To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure firstname.lastname@example.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
This paper addresses the motion planning and control problem of a system of 1-trailer robots navigating a dynamic environment cluttered with obstacles including a swarm of boids. A set of nonlinear continuous control laws is proposed via the Lyapunov-based Control Scheme for collision, obstacle, and swarm avoidances. Additionally, a leader–follower strategy is utilized to allow the flock to split and rejoin when approaching obstacles. The effectiveness of the control laws is demonstrated through numerical simulations, which show the split and rejoin maneuvers by the flock when avoiding obstacles while the swarm exhibits emergent behaviors.
The paper considers the problem of motion planning and posture control of multiple n-link doubly nonholonomic mobile manipulators in an obstacle-cluttered and bounded workspace. The workspace is constrained with the existence of an arbitrary number of fixed obstacles (disks, rods and curves), artificial obstacles and moving obstacles. The coordination of multiple n-link doubly nonholonomic mobile manipulators subjected to such constraints becomes therefore a challenging navigational and steering problem that few papers have considered in the past. Our approach to developing the controllers, which are novel decentralized nonlinear acceleration controllers, is based on a Lyapunov control scheme that is not only intuitively understandable but also allows simple but rigorous development of the controllers. Via the scheme, we showed that the avoidance of all types of obstacles was possible, that the manipulators could reach a neighborhood of their goal and that their final orientation approximated the desired orientation. Computer simulations illustrate these results.
This paper formulates a new scalable algorithm for motion planning and control of multiple point-mass robots. These autonomous robots are designated to move safely to their goals in a priori known workspace cluttered with fixed and moving obstacles of arbitrary positions and sizes. The control laws proposed for obstacle and collision avoidance and target convergence ensure that the equilibrium point of the given system is asymptotically stable. Computer simulations with the proposed technique and applications to a team of two planar (RP) manipulators working together in a common workspace are presented. Also, the robustness of the system in the presence of noise is verified through simulations.
This paper tackles the problem of motion planning and control of a car-like robot in an obstacle-ridden workspace. A kinematic model of the vehicle, governed by a homogeneous system of first-order differential equations, is used. A solution to the multi-tasking problem of target convergence, obstacle avoidance, and posture control is then proposed. The approach of solving the problem is two-fold. Firstly, a novel velocity algorithm is proposed to drive the car-like robot from its initial position to the target position. Secondly, a single layer artificial neural network is trained to avoid disc-shaped obstacles and provide corresponding weights, which are then used to develop a function for the steering angles. Thus, our method does not need a priori knowledge of the environment except for the goal position. With the help of the Direct Method of Lyapunov, it is shown that the proposed forms of the velocity and steering angle ensure point stability. For posture stability, we model the two parallel boundaries of a row-structured parking bay as continua of disk-shaped obstacles. Thus, our method is extendable to ensuring posture stability, which gives the desired final orientation. Computer simulations of the generated path are presented to illustrate the effectiveness of the method.
Email your librarian or administrator to recommend adding this to your organisation's collection.