Motion Planning for an Underactuated Planar Robot in a Viscous Environment

2015 ◽  
Vol 10 (5) ◽  
Author(s):  
Elie Shammas ◽  
Daniel Asmar
Keyword(s):  
Motion Planning ◽  
Degrees Of Freedom ◽  
Mechanical Systems ◽  
Planning Problem ◽  
Frictional Forces ◽  
Planar Robot ◽  
Motion Planning Problem

In this paper, we solve the motion planning problem for a class of underactuated multibodied planar mechanical systems. These systems interact with the environment via viscous frictional forces. The motion planning problem is solved by specifying the location of friction pads on the robot as well as by specifying the input of the actuated degrees of freedom. Moreover, through the proposed novel motion planning analysis, we identify the simplest planar swimming robot, the two-link swimmer.

Motion Planning and Differential Flatness of Mechanical Systems on Principal Bundles

2015 ◽  
Author(s):  
Tony Dear ◽  
Scott David Kelly ◽  
Matthew Travers ◽  
Howie Choset
Keyword(s):  
Nonlinear Control ◽  
Motion Planning ◽  
Geometric Structure ◽  
Mechanical Systems ◽  
Differential Flatness ◽  
Planning Problem ◽  
Principal Bundles ◽  
General Motion ◽  
Motion Planning Problem

Mechanical systems often exhibit physical symmetries in their configuration variables, allowing for significant reduction of their mathematical complexity arising from characteristics such as underactuation and nonlinearity. In this paper, we exploit the geometric structure of such systems to explore the following motion planning problem: given a desired trajectory in the workspace, can we explicitly solve for the appropriate inputs to follow it? We appeal to results on differential flatness from the nonlinear control literature to develop a general motion planning formulation for systems with symmetries and constraints, which also applies to both fully constrained and unconstrained kinematic systems. We conclude by demonstrating the utility of our results on several canonical mechanical systems found in the locomotion literature.

Motion Planning of a Nonholonomic Multibody System Using Genetic Algorithm

2003 ◽  
Author(s):  
Xin-Sheng Ge ◽  
Li-Qun Chen
Keyword(s):  
Genetic Algorithm ◽  
Motion Planning ◽  
Nonholonomic System ◽  
Multibody System ◽  
Planning Problem ◽  
Control Approach ◽  
Velocity Constraints ◽  
Nonholonomic Motion Planning ◽  
Optimal Control Approach ◽  
Motion Planning Problem

The motion planning problem of a nonholonomic multibody system is investigated. Nonholonomicity arises in many mechanical systems subject to nonintegrable velocity constraints or nonintegrable conservation laws. When the total angular momentum is zero, the control problem of system can be converted to the motion planning problem for a driftless control system. In this paper, we propose an optimal control approach for nonholonomic motion planning. The genetic algorithm is used to optimize the performance of motion planning to connect the initial and final configurations and to generate a feasible trajectory for a nonholonomic system. The feasible trajectory and its control inputs are searched through a genetic algorithm. The effectiveness of the genetic algorithm is demonstrated by numerical simulation.

Motion planning problem with obstacle avoidance for step-2 bracket-generating systems

PAMM ◽  
2017 ◽  
Vol 17 (1) ◽  
pp. 799-800 ◽  
Author(s):  
Victoria Grushkovskaya ◽  
Alexander Zuyev
Keyword(s):  
Motion Planning ◽  
Obstacle Avoidance ◽  
Planning Problem ◽  
Motion Planning Problem

Solving the motion planning problem by using neural networks

Robotica ◽  
1994 ◽  
Vol 12 (4) ◽  
pp. 323-333 ◽  
Author(s):  
R.H.T. Chan ◽  
P.K.S. Tam ◽  
D.N.K. Leung
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Motion Planning ◽  
Configuration Space ◽  
Logic Gates ◽  
Moving Object ◽  
Planning Problem ◽  
Processing Unit ◽  
Configuration Point ◽  
Motion Planning Problem

SUMMARYThis paper presents a new neural networks-based method to solve the motion planning problem, i.e. to construct a collision-free path for a moving object among fixed obstacles. Our ‘navigator’ basically consists of two neural networks: The first one is a modified feed-forward neural network, which is used to determine the configuration space; the moving object is modelled as a configuration point in the configuration space. The second neural network is a modified bidirectional associative memory, which is used to find a path for the configuration point through the configuration space while avoiding the configuration obstacles. The basic processing unit of the neural networks may be constructed using logic gates, including AND gates, OR gates, NOT gate and flip flops. Examples of efficient solutions to difficult motion planning problems using our proposed techniques are presented.

Sign in / Sign up

Export Citation Format

Share Document