Adaptive swarming by exploiting hydrodynamic interaction based on Stokesian Dynamics method

This piece of work studies the partitioning problem on independently operating swarm of autonomous mobile robots and devises algorithms for unbalanced partitioning in a distributed computing environment. The robots considered here are all identical and are very simple and weak. There is no central control over the robots and the robots do not communicate among themselves. Each robot executes the same algorithm based on their local information. This paper frames the algorithms for unbalanced partitioning by sorting the robots based on their ranking and then allocating them in different groups based on their ranks, such that N robots are divided into K unbalanced groups of unequal robots in each group. This paper also presents the performance based analysis of the un-balanced algorithm U_PART over the balanced algorithm and examines their effects via different examples through 50 separate test cases. It also tries to bring out the shortcomings of the proposed U_PART algorithm and proposes another alternative approach towards un-balanced partitioning to overcome the limitation of the U_PART algorithm.

We describe a set of distributed algorithms used to disperse a large group of autonomous mobile robots efficiently throughout an indoor environment. Only local inter-robot communication and processing is used. Ad-hoc communications network topologies formed by gradient floods spread messages and guide robot motion. Special attention has been given to doors, hallways, and other constrictions. The network maintains a route to chargers to allow self-charging.

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.

Interactions between hard spheres sedimenting at low Reynolds number

In this paper a honeybee inspired collective-decision-making algorithm called BEECLUST is studied in a swarm of autonomous robots and the performance of the swarm is investigated in different conditions. The algorithm has low requirements thus it is promising for implementation in robots with low resources. Here the algorithm is applied in swarms of improved e-puck robots in three different conditions in order to study the strengths and limitations of the algorithm. The collective system demonstrated a high performance in adapting to a dynamic environment as well as a very low sensitivity to additional robots with malfunctioning sensors. On the other hand the system shows an strong response to robots that act as social seeds influencing the decision-making of the swarm.

A scalable parallel Stokesian Dynamics method for the simulation of colloidal suspensions

Since its development, Stokesian dynamics has been a leading approach for the dynamic simulation of suspensions of particles at arbitrary concentrations with full hydrodynamic interactions. Although developed originally for the simulation of passive particle suspensions, the Stokesian dynamics framework is equally well suited to the analysis and dynamic simulation of suspensions of active particles, as we elucidate here. We show how the reciprocal theorem can be used to formulate the exact dynamics for a suspension of arbitrary active particles, and then show how the Stokesian dynamics method provides a rigorous way to approximate and compute the dynamics of dense active suspensions where many-body hydrodynamic interactions are important.

Brownian Dynamics Simulations of Ferromagnetic Colloidal Dispersions in a Simple Shear Flow

The characteristics and differences in the two approximations. i.e., the additivity of forces and the additivity of velocities, on which Stokesian dynamics methods are based, have been investigated. To do so, Stokesian dynamics simulations of a ferromagnetic colloidal dispersion have been carried out for a simple shear flow, and the aggregate structures and the averaged viscosities have been evaluated, From the results of aggregate structures in equilibrium, the results obtained by the additivity of forces are quantaively different from those by the approximation of ignoring hydrodynamic interactions, although a qualitative agreement is recognized. The results obtained by the additivity of velocities give medium characteristics between the two above approximations. This difference in characeristics between the two additivity approximations and that of ignoring hydrodynamics becomes clearer as the influences of the shear flow dominate those of magnetic forces more significantly. From the results of transient characteristics from an initial state, the results of the additivity of velocities agree well with those of the additivity of forces. In contrast, the results without hydrodynamic interactions deviate from those of the two additivity approximations at a time step when a nearly particle particle touching situation starts to appear.

This study aims to explore the tracking control challenge in a swarm of multiple nonholonomic wheeled mobile robots (NWMRs) by utilizing a distributed leader–follower strategy grounded in the cascade system theory. Firstly, the kinematic control law is developed for the leader by constructing a sliding surface based on the error tracking model with a virtual reference trajectory. Secondly, a communication topology with the desired formation pattern is modeled for the multiple robots by using the graph theory. Further, in the leader–follower NWMR system, each follower lacks direct access to the leader’s information. Therefore, a novel distributed-based controller by PD-based controller for the follower is developed, enabling each follower to obtain the leader’s information. Thirdly, for each case, we give a further analysis of the closed-loop system to guarantee uniform global asymptotic stability with the conditions based on the cascade system theory. Finally, the trajectory tracking performance of the proposed controllers for the NWMR system is illustrated through simulation results. The leader robot achieved a low RMSE of 1.6572 (Robot 1), indicating accurate trajectory tracking. Follower robots showed RMSEs of 2.6425 (Robot 2), 3.0132 (Robot 3), and 4.2132 (Robot 3), reflecting minor variations due to the distributed control strategy and local disturbances.

The purpose of the demonstrator is to present a novel system for gesture-based interaction between humans and a swarm of mobile robots. The human interacts with the swarm by showing hand gestures using an orange glove. Following initial hand glove detection, the robots move to adapt their positions and viewpoints. The purpose is to improve individual sensing performance and maximize the gesture information mutually gathered by the swarm as a whole. Using multi-hop message relaying, robots spread their opinions and the associated confidence about the issued hand gesture throughout the swarm. To let the robots in the swarm integrate and weight the different opinions, we developed a distributed consensus protocol. When a robot has gathered enough evidence, it takes a decision for the hand gesture, and sends it into the swarm. Different decisions compete with each other. The one assessed with the highest confidence eventually wins. When consensus is reached about the hand gesture, the swarm acts accordingly, for example by moving to a location, or splitting into groups.The working of the system is shown and explained in the video accessible at the following address: http://www.idsia.ch/~gianni/SwarmRobotics/aamasdemo.zip.

This paper presents a distributed algorithm for painting a priori known obstacle free rectangular region by swarm of mobile robots with limited visibility capability. We have assumed that initially the visibility graph is connected. Our approach is to divide the region into some non overlapping strips, and to let each robot to paint one of these strips assigned to it. Width of the strips may vary for different robots. In the proposed algorithm, the robots follow the basic Wait-Observe-Compute-Move model together with the Asynchronous timing model.

Costs and benefits of behavioral specialization

A parallel implementation of the Stokesian Dynamics method applied to the simulation of colloidal suspensions

For a swarm of mobile robots in an unknown environment, the most challenging task is to plan the optimal path and also to learn the environmental parameters. Machine learning is one of the solutions to this problem. This paper proposes a combination of particle swarm optimization (PSO) and Q-value based reinforcement learning (Q-Learning) for a swarm of mobile robots to find the optimal path in an unknown environment and to learn the environment. Q-learning combined with PSO enable the robots to learn the unknown environment with reward and action selection policy while reducing the time to find the optimal path in the environment using the iterative improvement method of PSO. The proposed algorithm is simulated and found performing faster than Q-learning and PSO performing alone. We also compared with some other established algorithm where the proposed algorithm outperforms each of them in accuracy and speed.