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In this paper, we present a flocking model where agents are equipped with navigational and obstacle avoidance capabilities that conform to user defined paths and formation shape requirements. In particular, we adopt an agent-based paradigm to achieve flexible formation handling at both the individual and flock level. The proposed model is studied under three different scenarios where flexible flock formations are produced automatically via algorithmic means to: 1) navigate around dynamically emerging obstacles, 2) navigate through narrow space and 3) navigate along path with sharp curvatures, hence minimizing the manual effort of human animators. Simulation results showed that the proposed model leads to highly realistic, flexible and real-time reactive flock formations. [ABSTRACT FROM AUTHOR]

We propose a self-stabilizing marching algorithm for a group of oblivious robots in an obstacle-free workplace. To this end, we develop a distributed algorithm for a group of robots to transport a polygonal object, where each robot holds the object at a corner, and observe that each robot can simulate the algorithm, even after we replace the object by an imaginary one; we thus can use the algorithm as a marching algorithm. Each robot independently computes a velocity vector using the algorithm, moves to a new position with the velocity for a unit of time, and repeats this cycle until it reaches the goal position. The algorithm is oblivious, i.e., the computation depends only on the current robot configuration, and is constructed from a naive algorithm that generates only a selfish move, by adding two simple ingredients. For the case of two robots, we theoretically show that the algorithm is self-stabilizing, and demonstrate by simulations that the algorithm produces a motion that is fairly close to the time-optimal motion. For cases of more than two robots, we show that a natural extension of the algorithm for two robots also produces smooth and elegant motions by simulations as well. [ABSTRACT FROM AUTHOR]