A grip force and slippage control for robotic object manipulation, based on mechanical friction is presented. This approach allows a load to be held reliably in the robot gripper without application of excessive forces or allowing uncontrolled slippage. It is a simple, robust and low cost solution, and could be used for applications where low cost integrated grip force and slippage control are needed. This solution could be customized to provide reliable grip force and slippage control for light, medium or heavy load holding applications for a variety of different objects.

 This paper presents a new hierarchical control strategy for a hyper redundant articulate nimble adaptable trunk (ANAT) robot. The objective is to track a desired trajectory in the robots workspace. The pseudo-inverse of the Jacobian is used to transform the desired trajectory from the workspace to the joint space. The control strategy consists in controlling the last joint by assuming that the remaining joints are stable and follow their desired trajectories. Then going backward to the (n-1)-th joint, the same strategy is applied and so on until the first joint. In each step, the sliding mode technique is used to develop the control law. This control algorithm was experimented on a 7 DOF ANAT manipulator and gave effective results and good trajectory tracking in the workspace.

 In this article, the artificial potential field is computed for the obstacle avoidance problem of a mobile robot, using harmonic-functions. Given the maximum linear attainable velocity of the robot, equidistant points on the robot's resulting path are selected. A third-order Bezier-curve is used for time and space parameterization of each segment of the path between neighboring points. This curve is optimized so as the robot to be capable of moving along these segments given the maximum constraints on its linear and angular velocity. A simple non--linear feedback controller is used for tracking control of the trajectory. Simulation studies indicate the efficiency of the proposed method in optimizing the robot's trajectory.

 This paper presents a feature-based registration for 3D environments using mobile robots. The developed 3D laser scanner with custom hardware setup is able to scan both indoor and outdoor. For the map registration a nonlinear variant of the Iterative Closest Point (ICP) algorithm was used with initial alignment from the correspondences given by the features of the scenes. The initial alignment was determined using a set of key-points and the features of the keypoints in order to reduce the computational time and to ensure a robust estimation. Considering the increasing interest in 3D navigation for mobile robots, our aim was to use the created maps for both indoor and outdoor navigation purposes. Several maps were built by merging point clouds while our method was tested for a wide range of datasets including urban and office environments.

 In this paper we present a method that relies on swarm intelligence to confront the problem of navigating in non-structured dynamic environments. The collective behavior of a group of particles is used to find an obstacle-free path that is able to adapt its course as the structure of the environment changes. Of particular importance is the rule that governs collision avoidance in that it not only takes into account the positions of the obstacles, but also their velocities.