Predicting the Size of Spring Network Swarm in Quadratic Potential Fields

Overview maps are a highly demanded feature in Massively Multiplayer Online Games. Current server-based techniques will be replaced by distributed networks due to scalability and investment costs. In a P2P MMOG it is more complex to calculate a local histogram map for every player in the virtual environment than with a centralized approach. In this work a process is proposed which aggregates local information to a 2-dimensional histogram for all players. The aggregation performs on a dynamic Delaunay triangulation network with compass routing in a clustered virtual space. It is shown that the aggregation is consistent and the Delaunay triangulation can be maintained with the proposed dislocation limitation algorithm. Furthermore, it was mathematically proved that the localized histogram aggregation process is asymptotically average optimal in message complexity.

We present a number of breakthroughs for coordinated motion planning, in which the objective is to reconfigure a swarm of labeled convex objects by a combination of parallel, continuous, collision-free translations into a given target arrangement. Problems of this type can be traced back to the classic work of Schwartz and Sharir (1983), who gave a method for deciding the existence of a coordinated motion for a set of disks between obstacles; their approach is polynomial in the complexity of the obstacles, but exponential in the number of disks. Despite a broad range of other non-trivial results for multi-object motion planning, previous work has largely focused on sequential schedules, in which one robot moves at a time, with objectives such as the number of moves; attempts to minimize the overall makespan of a coordinated parallel motion schedule (with many robots moving simultaneously) have defied all attempts at establishing the complexity in the absence of obstacles, as well as the existence of efficient approximation methods. We resolve these open problems by developing a framework that provides constant-factor approximation algorithms for minimizing the execution time of a coordinated, parallel motion plan for a swarm of robots in the absence of obstacles, provided their arrangement entails some amount of separability. In fact, our algorithm achieves constant stretch factor: If all robots want to move at most d units from their respective starting positions, then the total duration of the overall schedule (and hence the distance traveled by each robot) is O(d). Various extensions include unlabeled robots and different classes of robots. We also resolve the complexity of finding a reconfiguration plan with minimal execution time by proving that this is NP-hard, even for a grid arrangement without any stationary obstacles. On the other hand, we show that for densely packed disks that cannot be well separated, a stretch factor Omega(N^{1/4}) may be required. On the positive side, we establish a stretch factor of O(N^{1/2}) even in this case. The intricate difficulties of computing precise optimal solutions are demonstrated by the seemingly simple case of just two disks, which is shown to be excruciatingly difficult to solve to optimality.

This paper studies self-organized flocking in a swarm of mobile robots. We present Kobot, a mobile robot platform developed specifically for swarm robotic studies, briefly describing its sensing and communication abilities. In particular, we describe a scalable method that allows the robots to sense the orientations of their neighbors using a digital compass and wireless communication. Then we propose a behavior for a swarm of robots that creates self-organized flocking by using heading alignment and proximal control. The flocking behavior is observed to operate in three phases: alignment, advance, and avoidance. We evaluate four variants of this behavior by setting its parameters to extreme values and analyze the performance of flocking using a number of metrics, such as order and entropy. Our results show that, the flocking behavior obtained under appropriate parameter values, is quite robust and generates successful self-organized flocking in constraint environments.

Structuring finite sets of points is at the heart of computational geometry. Such point sets arise naturally in many applications. Examples in R3 are point sets sampled from the surface of a solid or the locations of atoms in a molecule. A first step in processing these point sets is to organize them in some data structure. Structuring a point set into a simplicial complex like the Delaunay triangulation has turned out to be appropriate for many modeling tasks. Here we introduce the flow complex which is another simplicial complex that can be computed efficiently from a finite set of points. The flow complex turned out to be well suited for surface reconstruction from a finite sample and for some tasks in structural biology. Here we study mathematical and algorithmic properties of the flow complex and show how to exploit it in applications.

This paper presents a new design for formation control on a group of autonomous humanoid robots with obstacle and collision avoidance ability that inspired by human walking behavior in a pedestrian crowd. This human walking behavior model is known as Social Force Model (SFM) and has been widely used to predict individual movement in a crowd in many variety of situation. We combine consensus algorithm with virtual structure approach for formation control and SFM for obstacle and collision avoidance. In this research, we induced some factors in SFM into consensus equation and then implemented into a group of humanoid robots. The aim of the integration is to create a group of robots that are capable of carrying out its collective tasks while still able to maintain its safety. The use of perception effect, distinct obstacle and collision avoidance function on the algorithm will differentiate algorithm with the others. The attractive feature of this new design is the various behaviors of robots to go back to their initial formation after they avoid obstacles. In the case of robot is trapped in a crowded situation (singularity condition), robot will still trying to look a new position, until it find a condition that allow him to move forward. We verify the proposed algorithm by some simulations and experiments.

My son Alex should have spent more time this spring on his homework. Instead, he spent it building a robot.

