Abstract: This paper proposes a new obstacle and collision
avoidance control laws for a three-dimensional swarm of boids.
The swarm exhibit collective emergent behaviors whilst avoiding the
obstacles in the workspace. While flocking, animals group up in order
to do various tasks and even a greater chance of evading predators. A
generalized algorithms for attraction to the centroid, inter-individual
swarm avoidance and obstacle avoidance is designed in this paper.
We present a set of new continuous time-invariant velocity control
laws is presented which is formulated via the Lyapunov-based control
scheme. The control laws proposed in this paper also ensures practical
stability of the system. The effectiveness of the proposed control laws
is demonstrated via computer simulations
Abstract: In this paper, we propose a solution to the motion
planning and control problem for a swarm of three-dimensional
boids. The swarm exhibit collective emergent behaviors within the
vicinity of the workspace. The capability of biological systems
to autonomously maneuver, track and pursue evasive targets in a
cluttered environment is vastly superior to any engineered system. It
is considered an emergent behavior arising from simple rules that are
followed by individuals and may not involve any central coordination.
A generalized, yet scalable algorithm for attraction to the centroid
and inter-individual swarm avoidance is proposed. We present a set
of new continuous time-invariant velocity control laws, formulated via
the Lyapunov-based control scheme for target attraction and collision
avoidance. The controllers provide a collision-free trajectory. The
control laws proposed in this paper also ensures practical stability
of the system. The effectiveness of the control laws is demonstrated
via computer simulations.
Abstract: This paper proposes a solution to the motion planning
and control problem of car-like mobile robots which is required to
move safely to a designated target in a priori known workspace
cluttered with swarm of boids exhibiting collective emergent
behaviors. A generalized algorithm for target convergence and
swarm avoidance is proposed that will work for any number of
swarms. The control laws proposed in this paper also ensures
practical stability of the system. The effectiveness of the proposed
control laws are demonstrated via computer simulations of an
emergent behavior.