This paper analyzes how flocking behavior in fish can be used to develop target protection algorithms. This starts from the hypothesis that fish aggregate into coordinated flocks in order to protect themselves from predatory attacks. In order to test the protection capabilities of fish, a Prey-Predator instance is developed in which faults are introduced. Prey fish try to protect their faulty flock-mates and themselves from predatory attacks while Predators hunt the fish in order to stay alive. The simulated fish are developed using Boids that emerge in a 70 prey versus 7 predators ecosystem [4]. Results are then extracted using multiple attacking and protection strategies as well as different faulty Boid configurations.
The results initially show no improvement when flocking around faulty prey but when a genetic approach is introduced, the prey gains a clear advantage against the predators. This implies that fish flocking (as opposed to individualistic behavior) is an optimal protection strategy against attacks and could be used in other instances such as military operations or agriculture automation.

A majority of existing single-anchor localization algorithms make use of antenna arrays or special antenna systems. However, the need for specialized antenna systems incurs higher costs, complexity and power consumption. This paper presents a novel single-anchor localization algorithm, which does not require antenna arrays or special antennas. The algorithm is implemented in a two-robot system, where one of the robots acts as the anchor and the other acts as the target. The localization algorithm uses velocity measurements of each robot and distance measurements between robots. Using the change in distance between the robots and the velocity of the target robot relative to the anchor robot, the target robot can be localized relative to the anchor robot. The localization algorithm uses a Kalman filter as a state estimator of the movement of the target robot and a Savitzky-Golay filter to filter the distance measurements. A simulation with Gaussian noise in the distance measurements is performed to display the impact of the filtering methods. Another simulation showed an average localization error of 1.38 meters when the distance measurements are acquired through RSSI measurements, which is comparable to existing RSSI-based localization methods.

One of the key problems of swarm robotics is how the mobile robots can navigate accurately in a given environment. To achieve this, the mobile robots need to accurately determine where they are globally, or relative to other robots and landmarks. This paper is going to be an investigation of the Monte Carlo Localization algorithm in an environment containing 3 anchors. In addition, an alternative modification is suggested to this localization algorithm to improve the localization performance. An experiment is devised to assess different aspects of localization performances of these algorithms. With the experimental results, a quantitative analysis of performance figures are compared and analyzed.

In this paper, we explore the creation of control algorithms for swarms of robots playing the role of either predator or prey in an environment filled with static obstacles. The paper devel- ops on a famous flock simulation model proposed by Craig Reynolds called boids. The paper analyzes a zero-sum game situation, in which one swarm of robots, the prey, is trying to reach a certain pre-determined target, while another swarm of robots, the predator, is trying to prevent it from reaching its objective. Swarm control algorithms for both the predator and the prey scenarios are analyzed in an arms race manner. The robots are modelled as point-mass holonomic entities, that can move in arbitrary directions. The proposed algorithms are tested on characteristics such as success rate and time in a simulated environment. As a result, a set of algorithms for both the predator and prey are proposed and their strength and weaknesses are discussed.

Robotic swarms provide a great many uses within a world increasingly relying on autonomous systems. Alas these swarms are also very vulnerable to faults, even the smallest fault can cripple the performance of a whole swarm. Such a fault could be one of several types; those that have to do with sensors, communication, ... This paper will try to discover the effect that these faults have on the performance of a robotic swarm attempting to complete a specified task, e.g. a multiple destination routing problem or a variant of a foraging task. With the experiments concluded and the data gathered it proved possible to observe correlations between fault and performance, but this dependency heavily changes based on the algorithm used and fault introduced.

Robot swarming has been done for quite some years now. However, creating a very large swarm can become very difficult, since the networking becomes complex. With small swarms, a robot can broadcast information, or a central node can coordinate the robots. However, when a swarm becomes very large, these techniques fall short. Broadcasting all information is too much to be evaluated by every single robot, and a central node can't control it as well.
In this research, we have looked at how nature works and tried to find a solution by mimicking its behaviour. In nature, animals stay in a swarm, based on their instinct combined with following their neighbours' behaviour.
This document is part of a bigger research which implements a bird-flocking algorithm with cars. For bird-flocking, the speed and heading of neighbours need to be known. Detection of neighbours with robots is technically challenging.
We have researched a way to detect neighbours based on a hardware filter based on RSSI, in combination with a software filter.

Localization is an important element for a moving swarm of robots. A swarm contains many individuals, and it is essential that the swarm members do not collide. Expensive and complex implementations to localize others is undesirable. Hence, a localization system designed using the available resources is desired. The available techniques that have been analysed are Received Signal Strength Indicator (RSSI), Time difference of arrival (TDOA) using the ESP-NOW protocol and a spinning time of flight (TOF) sensor. The spinning TOF sensor showed to be the most promising, with close range distance detection only containing a maximum errors of 5cm. The sensor is implemented as a cheap LiDAR system by mounting it to the front of the robot, which spins around its own axis. An IMU is responsible for keeping track of the orientation of the TOF sensor. Furthermore, two small algorithms were designed and compared in order to process the TOF data.