. Three main experiments (benchmark problems, engineering problems, path planning problems) were designed to assess the capabilities and performances of these five new techniques. These new techniques were also compared against several other well-established SI techniques such as the Genetic Algorithm (GA), Differential Equation (DE) and Cuckoo Search Algorithm (CSA). Potential Field (PF), Probabilistic Road Map (PRM), Rapidly-explore Random Tree (RRT) and Dijkstra’s Algorithm (DA) were also included in the path planning problem in order to compare these new techniques’ performances against Classical methods of path planning. Results showed all five introduced techniques managed to outperform or at least perform as good as well-established techniques in all three experiments

    Multiple robot co-ordination using particle swarm optimisation and bacteria foraging algorithm

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    The use of multiple robots to accomplish a task is certainly preferable over the use of specialised individual robots. A major problem with individual specialized robots is the idle-time, which can be reduced by the use of multiple general robots, therefore making the process economical. In case of infrequent tasks, unlike the ones like assembly line, the use of dedicated robots is not cost-effective. In such cases, multiple robots become essential. This work involves path-planning and co-ordination between multiple mobile agents in a static-obstacle environment. Multiple small robots (swarms) can work together to accomplish the designated tasks that are difficult or impossible for a single robot to accomplish. Here Particle Swarm Optimization (PSO) and Bacteria Foraging Algorithm (BFA) have been used for coordination and path-planning of the robots. PSO is used for global path planning of all the robotic agents in the workspace. The calculated paths of the robots are further optimized using a localised BFA optimization technique. The problem considered in this project is coordination of multiple mobile agents in a predefined environment using multiple small mobile robots. This work demonstrates the use of a combinatorial PSO algorithm with a novel local search enhanced by the use of BFA to help in efficient path planning limiting the chances of PSO getting trapped in the local optima. The approach has been simulated on a graphical interface

    Multi-Agent Path Planning for Locating a Radiating Source in an Unknown Environment

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    A situation is addressed in which multiple autonomous agents are used to search an unknown environment for a target, the position and orientation of which is known with respect to each agent. A controlling framework is proposed to inform and coordinate the agents’ movements in order to reduce the time required to locate the target. Four primary variables are considered: the cost function used to select the agents’ paths, the number of agents in a given scenario, the distance over which the agents are assumed to communicate, and the size of the environment in which the agents are operating. It was found that a cost function that balances progress toward the target with exploration of the environment is generally most effective for all combinations of the other variables. More agents and greater communication are beneficial, to a point, in larger environments, although these may be less effective in smaller ones

    Swarm robotics: Cooperative navigation in unknown environments

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    Swarm Robotics is garnering attention in the robotics field due to its substantial benefits. It has been proven to outperform most other robotic approaches in many applications such as military, space exploration and disaster search and rescue missions. It is inspired by the behavior of swarms of social insects such as ants and bees. It consists of a number of robots with limited capabilities and restricted local sensing. When deployed, individual robots behave according to local sensing until the emergence of a global behavior where they, as a swarm, can accomplish missions individuals cannot. In this research, we propose a novel exploration and navigation method based on a combination of Probabilistic Finite Sate Machine (PFSM), Robotic Darwinian Particle Swarm Optimization (RDPSO) and Depth First Search (DFS). We use V-REP Simulator to test our approach. We are also implementing our own cost effective swarm robot platform, AntBOT, as a proof of concept for future experimentation. We prove that our proposed method will yield excellent navigation solution in optimal time when compared to methods using either PFSM only or RDPSO only. In fact, our method is proved to produce 40% more success rate along with an exploration speed of 1.4x other methods. After exploration, robots can navigate the environment forming a Mobile Ad-hoc Network (MANET) and using the graph of robots as network nodes

    Design and implementation of a bristle bot swarm system

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    Swarm robotics focuses on the study and development of robot systems containing a large number of agents that interact with each other in a collective behaviour in order to achieve tasks or overcome obstacles. Bristlebots are vibration-driven mobile robots. They are characterized by small size, high speed, simple design and low costs for production and application – qualities which are advantageous for agents of swarm robotic systems. However, most studies have been developed over systems with no control or systems with two or more actuators. The aim of this master thesis is the development of a bristle based robot agent for a swarm robotics microsystem with units for locomotion, sensing, data processing, control, communication and energy storage. New approaches in modelling and development of swarm agents are given, and a robot prototype is presented. The robot is driven by a single DC motor and uses a bristle system to create locomotion. It should be noted, that within the system design, considerations for the size, weight and minimalist architecture are taken. Experiments are presented and the system’s capabilities for displacement, velocity and trajectory generation are analysed. While the parallel velocity maintains a positive magnitude in both motor rotation directions, the rotation speed and transversal velocity of the robot have opposite directions, creating curved trajectories with opposite orientations. In Frequencies up to 210 Hz, the rotation direction of the robot is maintained while the magnitude slightly varies. However, for higher frequencies, the rotation direction of the robot is reversed, maintaining a similar magnitude. The transversal speeds at this frequency range, maintain their direction but are clearly reduced compared to lower frequencies.Tesi

    Cluster Control of a Multi-Robot Tracking Network and Tracking Geometry Optimization

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    The position of a moving object can be tracked in numerous ways, the simplest of which is to use a single static sensor. However, the information from a single sensor cannot be verified and may not be reliable without performing multiple measurements of the same object. When multiple static sensors are used, each sensor need only take a single measurement which can be combined with other sensor measurements to produce a more accurate position estimate. Work has been done to develop sensors that move with the tracked object, such as relative positioning, but this research takes this concept one step further; this dissertation presents a novel, highly capable strategy for utilizing a multi-robot network to track a moving target. The method optimizes the configuration of mobile tracking stations in order to produce the position estimate for a target object that yields the smallest estimation error, even when the sensor performance varies. The simulations and experiments presented here verify that the optimization process works in the real world, even under changing conditions and noisy sensor data. This demonstrates a simple, robust system that can accurately follow a moving object, as illustrated by results from both simulations and physical experiments. Further, the optimization led to a 6% improvement in the target location estimate over the non-optimized worst-case scenario tested with identical sensors at the nominal fixed radius distance of 2.83 m and even more significant improvements of over 90% at larger radial distances. This method can be applied to a wider variety of conditions than current methods since it does not require a Kalman filter and is able to find an optimal solution for the fixed radius case. To make this optimization method even more useful, it is proposed to extend the mathematical framework to n robots and extend the mathematical framework to three dimensions. It is also proposed to combine the effect of position uncertainty in the tracking system with position uncertainty of the tracking stations themselves in the analysis in order to better account for real-world conditions. Additionally, testing should be extended to different platforms with different sensors to further explore the applicability of this optimization method. Finally, it is proposed to modify the optimization method to compensate for the dynamics of the system so that sensor systems could move into an intercept course that would result in the optimal configuration about the tracked object at the desired time step. These proposals would result in a more applicable and robust system than is currently available
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