With current overuse of the road transportation system and planned increase in traffic, inno- vative solutions that overcome environmental and financial cost of the current system should be assessed. A promising idea is the use of the third dimension for personal transportation. Therefore, the European project myCopter, funded under the 7th framework, aimed at en- abling the technologies for Personal Aerial Transportation Systems as breakthrough in 21st century transportation systems. This project was the starting point of this thesis. When multiple vehicles share a common part of the sky, the biggest challenge is the man- agement of the risk of collision. While optimal collision-free navigation strategies have been proposed for autonomous robots, trajectories and accelerations for Personal Aerial Vehicles (PAVs) should also take into account human comfort for their passengers, which has rarely been the focus of these studies. Comfort of the trajectories is a key factor in order for this new transportation mean to be accepted and adopted by everyday users. Existing strategies used to maximize human-comfort of trajectories are based on path planning strategies, which compute beforehand the whole trajectory, implementing comfort as an optimization criteria. Personal Aerial Transportation Systems will have a high density of vehicles, where the time to react to potential threats might decrease to a few seconds only. This might be insufficient to compute a new trajectory each time using these path planning strategies. Therefore, in this thesis, a reactive decentralized strategy is proposed, maximizing the comfort of the trajectories for humans traveling in a Personal Aerial Vehicle. To prove the feasibility of collision avoidance strategies, it is not sufficient anymore to validate them only in simulation, but, in addition, real-time tests in a realistic outdoor environment should be performed. Nowadays, single drones can be effectively controlled by a single operator on the ground. The challenge relies instead on an efficient management of a whole swarm of drone. In this thesis, a framework to perform outdoor drone experiment was developed in order to validate the proposed collision avoidance strategy. On the one hand, an autopilot framework was developed, tailored for multi-drone experiments, allowing fast and easy deployment and maintenance of a swarm of drones. On the other hand, a ground control interface is proposed in order to monitor, control and maintain safety in a flight with a swarm of drones. Using the autopilot framework together with the ground control interface, the proposed collision avoidance strategy was validated using 10 quadrotors flying autonomously outdoor in a challenging scenario.

Distributed formation control of multi humanoid robot system with obstacle avoidance and collision avoidance are presented. The architecture consists of several layers, namely consensus tracking, consensus based formation control, behaviour and physical robot layers. The formation control system was simulated in MATLAB software before it was implemented to humanoid NAO Aldebaran robots. In the experimental study the visual odometry was used to track the robots’ position and their moving direction. The result of the experiment showed that the distributed formation control design was succeed in directing the robots into their planned formation, avoiding obstacles, and avoiding collisions among themselves with certain communication topology, shape of formation, obstacles’ position and their number.

This thesis focuses on the problem of efficiently allocating resources for enhancing the performance of an autonomous robotic agent. Such an agent is expected to operate in complex dynamic environments by continuously monitoring its internal states and the external events. These requirements give raise to countless problems that have populated research in the autonomous robotics community in the last two decades. Among these issues, one of the most relevant is to coordinate different low and high-level behaviors, giving them, from time to time, different priority values both for resource allocation and for action selection processes. The main problem in achieving this requirement is that the number and complexity of the stimuli received by each behavior may be quite high and also the effects on the emerging activity may be very hard to foresee. It is clear that it is not possible for the robotic control system to process all the incoming information, especially for real-time applications. Thus, it becomes necessary to build mechanisms able to guide this sensory input selection process and to choose the best action to perform, assuring an efficient use of the robot limited sensorial and cognitive resources. For this purpose, attentional mechanisms, balancing sensors elaboration and actions execution, can be very useful since they play two main roles: they focus the attention on salient regions of the space and they distribute resources and activities in time. As a result of the application of these mechanisms within the robotic control system for sensory-motor coordination, the robot behavior is improved: the robot becomes able to react faster to task-related or safety-critical stimuli and to opportunely split resources among concurrent behaviors. Attentional mechanisms applied to autonomous robotic systems have already been proposed, but mainly for vision-based robotics; conversely, the contribution introduced by the present thesis is the use of an artificial attentional mechanism suitable both for optimizing the use of resources and for execution monitoring and control.

Distributed predictive formation control for autonomous underwater vehicles under dynamic switching topology

