Comparison of different cue-based swarm aggregation strategies

In this paper, we compare different aggregation strategies for cue-based aggregation with a mobile robot swarm. We used a sound source as the cue in the environment and performed real robot and simulation based experiments. We compared the performance of two proposed aggregation algorithms we called as the vector averaging and naïve with the state-of-the-art cue-based aggregation strategy BEECLUST. We showed that the proposed strategies outperform BEECLUST method. We also illustrated the feasibility of the method in the presence of noise. The results showed that the vector averaging algorithm is more robust to noise when compared to the naïve method

Colias-Φ: an autonomous micro robot for artificial pheromone communication

Ants pheromone communication is an efficient mechanism which took inspiration from nature. It has been used in various artificial intelligence and multi robotics researches. This paper presents the development of an autonomous micro robot to be used in swarm robotic researches especially in pheromone based communication systems. The robot is an extended version of Colias micro robot with capability of decoding and following artificial pheromone trails. We utilize a low-cost experimental setup to implement pheromone-based scenarios using a flat LCD screen and a USB camera. The results of the performed experiments with group of robots demonstrated the feasibility of Colias-Φ to be used in pheromone based experiments

Experimental Study and Modeling of Three Classes of Collective Problem-Solving Methods

People working together can be very successful problem-solvers. Many real-life examples, from Wikipedia to citizen science projects, show that, under the right conditions, crowds can find remarkable solutions to complex problems. Yet, joining the capabilities of many people can be challenging. What factors make some groups more successful than others? How does the nature of the problem and the structure of the environment influence the group's performance? To answer these questions, I consider problem-solving as a search process -- a situation in which individuals are searching for a good solution. I describe and compare three different methods for structuring groups: (1) non-interacting groups, where individuals search independently without exchanging any information, (2) social groups, where individuals freely exchange information during their search, and (3) solution-influenced groups, where individuals repeatedly contribute to a shared collective solution. First, I introduce the idea of transmission chains - a specific type of solution-influenced group where individuals tackle the problem one after another, each one starting from the solution of its predecessor. I apply this method to binary choice problems and compare it to majority voting rules in non-interacting groups. The results show that transmission chains are superior in environments where individual accuracy is low and confidence is a reliable indicator of performance. This type of environment, however, is rarely observed in two experimental datasets. Then, I evaluate the performance of transmission chains for problems that have a complex structure, such as multidimensional optimization tasks. Again, I use non-interacting groups as a comparison, this time by selecting the best out of multiple independent solutions. Simulations and experimental data show that transmission chains outperform independent groups under two environmental conditions: either when problems are rather easy, or when group members are relatively unskilled. Next, I focus on social groups, where individuals influence each other during the search. To understand the social dynamics that operate in such groups, I conduct two studies: I first examine how people search for a solution independently from others, and then study how this individual process is impacted by social influence. The first study presents experimental data to show that the individual search behavior can be described by a take-the-best heuristic, that is, a simple rule-of-thumb that ignores all but one cue at a time. This heuristic reproduces a variety of behavioral patterns observed in different environments. Then, I extend this heuristic to include social interactions where multiple individuals exchange information during their search. My results show that, in this case, individuals tend to converge towards similar solutions. This induces a collective search dilemma: compared to non-interacting groups, the quality of the average individual's solution is improved at the expense of the best solution of the group. Nevertheless, further analyses show that this dilemma disappears for more difficult problems. Overall, this thesis shows that no collective problem-solving method is superior to the others in all environments and for all problems. Instead, the performance of each method depends on numerous factors, such as the nature of the task, the problem difficulty, the group composition, and the skill levels of the individuals. My work helps understanding the role of these different factors and their influence on collective problem-solving

An Approach Based on Particle Swarm Optimization for Inspection of Spacecraft Hulls by a Swarm of Miniaturized Robots

