Abstract — In this brief article we specify an “individual-based ” continuous time model for swarm aggregation in n-dimensional space and study its stability properties. We show that the individuals (autonomous agents or biological creatures) will form a cohesive swarm in a finite time. Moreover, we obtain an explicit bound on the swarm size, which depends only on the parameters of the swarm model. I.

We introduce a framework, called "physicomimetics," that provides distributed control of large collections of mobile physical agents in sensor networks. The agents sense and react to virtual forces, which are motivated by natural physics laws. Thus, physicomimetics is founded upon solid scientific principles. Furthermore, this framework provides an effective basis for self-organization, fault-tolerance, and self-repair. Three primary factors distinguish our framework from others that are related: an emphasis on minimality (e.g., cost effectiveness of large numbers of agents implies a need for expendable platforms with few sensors), ease of implementation, and run-time efficiency. Examples are shown of how this framework has been applied to construct various regular geometric lattice configurations (distributed sensing grids), as well as dynamic behavior for perimeter defense and surveillance. Analyses are provided that facilitate system understanding and predictability, including both qualitative and quantitative analyses of potential energy and a system phase transition. Physicomimetics has been implemented both in simulation and on a team of seven mobile robots. Specifics of the robotic embodiment are presented in the paper.

The work of this paper is inspired by the flocking phenomenon observed in Reynolds (1987). We introduce a class of local control laws for a group of mobile agents that result in: (i) global alignment of their velocity vectors, (ii) convergence of their speeds to a common one, (iii) collision avoidance, and (iv) minimization of the agents artificial potential energy. These are made possible through local control action by exploiting the algebraic graph theoretic properties of the underlying interconnection graph. Algebraic connectivity a#ects the performance and robustness properties of the overall closed loop system. We show how the stability of the flocking motion of the group is directly associated with the connectivity properties of the interconnection network and is robust to arbitrary switching of the network topology.

This paper presents a geometric based approach for multiple mobile robot motion coordination 1 . All the robot paths being computed independently, we address the problem of coordinating the motion of the robots along their path in such a way they do not collide each other. The proposed algorithm is based on a bounding box representation of the obstacles in the so-called coordination diagram. The algorithm is resolution-complete but it is shown to be complete for a large class of inputs. Despite the exponential dependency of the coordination problem, the algorithm solves efficiently problems involving up to ten robots in worst case situations, and more than 100 robots in practical ones.

The ability of self-reconfigurable robots to solve a variety of robot tasks comes in part from their use of a large number of modules. E#ective use of these systems requires parallel actuation and planning, both for e#ciency and independence from a central controller. This paper presents the PacMan algorithm, a technique for distributed actuation and planning. This algorithm was developed for systems with unit-compressible modules, such as the crystalline robot. We also describe some analytical properties of the PacMan planning and actuation, and discuss simulation and hardware experiments. 1

Abstract—Operations with multiple autonomous underwater vehicles (AUVs) have a variety of underwater applications. For example, a coordinated group of vehicles with environmental sensors can perform adaptive ocean sampling at the appropriate spatial and temporal scales. We describe a methodology for cooperative control of multiple vehicles based on virtual bodies and artificial potentials (VBAP). This methodology allows for adaptable formation control and can be used for missions such as gradient climbing and feature tracking in an uncertain environment. We discuss our implementation on a fleet of autonomous underwater gliders and present results from sea trials in Monterey Bay in August, 2003. These at-sea demonstrations were performed as part of the Autonomous Ocean Sampling Network (AOSN) II project. Index Terms—Adaptive sampling, autonomous underwater vehicles (AUVs), cooperative control, formations, gradient climbing, underwater gliders. I.

Abstract Virtual environment are often populated with moving units and the paths for these units shouldbe planned. When multiple units need to exhibit coherent behavior in a cluttered environment,

Programmable vector fields can be used to control a variety of flexible planar parts feeders. These devices can exploit exotic actuation technologies such as arrayed, massively-parallel microfabricated motion pixels or transversely vibrating (macroscopic) plates. These new automation designs promise great flexibility, speed, and dexterity---we believe they may be employed to orient, singulate, sort, feed, and assemble parts. However, since they have only recently been invented, programming and controlling them for manipulation tasks is challenging. When a part is placed on our devices, the programmed vector field induces a force and moment upon it. Over time, the part may come to rest in a dynamic equilibrium state. By chaining together sequences of vector fields, the equilibrium states of a part in the field may be cascaded to obtain a desired final state. The resulting strategies require no sensing and enjoy efficient planning algorithms. This paper begins by describing our experimen...

Abstract—In this article we specify an-member “individual-based” continuous time swarm model with individuals that move in an-dimensional space according to an attractant/repellent or a nutrient profile. The motion of each individual is determined by three factors: i) attraction to the other individuals on long distances; ii) repulsion from the other individuals on short distances; and iii) attraction to the more favorable regions (or repulsion from the unfavorable regions) of the attractant/repellent profile. The emergent behavior of the swarm motion is the result of a balance between inter-individual interactions and the simultaneous interactions of the swarm members with their environment. We study the stability properties of the collective behavior of the swarm for different profiles and provide conditions for collective convergence to more favorable regions of the profile. Index Terms—Aggregations, attraction, continuous time swarm, gradient climbing, individual-based, inter-individual interactions, multi-agent systems,-dimensional space, repulsion, stability analysis, swarms. I.

We develop and analyze algorithms for dispersing a swarm of primitive robots in an unknown environment, R. The primary objective is to minimize the makespan, that is, the time to fill the entire region. An environment is composed of pixels that form a connected subset of the integer grid. There is at most one robot per pixel and robots move horizontally or vertically at unit speed. Robots enter R by means of k ≥ 1 door pixels. Robots are primitive finite automata, only having local communication, local sensors, and a constant-sized memory. We first give algorithms for the single-door case...