Input Efficiency for Influencing Swarm

Many cooperative control problems ranging from formation following, to rendezvous to flocking can be expressed as consensus problems. The ability of an operator to influence the development of consensus within a swarm therefore provides a basic test of the quality of human-swarm interaction (HSI). Two plausible approaches are : Direct- dictate a desired value to swarm members or Indirect- control or influence one or more swarm members relying on existing control laws to propagate that influence. Both approaches have been followed by HSI researchers. The Indirect case uses standard consensus methods where the operator exerts influence over a few robots and then the swarm reaches a consensus based on its intrinsic rules. The Direct method corresponds to flooding in which the operator directly sends the intention to a subset of the swarm and the command then propagates through the remainder of the swarm as a privileged message. In this paper we compare these two methods regarding their convergence time and properties in noisy and noiseless conditions with static and dynamic graphs. We have found that average consensus method (indirect control) converges much slower than flooding (direct) method but it has more noise tolerance in comparison with simple flooding algorithms. Also, we have found that the convergence time of the consensus method behaves erratically when the graph’s connectivity (Fiedler value) is high

HUMAN-DATA INTERACTION IN LARGE AND HIGH-DIMENSIONAL DATA

Human-Data Interaction (HDI) is an emerging field which studies how humans make sense of large and complex data. Visual analytics tools are a central component of this sensemaking process. However, the growth of big data has affected their performance, resulting in latency in interactivity or long query-response times, both of which degrade one's ability to do knowledge discovery. To address these challenges, a new paradigm of data exploration has appeared in which a rapid but inaccurate result is followed by a succession of gradually more accurate answers. As the primary objective of this thesis, we investigated how this incremental latency affects the quantity and quality of knowledge discovery in an HDI system. We have developed a big data visualization tool and studied 40 participants in a think-aloud experiment, using this tool to explore a large and high-dimensional data. Our findings indicate that although incremental latency reduces the rate of discovery generation, it does not affect one's chance of making a discovery per each generated visualization, and it does not affect the correctness of those discoveries. However, in the presence of latency, utilizing contextual layers such as a map result in fewer mistakes while exploring higher-dimensional visualizations lead to more incorrect discoveries. As the secondary objective, we investigated what strategies improved a subject's performance. Our observations suggest that successful participants explore the data methodically, by first examining simple and familiar concepts and then gradually adding complexity to the visualizations, until they build a correct mental model of the inner workings of the tool. With this model, they generate several discovery patterns, each acting as a blueprint for forming new insights. Ultimately, some participants combined their discovery patterns to create multifaceted data-driven stories. Based on these observations, we propose design guidelines for developing HDI platforms for large and high-dimensional data

A dynamic fuzzy-based crossover method for genetic algorithms

Currently, Genetic Algorithms (GA) are widely used in different optimization problems. One of the problems with GAs is tuning their parameters correctly as they can have a significant effect on GA's overall performance. Till now, different methods have been proposed for fine tuning these parameters. Many of these methods use fuzzy linguistic rules in order to find the correct parameters in each stage of the GA evolution. But these methods look at each chromosome as a whole solution for a specific problem. In our contribution, a new method has been proposed which breaks each chromosome into sub parts and uses the better sub-solutions as the building blocks of the next generation using a fuzzy-based approach. The performance of this algorithm has been shown on the Traveling Salesman Problem (TSP) with comparison to Simple GA and Adaptive GA

Explicit vs. Tacit Leadership in Influencing the Behavior of Swarms

Many researchers have employed some form of teleoperated leader to influence a robotic swarm; however, the way in which this influence is conveyed has not been well studied. Some researchers employ designated leaders that are known to be leaders by other members of the swarm and hence followed. Others do not impose a leader/follower distinction on the swarm’s algorithms and instead choose to influence the swarm indirectly through controlling one or more of its members. This paper compares leader-based methods of each type, designated as consensus (no explicit leader/follower distinction) and flooding (influence propagating from leader takes precedence). We compared the two methods for convergence time and properties in noisy and noiseless conditions with static and dynamic graphs. We found that consensus converged much slower than flooding but had slightly better noise tolerance. In the human experiments we compared the ability of operators to maneuver a swarm to goal points using each method, both with and without sensing error. As in simulation, the flooding method was significantly more effective in moving the swarm between goal points. The greater sensitivity of flooding to error found in simulation, however, was not observed in the human experiments. Instead, the error degraded performance equally across the two condition