The remoteness and hazards that are inherent to the operating environments of space infrastructures promote their need for automated robotic inspection. In particular, micrometeoroid and orbital debris impact and structural fatigue are common sources of damage to spacecraft hulls. Vibration sensing has been used to detect structural damage in spacecraft hulls as well as in structural health monitoring practices in industry by deploying static sensors. In this paper, we propose using a swarm of miniaturized vibration-sensing mobile robots realizing a network of mobile sensors. We present a distributed inspection algorithm based on the bio-inspired particle swarm optimization and evolutionary algorithm niching techniques to deliver the task of enumeration and localization of an a priori unknown number of vibration sources on a simplified 2.5D spacecraft surface. Our algorithm is deployed on a swarm of simulated cm-scale wheeled robots. These are guided in their inspection task by sensing vibrations arising from failure points on the surface which are detected by on-board accelerometers. We study three performance metrics: (1) proximity of the localized sources to the ground truth locations, (2) time to localize each source, and (3) time to finish the inspection task given a 75% inspection coverage threshold. We find that our swarm is able to successfully localize the present so

Improving the adaptability of simulated evolutionary swarm robots in dynamically changing environments

One of the important challenges in the field of evolutionary robotics is the development of systems that can adapt to a changing environment. However, the ability to adapt to unknown and fluctuating environments is not straightforward. Here, we explore the adaptive potential of simulated swarm robots that contain a genomic encoding of a bio-inspired gene regulatory network (GRN). An artificial genome is combined with a flexible agent-based system, representing the activated part of the regulatory network that transduces environmental cues into phenotypic behaviour. Using an artificial life simulation framework that mimics a dynamically changing environment, we show that separating the static from the conditionally active part of the network contributes to a better adaptive behaviour. Furthermore, in contrast with most hitherto developed ANN-based systems that need to re-optimize their complete controller network from scratch each time they are subjected to novel conditions, our system uses its genome to store GRNs whose performance was optimized under a particular environmental condition for a sufficiently long time. When subjected to a new environment, the previous condition-specific GRN might become inactivated, but remains present. This ability to store 'good behaviour' and to disconnect it from the novel rewiring that is essential under a new condition allows faster re-adaptation if any of the previously observed environmental conditions is reencountered. As we show here, applying these evolutionary-based principles leads to accelerated and improved adaptive evolution in a non-stable environment

Can simple transmission chains foster collective intelligence in binary-choice tasks?

In many social systems, groups of individuals can find remarkably efficient solutions to complex cognitive problems, sometimes even outperforming a single expert. The success of the group, however, crucially depends on how the judgments of the group members are aggregated to produce the collective answer. A large variety of such aggregation methods have been described in the literature, such as averaging the independent judgments, relying on the majority or setting up a group discussion. In the present work, we introduce a novel approach for aggregating judgments - the transmission chain - which has not yet been consistently evaluated in the context of collective intelligence. In a transmission chain, all group members have access to a unique collective solution and can improve it sequentially. Over repeated improvements, the collective solution that emerges reflects the judgments of every group members. We address the question of whether such a transmission chain can foster collective intelligence for binary-choice problems. In a series of numerical simulations, we explore the impact of various factors on the performance of the transmission chain, such as the group size, the model parameters, and the structure of the population. The performance of this method is compared to those of the majority rule and the confidence-weighted majority. Finally, we rely on two existing datasets of individuals performing a series of binary decisions to evaluate the expected performances of the three methods empirically. We find that the parameter space where the transmission chain has the best performance rarely appears in real datasets. We conclude that the transmission chain is best suited for other types of problems, such as those that have cumulative properties

Advancing performability in playable media : a simulation-based interface as a dynamic score

When designing playable media with non-game orientation, alternative play scenarios to gameplay scenarios must be accompanied by alternative mechanics to game mechanics. Problems of designing playable media with non-game orientation are stated as the problems of designing a platform for creative explorations and creative expressions. For such design problems, two requirements are articulated: 1) play state transitions must be dynamic in non-trivial ways in order to achieve a significant level of engagement, and 2) pathways for players’ experience from exploration to expression must be provided. The transformative pathway from creative exploration to creative expression is analogous to pathways for game players’ skill acquisition in gameplay. The paper first describes a concept of simulation-based interface, and then binds that concept with the concept of dynamic score. The former partially accounts for the first requirement, the latter the second requirement. The paper describes the prototype and realization of the two concepts’ binding. “Score” is here defined as a representation of cue organization through a transmodal abstraction. A simulation based interface is presented with swarm mechanics and its function as a dynamic score is demonstrated with an interactive musical composition and performance