Multi-robot systems have demonstrated strong capability in handling environmental operations. In this study, We examine how a team of robots can be utilized in covering and removing spill patches in a dynamic environment by executing three consecutive stages: deployment, coverage, and rendezvous. For the deployment problem, we aim for robot allocation based on the discreteness of the patches that need to be covered. With the deep neural network (DNN) based spill detector and remote sensing facilities such as drones with vision sensors and satellites, we are able to obtain the spill distribution in the workspace. Then, we formulate the allocation problem in a general optimization form and provide solutions using an integer linear programming (ILP) solver under several realistic constraints. After the allocation process is completed and the robot team is divided according to the number of spills, we deploy robots to their computed optimal goal positions. In the robot deployment part, control laws based on artificial potential field (APF) method are proposed and practiced on robots with a common unicycle model. For the coverage control problem, we show two strategies that are tailored for a wirelessly networked robot team. We propose strategies for coverage with and without path planning, depending on the availability of global information. Specifically, in terms of coverage with path planning, we partition the workspace from the aerial image into pieces and let each robot take care of one of the pieces. However, path-planning-based coverage relies on GPS signals or other external positioning systems, which are not applicable for indoor or GPS-denied circumstances. Therefore, we propose an asymptotic boundary shrink control that enables a collective coverage operation with the robot team. Such a strategy does not require a planned path, and because of its distributedness, it shows many advantages, including system scalability, dynamic spill adaptability, and collision avoidance. In case of a large-scale patch that poses challenges to robot connectivity maintenance during the operation, we propose a pivot-robot coverage strategy by mean of an a priori geometric tessellation (GT). In the pivot-robot-based coverage strategy, a team of robots is sent to perform complete coverage to every packing area of GT in sequence. Ultimately, the entire spill in the workspace can be covered and removed. For the rendezvous problem, we investigate the use of graph theory and propose control strategies based on network topology to motivate robots to meet at a designated or the optimal location. The rendezvous control strategies show a strong robustness to some common failures, such as mobility failure and communication failure. To expedite the rendezvous process and enable herding control in a distributed way, we propose a multi-robot multi-point rendezvous control strategy. To verify the validity of the proposed strategies, we carry out simulations in the Robotarium MATLAB platform, which is an open source swarm robotics experiment testbed, and conduct real experiments involving multiple mobile robots.

In order to implement swarm robots accurate and efficient formation control, a formation control algorithm for swarm robots is designed in this paper. Hungarian algorithm is used to complete the assignment task to get the overall minimum path in the process of formation. A consensus algorithm is also designed to complete the arbitrary shape formation control. In addition, the positioning system for swarm robots is set up, in which the position information of robots can be obtained through the visual positioning technology. Then the swarm robots formation experiments are carried out and the reliability of the formation control algorithm is verified.

This manuscript presents an application of the multi-layer control scheme to guide a formation of unmanned aerial vehicles (UAV) in positioning and trajectory tracking missions. In such case, each part of the formation control problem is dealt by an individual layer, which is independent module dealing with a specific part of the navigation problem. These layers are responsible to generate the desired path of the formation, to provide the desired posture of the robots, and to establish the control signal of each robot to reach their desired positions. The formation controller here introduced is able to coordinate the robots to the desired formation, including the possibility of time-varying position and/or shape, while a nonlinear underactuated controller previously proposed is responsible to guide the UAVs to their desired positions. The stability analysis of the closed-loop system is demonstrated in the sense of Lyapunov, resulting that the formation errors are ultimately bounded. In the sequel, a strategy to guide a formation of multiple unmanned aerial vehicles (MUAV) to accomplish positioning and trajectory-tracking tasks is also proposed. Delaunay triangulation is here used to split the platoon of UAVs in N-triangles, which are individually guided by the multi-layer control scheme (MLCS). The advantage of the MLCS in such proposal is the maintenance of the the Jacobian matrix order, regardless of the number of robots in the formation, which reduces the complexity of the control problem. Simulation results demonstrate the ability to accomplish the aforementioned tasks, thus validating the proposal.

We here propose a new evolutionary approach with learning to create a variety of behavioral patterns in autonomous robots. The conventional evolution or learning is to optimize a cost function such as fitness function and error function. In practice, the robot encounters situations where exist multiple solutions having quite similar fitness or error values. The optimum solution is generally selected, while others are eliminated, even if the difference in the fitness or error is very little between the solutions. This causes an essential problem for behavior-based robots. Ideally, the robot should be able to select one of the behaviors by perceiving a slight difference in the sensory information, but the ability is lost. To overcome this problem, we introduced a structural learning during the evolution of neural network ensemble (NNE). Motor outputs were generated by summing outputs of component neural networks of an NNE, and they were trained to segregate each other by negative correlation learning between generations. In experiment, each component network exhibited different functionality, producing a variety of behaviors as a whole. The proposed evolution of NNE with negative-correlation learning thus can be a practical solution for the plasticity-stability problem in robotics.

Swarm robotics is a specific research field of multirobotics where a large number of mobile robots are controlled in a coordinated way. Formation control is one of the most challenging goals for the coordination control of swarm robots. In this paper, a behavior‐based control design approach is proposed for two kinds of important formation control problems: efficient initial formation and formation control while avoiding obstacles. In this approach, a classification‐based searching method for generating large‐scale robot formation is presented to reduce the computational complexity and speed up the initial formation process for any desired formation. The behavior‐based method is applied for the formation control of swarm robot systems while navigating in an unknown environment with obstacles. Several groups of experimental results demonstrate the success of the proposed approach. These methods have potential applications for various swarm robot systems in both the simulation and the practical environments.