Group Formation Using Shortest Path Approach

Group work is becoming more important in education. Working in groups give students the ability to share ideas, to enhance problem solving skills and to improve communication skills. Thus, group formation becomes a crucial issue in order to increase group capability. However in UUM, several colleges are located remotely and majority of the students do not own personal transport. These create constraints for group meetings and it will effect the group performance. Therefore, this paper proposes method for identifying groups using shortest path approach and we hope this approach is useful for lecturers who have a large class. We also believe that the approach can be integrated with other existing methods in group formation

Group Formation Using Shortest Path Approach

Group work is becoming more important in education. Working in groups give students the ability to share ideas, to enhance problem solving skills and to improve communication skills. Thus, group formation becomes a crucial issue in order to increase group capability. However in UUM, several colleges are located remotely and majority of the students do not own personal transport. These create constraints for group meetings and it will effect the group performance. Therefore, this paper proposes method for identifying groups using shortest path approach and we hope this approach is useful for lecturers who have a large class. We also believe that the approach can be integrated with other existing methods in group formation

Wall following to escape local minima for swarms of agents using internal states and emergent behaviour

Natural examples of emergent behaviour, in groups due to interactions among the group's individuals, are numerous. Our aim, in this paper, is to use complex emergent behaviour among agents that interact via pair-wise attractive and repulsive potentials, to solve the local minima problem in the artificial potential based navigation method. We present a modified potential field based path planning algorithm, which uses agent internal states and swarm emergent behaviour to enhance group performance. The algorithm is used successfully to solve a reactive path-planning problem that cannot be solved using conventional static potential fields due to local minima formation. Simulation results demonstrate the ability of a swarm of agents to perform problem solving using the dynamic internal states of the agents along with emergent behaviour of the entire group

An emergent wall following behaviour to escape local minima for swarms of agents

Natural examples of emergent behaviour, in groups due to interactions among the group's individuals, are numerous. Our aim, in this paper, is to use complex emergent behaviour among agents that interact via pair-wise attractive and repulsive potentials, to solve the local minima problem in the artificial potential based navigation method. We present a modified potential field based path planning algorithm, which uses agent internal states and swarm emergent behaviour to enhance group performance. The algorithm is used successfully to solve a reactive path-planning problem that cannot be solved using conventional static potential fields due to local minima formation. Simulation results demonstrate the ability of a swarm of agents to perform problem solving using the dynamic internal states of the agents along with emergent behaviour of the entire group

DFT-Assisted Design and Evaluation of Bifunctional Amine/Pyridine-Oxazoline Metal Catalysts for Additions of Ketones to Unactivated Alkenes and Alkynes

Bifunctional catalyst systems for the direct addition of ­ketones to unactivated alkenes/alkynes were designed and modeled by density functional theory (DFT). The designed catalysts possess bidentate ligands suitable for binding of pi-acidic group 10 metals capable of activating alkenes/alkynes, and a tethered organocatalyst amine to ­activate the ketone via formation of a nucleophilic enamine intermediate. The structures of the designed catalysts before and after C–C bond formation were optimized using DFT, and reaction steps involving group 10 metals were predicted to be significantly exergonic. A novel oxazoline precatalyst with a tethered amine separated by a meta-substituted benzene spacer was synthesized via a 10-step sequence that ­includes a key regioselective epoxide ring-opening step. It was combined with group 10 metal salts, including cationic Pd(II) and Pt(II), and screened for the direct addition of ketones to several alkenes and an ­internal alkyne. 1H NMR studies suggest that catalyst-catalyst inter­actions with this system via amine–metal coordination may preclude the desired addition reactions. The catalyst design approach disclosed here, and the promising calculations obtained with square planar group 10 metals, light a path for the discovery of novel bifunctional catalysts for C–C bond formation

Coordination of Multiple Mobile Robots

In recent years, the demand for mobile robots to perform more complex tasks is increasing and a more complex maneuverability is required. Such task can be completed by utilizing a group of mobile robots, in a coordinate manner. To coordinate multiple mobile robots, formation control is required. Formation control refers to the ability to control the relative position and orientation of robots in a group, while allowing them to move as a whole. For this project, leader and follower formation control method is chosen. The objective of this project is to coordinate multiple mobile robots to perform specified trajectory while maintaining spatial distance between leader robot and followers. Three AmigoBot mobile robots are used to carry out the experiments. There are three experiments which are experiment 1; 10m straight line trajectory path, experiment 2; 6m zigzag shaped trajectory path and experiment 3; 10m straight line with obstacles trajectory path. Specified trajectory path plan are developed and given to the leader robot and the follower will move relative to the leader coordinates using the designed control program. Deviation error for the spatial distance is recorded. The mobile robots successfully maintain formation, with spatial distance deviation error less than 5%

Fault-tolerant formation driving mechanism designed for heterogeneous MAVs-UGVs groups

A fault-tolerant method for stabilization and navigation of 3D heterogeneous formations is proposed in this paper. The presented Model Predictive Control (MPC) based approach enables to deploy compact formations of closely cooperating autonomous aerial and ground robots in surveillance scenarios without the necessity of a precise external localization. Instead, the proposed method relies on a top-view visual relative localization provided by the micro aerial vehicles flying above the ground robots and on a simple yet stable visual based navigation using images from an onboard monocular camera. The MPC based schema together with a fault detection and recovery mechanism provide a robust solution applicable in complex environments with static and dynamic obstacles. The core of the proposed leader-follower based formation driving method consists in a representation of the entire 3D formation as a convex hull projected along a desired path that has to be followed by the group. Such an approach provides non-collision solution and respects requirements of the direct visibility between the team members. The uninterrupted visibility is crucial for the employed top-view localization and therefore for the stabilization of the group. The proposed formation driving method and the fault recovery mechanisms are verified by simulations and hardware experiments presented in the paper

Navigation, localization and stabilization of formations of unmanned aerial and ground vehicles

A leader-follower formation driving algorithm developed for control of heterogeneous groups of unmanned micro aerial and ground vehicles stabilized under a top-view relative localization is presented in this paper. The core of the proposed method lies in a novel avoidance function, in which the entire 3D formation is represented by a convex hull projected along a desired path to be followed by the group. Such a representation of the formation provides non-collision trajectories of the robots and respects requirements of the direct visibility between the team members in environment with static as well as dynamic obstacles, which is crucial for the top-view localization. The algorithm is suited for utilization of a simple yet stable visual based navigation of the group (referred to as GeNav), which together with the on-board relative localization enables deployment of large teams of micro-scale robots in environments without any available global localization system. We formulate a novel Model Predictive Control (MPC) based concept that enables to respond to the changing environment and that provides a robust solution with team members' failure tolerance included. The performance of the proposed method is verified by numerical and hardware experiments inspired by reconnaissance and surveillance missions