The present paper investigates an intuitive way of robot path planning, called robot teaching by demonstration. In this method, an operator holds the robot end-effector and moves it through a number of positions and orientations in order to teach it a desired task. The presented control architecture applies impedance control in such a way that the end-effector follows the operator’s hand with d...

Planning optimal paths for large numbers of robots is computationally expensive. In this thesis, we present a new framework for multirobot path planning called subdimensional expansion, which initially plans for each robot individually, and then coordinates motion among the robots as needed. More specifically, subdimensional expansion initially creates a one-dimensional search space embedded in...

Robot path planning is a NP problem; traditional optimization methods are not very effective to solve it. Traditional genetic algorithm trapped into the local minimum easily. Therefore, based on a simple genetic algorithm and combine the base ideology of orthogonal design method then applied it to the population initialization, using the intergenerational elite mechanism, as well as the introdu...

In this paper, we investigate methods for motion planning for deformable robots. Our framework is based on a probabilistic roadmap planner. As with traditional motion planning, the planner’s goal is to find a valid path for the robot. Unlike typical motion planning, the robot is allowed to change its shape (deform) to avoid collisions as it moves along the path. We propose a two-stage approach....

In This study, a new artificial fish-swarm optimization, to improve the foraging behavior of artificial fish swarm algorithm is closer to reality in order to let the fish foraging behavior, increase a look at the link (search) ambient, after examining environment, artificial fish can get more status information of the surrounding environment. Artificial fish screened from the information obtain...

Humans and robots think in different ways: humans evaluate qualitatively while robots process quantitative data. For example, a human may say “I am at the east of the building” in expressing a location, while a robot may use a precise coordinate. Similarly a human may specify “run carefully” in requiring a behavior pattern, but a robot may read linear and angular velocities. There is a need to ...

In this article we introduce a neural eld approach for local path planning of an autonomous mobile robot. The robot's heading direction is determined by the localized peak and its velocity by the maximum activation in the eld. We emphasize the neural eld's ability to keep the path planning stable even in the case of noisy sensor data or varying environments. The theoretical framework is validat...

This paper presents a weighted path planning approach for a light weight robot coming into compliant contact with the environment, as well as robot‐ environment interaction enabled impedance control. Using a joint torque sensor, Cartesian impedance control is introduced to realize the manipulator compliance control. Then the weighted path planning approach is de...

The multi-robot coverage path-planning problem involves finding collision-free paths for a set of robots so that they can completely cover the surface of an environment. This problem is non-trivial as the geometry and location of obstacles in the environment is usually not known a priori by the robots, and they have to adapt their coverage path as they discover obstacles while moving in the env...