Mobile robot global path planning in a static environment is an important problem. The paper proposes a method of global path planning based on neural network and genetic algorithm. We constructed the neural network model of environemntal information in the workspace for a robot and used this model to establish the relationship between a collision avoidance path and the output of the model. Then the two-dimensional coding for the path via-points was converted to one-dimensional one and the fitness of both the collision avoidance path and the shortest distance are integrated into a fitness function. The simulation results showed that the proposed method is correct and effective.

Neural network and genetic algorithm based global path planning in a static environment

We present new techniques that use motion planning algorithms based on probabilistic roadmaps to control 22 degrees of freedom (DOFs) of human-like characters in interactive applications. Our main purpose is the automatic synthesis of collision-free reaching motions for both arms, with automatic column control and leg flexion. Generated motions are collision-free, in equilibrium, and respect articulation range limits. In order to deal with the high (22) dimension of our configuration space, we bias the random distribution of configurations to favor postures most useful for reaching and grasping. In addition, extensions are presented in order to interactively generate object manipulation sequences: a probabilistic inverse kinematics solver for proposing goal postures matching pre-designed grasps; dynamic update of roadmaps when obstacles change position; online planning of object location transfer; and an automatic stepping control to enlarge the character's reachable space. This is, to our knowledge, the first time probabilistic planning techniques are used to automatically generate collision-free reaching motions involving the entire body of a human-like character at interactive frame rates.

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Planning collision-free reaching motions for interactive object manipulation and grasping

We present new techniques that use motion planning algorithms based on probabilistic roadmaps to control 22 degrees of freedom (DOFs) of human-like characters in interactive applications. Our main purpose is the automatic synthesis of collision-free reaching motions for both arms, with automatic column control and leg flexion. Generated motions are collision-free, in equilibrium, and respect articulation range limits. In order to deal with the high (22) dimension of our configuration space, we bias the random distribution of configurations to favor postures most useful for reaching and grasping. In addition, extensions are presented in order to interactively generate object manipulation sequences: a probabilistic inverse kinematics solver for proposing goal postures matching predesigned grasps; dynamic update of roadmaps when obstacles change position; online planning of object location transfer; and an automatic stepping control to enlarge the character's reachable space. This is, to our knowledge, the first time probabilistic planning techniques are used to automatically generate collision-free reaching motions involving the entire body of a human-like character at interactive frame rates

This paper deals with the trajectory planning problem for redundant manipulators. A genetic algorithm (GA) using a floating point representation is proposed to search for the optimal end-effector trajectory for a redundant manipulator. An evaluation function is defined based on multiple criteria, including the total displacement of the end-effector, the total angular displacement of all the joints, as well as the uniformity of Cartesian and joint space velocities. These criteria result in minimized, smooth end-effector motions. Simulations are carried out for path planning in free space and in a workspace with obstacles. Results demonstrate the effectiveness and capability of the proposed method in generating optimized collision-free trajectories.

Motion Planning for Redundant Manipulators Using a Floating Point Genetic Algorithm

A simple path planning scheme is proposed for navigation of mobile robots while avoiding obstacles. In generating the goal directed dynamic path, the path planning scheme uses a genetic search algorithm whose coding technique speeds up the execution of genetic search for fast path generation. The fitness value of the generated paths is evaluated in terms of the safety from the obstructing dynamic objects and the distance to the goal position by the genetic algorithm. The execution time of genetic search is further accelerated by projecting the two dimensional data to one dimensional ones to reduce the size of search space.

Genetic algorithm based path planning and dynamic obstacle avoidance of mobile robots

The authors introduce a novel method for obstacle avoidance with redundant robot manipulators based on genetic algorithms (GAs), GAs are search procedures which duplicate many of the processes of natural genetics. GAs work with a coding of the parameters. The use of a 30-b ternary number to represent the moves associated with a three-degree-of-freedom robot is discussed. A finite number of incremental moves are used to guide the end-effector to the goal point through a collision-free path. Two example runs are described, and advantages and disadvantages of the method are discussed. >

J.K. Parker

Papers

Obstacle avoidance of redundant manipulators using genetic algorithms