Human control of robot swarms with dynamic leaders

Controlling a swarm of robots after deployment is difficult, due to the unpredictable and emergent behavior of swarm algorithms. Past work has focused on influencing the swarm via statically selected leaders - swarm members that the operator directly controls - that are pre-selected and remain leaders throughout the scenario execution. This paper investigates the use of dynamically selected leaders that are directly controlled by the human operator to guide the rest of the swarm, which is operating under a flocking-style algorithm. The goal of the operator is to move the swarm to goal regions that arise dynamically in the environment. We experimentally investigated (a) the effect of density of leaders on the ease of human control and system performance, and (b) how restriction of information communicated to the human operator affects the ability to guide the swarm to goal regions. The density of leaders is computed based on an extension of the random competition clustering (RCC) algorithm used in wireless sensor networks to select cluster heads. In particular, we studied the effect of different guarantees of the maximum number of hops in the communication graph from any robot to the nearest leader. Increasing the maximum hop guarantee effectively lowers the density of leaders in the swarm. Our results show that, while there was a large drop in the number of goals reached when moving from a 1-hop to a 2-hop guarantee, the difference between a 2-hop and 3-hop guarantee was not statistically significant. Furthermore, we found that performance was just as good when the information returned to the operator was restricted, showing that operators can still navigate a swarm even when they have imperfect information

Human Control of Leader-Based Swarms

As swarms are used in increasingly more complex scenarios, further investigation is needed to determine how to give human operators the best tools to properly influence the swarm after deployment. Previous research has focused on relaying influence from the operator to the swarm, either by broadcasting commands to the entire swarm or by influencing the swarm through the teleoperation of a leader. While these methods each have their different applications, there has been a lack of research into how the influence should be propagated through the swarm in leader-based methods. This paper focuses on two simple methods of information propagation-flooding and consensus-and compares the ability of operators to maneuver the swarm to goal points using each, both with and without sensing error. Flooding involves each robot explicitly matching the speed and direction of the leader (or matching the speed and direction of the first neighboring robot that has already done so), and consensus involves each robot matching the average speed and direction of all the neighbors it senses. We discover that the flooding method is significantly more effective, yet the consensus method has some advantages at lower speeds, and in terms of overall connectivity and cohesion of the swarm

Control of swarms with multiple leader agents

The study of human control of robotic swarmsinvolves designing interfaces and algorithms for allowing ahuman operator to influence a swarm of robots. One ofthe main difficulties, however, is determining how to most effectively influence the swarm after it has been deployed. Past work has focused on influencing the swarm via statically selected leaders—swarm members that the operator directly controls. This paper investigates the use of a small subset of the swarm as leaders that are dynamically selected during the scenario execution and are directly controlled by the human operator to guide the rest of the swarm, which is operating under a flocking-style algorithm. The goal of the operator in this study is to move the swarm to goal regions that arise dynamically in the environment.We experimentally investigated three different aspects of dynamic leader-based swarm control and their interactions: leader density (in terms of guaranteed hops to a leader), sensing error, and method of information propagation from leaders to the rest of the swarm. Our results show that, while there was a large drop in the number of goals reached when moving from a 1-hop to a 2-hop guarantee, the difference between a 2-hop, 3-hop, and 4-hop guarantee was not statistically significant. Furthermore, we found that sensing error impacted the explicit information-propagation method more than the tacit method conditions, and caused participants more trouble the lower the density of leaders, although the explicit method performed better overall