Abstract: This paper deals with motion planning of multiple
mobile robots. Mobile robots working together to achieve several
objectives have many advantages over single robot system. However,
the planning and coordination between the mobile robots is
extremely difficult. In the present investigation rule-based and rulebased-
neuro-fuzzy techniques are analyzed for multiple mobile
robots navigation in an unknown or partially known environment.
The final aims of the robots are to reach some pre-defined goals.
Based upon a reference motion, direction; distances between the
robots and obstacles; and distances between the robots and targets;
different types of rules are taken heuristically and refined later to find
the steering angle. The control system combines a repelling influence
related to the distance between robots and nearby obstacles and with
an attracting influence between the robots and targets. Then a hybrid
rule-based-neuro-fuzzy technique is analysed to find the steering
angle of the robots. Simulation results show that the proposed rulebased-
neuro-fuzzy technique can improve navigation performance in
complex and unknown environments compared to this simple rulebased
technique.
Abstract: In this paper, we propose a solution to the motion
control problem of a 2-link revolute manipulator arm. We require the
end-effector of the arm to move safely to its designated target in a
priori known workspace cluttered with fixed circular obstacles of
arbitrary position and sizes. Firstly a unique velocity algorithm is
used to move the end-effector to its target. Secondly, for obstacle
avoidance a turning angle is designed, which when incorporated into
the control laws ensures that the entire robot arm avoids any number
of fixed obstacles along its path enroute the target. The control laws
proposed in this paper also ensure that the equilibrium point of the
system is asymptotically stable. Computer simulations of the
proposed technique are presented.