This paper presents the software tool used at the Institute of Industrial and Control Engineering (IOC-UPC) for teaching and research in robot motion planning. The tool allows to cope with problems with one or more robots, being a generic robot defined as a kinematic tree with a mobile base, i.e. the tool can plan and simulate from simple two degrees of freedom free-flying robots to multi-robot scenarios with mobile manipulators equipped with anthropomorphic hands. The main core of planners is provided by the Open Motion Planning Library (OMPL). Different basic planners can be flexibly used and parameterized, allowing students to gain insight into the different planning algorithms. Among the advanced features the tool allows to easily define the coupling between degrees of freedom, the dynamic simulation and the integration with task planers. It is principally being used in the research of motion planning strategies for hand-arm robotic systems.

Bronchoscopy is an interventional medical procedure employed to analyze the interior side of the human airways, clear possible obstructions and biopsy. Using a 3D reconstruction of the tracheobronchial tree, Virtual Bronchoscopy (VB) may help physicians in the exploration of peripheral lung lesions. We are developing a haptic-based navigation system for the VB that allows the navigation within the airways using a haptic device whose permitted motions mimics those done with the real bronchoscope. This paper describes the motion planning module of the system devoted to plan a path from the trachea to small peripheral pulmonary lesions, that takes into account the geometry and the kinematic constraints of the bronchoscope. The motion planner output is used to visually and haptically guide the navigation during the virtual exploration using the haptic device. Moreover, physicians can get useful information of whether the peripheral lesions can effectively be reached with a given bronchoscope or of which is the nearest point to the lesion that can be reached.

Teleoperation tasks are performed at cartesian level when the robot and the haptic device have dissimilar kinematics. If the size of the workspaces is also dissimilar, as it is usually the case, the mapping between workspaces must be handled with care in order to let the user teleoperate the robot in a natural and precise way. This paper formulates the mapping of workspaces based on the re-synchronization method and proposes an assisted system that lightens the user from the tedious part of the method, by guiding him/her towards the best re-synchronization position, thus minimizing the number of jumps. The proposal is part of a teleoperated guiding system being developed by the authors.

One of the key factors that affect the success and efficiency of sampling-based path planners is the obtention of samples in the more relevant regions of the workspace. This is known as importance sampling, and different approaches have already been proposed in this direction. This paper proposes a novel method to bias sampling by means of geometric constraints that reduces the sampling space to sets of lower dimensional submanifolds. These constraints may be imposed by the kinematic structure of the actuation system, by the task specification, or provided by a human user as an intuitive way to include problem knowledge to the planner. The method has been implemented and tested on a probabilistic roadmap planner giving promising results. A variant using a deterministic sampling source is also reported.

The paper deals with the problem of motion planning of anthropomorphic mechanical hands avoiding collisions. The proposed approach tries to mimic the real human hand motions, but reducing the dimension of the search space in order to obtain results as a compromise between motion optimality and planning complexity (time) by means of the concept of principal motion directions. Basically, the work includes the following phases: capturing the human hand workspace using a sensorized glove and mapping it to the mechanical hand workspace, reducing the space dimension by looking for the most relevant principal motion directions, and planning the hand movements using a sampling-based roadmap planner. The approach has been implemented for a four finger anthropomorphic mechanical hand, and some examples are included to illustrate its validity.

The planning of collision-free motions of a hand-arm system to reach a grasp or preshape configuration is not a simple issue due to the high number of involved degrees of freedom. This paper presents an efficient sampling-based path planner that copes with this issue by considering a reduced search space. The dimension of this space is not fixed but it is iteratively increased according to the difficulty of the task at hand. Initially the search space is 1-dimensional along the line defined by the initial and goal hand configurations (by construction those configurations always belong to the search space), and then its dimension is increased by iteratively adding principal motion directions (that couple the finger motions), trying in this way to produce hand movements through anthropomorphic natural postures.

Hierarchical cell decompositions of Configuration Space can be of great value for enhancing sampling-based path planners, as well as for other robotic tasks with requirements beyond the planning of free paths. This paper proposes an efficient method to obtain a hierarchical cell decomposition of C-space that is based on: a) the use of a deterministic sampling sequence that allows an uniform and incremental exploration of the space, and b) the use of distance measurements to handle as much information as possible from each sample in order to make the procedure more efficient. The proposed cell decomposition procedure is applied to different path planning methods.