These days researchers are looking for design and development of autonomous robots. It is desirable that robots be capable to acquire the information they need to perform actions and make decisions. Any mobile or humanoid robot will need to construct a spatial representation or model of the surrounding environment that allows it to move and execute tasks with success. The reconstruction of an environment is an important and useful capability for these kind of robots. In order to construct a model, the robot needs to obtain information through a series of acquisitions from its sensors by solving occlusions. Therefore, an important issue is how to plan these robot placements (views) optimally, according to certain criteria for the purpose of reconstructing a complete model automatically. In this work we present a view planning algorithm to solve the problem of 3D modelling for indoor environments; the algorithm uses a volumetric representation as a reasoning domain. In this paper we propose the use of probability distribution functions as a model for the desirable behavior of the system, considering perception range data. The method uses a maximum a posteriori estimator to find the perception system parameters that defines the next best view position. We present results in simulation for a five degrees of freedom robot with a 3D range camera mounted on it to validate our approach.

For manipulating an unknown object a robot needs a 3D model of it. Given the limited field of view of a camera and self occlusions, a set of views is required to build a complete 3D model, so an important problem is how to select these views optimally according to certain criteria. We propose a novel algorithm to select the next-best-view (NBV) for a range camera to model 3D arbitrary objects. We use a volumetric representation and voxel labeling. We propose a new utility function based on factors considering voxel, quality and navigation information. We also propose two novel strategies to make faster the search of the NBV, one based on a hierarchical decomposition of the search space and other based on a multi-resolution of ray tracing. We have tested our planner in simulation with 7 different 3D objects, showing good results in terms of quality of the models and computation time required, and at the same time reducing the distance that the sensor has to travel to obtain the set of views.

The future personal robot must be capable to work and take decisions in a dynamic human environment. One important capability of such a robot is manipulation. Grasp, as the beginning of any manipulation task is a key point. In this paper we present a grasp planner for manipulating real objects modeled by polyhedra. The algorithm is based on a random generation of grasp oriented by mass and inertial properties of the object. Recent techniques to filter and evaluate the quality of grasp are discussed.