The idea underlying this Special Issue arises from previous successfully international events organized in this robotics context. Thus, during 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, hosted in Edmonton, Canada, a Workshop, with the same title was successfully organized by this guest editor. Moreover, this editor was involved in this research area, as co-Chair of the “Manipulation and Grasping Interest Group”, within the European Robotics Research Network (i.e. EURON), from 2001, organizing also a couple of International Summer Schools, supported by EURON, on these topics (Spain, 2001 and 2004). On the other hand, as time goes by, more and more robotics applications are oriented towards working in all kind of service domains, such as hospitals, museums, etc. Hence, the interest on those robotic systems, integrating manipulation and navigation capabilities, namely mobile manipulators, is drastically increasing around the entire world. Therefore, this special issue is trying to face this new scenario providing a comprehensive overview of some key topics, foundations and applications within the Mobile Manipulators context, including human-robot interaction aspects and critical issues related with navigation and manipulation performance, among others.

We consider the task-oriented modeling of the differential kinematics of nonholonomic mobile manipulators (NMMs). A suitable NMM Jacobian is defined that relates the available input commands to the time derivative of the task variables, and can be used to formulate and solve kinematic control problems. When the NMM is redundant with respect to the given task, we provide an extension of two well-known redundancy resolution methods for fixed-base manipulators (Projected Gradient and Task Priority) and introduce a novel technique (Task Sequencing) aimed at improving performance, e.g., avoiding singularities. The proposed methods are applied then to the specific case of image-based visual servoing, where the NMM image Jacobian combines the interaction matrix and the kinematic model of the mobile manipulator. Comparative numerical results are presented for two case studies.

Wheeled Mobile Manipulators (WMM) possess many advantages over fixed-base counterparts in terms of improved workspace, mobility and robustness. However, the combination of the nonholonomic constraints with the inherent redundancy limits effective exploitation of end-effector payload manipulation capabilities. The dynamic-level redundancy-resolution scheme presented in this paper decomposes the system dynamics into decoupled task-space (end-effector motions/forces) and a dynamically consistent null-space (internal motions/forces) component. This simplifies the subsequent development of a prioritized task-space control (of end-effector interactions) and a decoupled but secondary null-space control (of internal motions) in a hierarchical WMM controller. Various aspects of the ensuing novel capabilities are illustrated using a series of simulation results.

The work presented in this paper aims at providing a unified modelling framework for the reactive control of wheeled mobile manipulators (WMM). Where most work in the literature often provides models, sometimes simplified, of a given type of WMM, an extensive description of obtaining explicit kinematic and dynamic models of those systems is given. This modelling framework is particularly well suited for reactive control approaches, which, in the case of mobile manipulation missions, are often necessary to handle the complexity of the tasks to be fulfilled, the dynamic aspect of the extended workspace and the uncertainties on the knowledge of the environment. A flexible reactive framework is thus also provided, allowing the sequencing of operational tasks (in our case, tasks described in the end-effector frame) whose natures are different but also an on-line switching mechanism between constraints that are to be satisfied using the system redundancy. This framework has been successfully implemented in simulation and on a real robot. Some of the obtained results are presented.

The problem studied in this paper is a mobile robot that autonomously navigates in a domestic environment, builds a map as it moves along and localizes its position in it. In addition, the robot detects predefined objects, estimates their position in the environment and integrates this with the localization module to automatically put the objects in the generated map. Thus, we demonstrate one of the possible strategies for the integration of spatial and semantic knowledge in a service robot scenario where a simultaneous localization and mapping (SLAM) and object detection recognition system work in synergy to provide a richer representation of the environment than it would be possible with either of the methods alone. Most SLAM systems build maps that are only used for localizing the robot. Such maps are typically based on grids or different types of features such as point and lines. The novelty is the augmentation of this process with an object-recognition system that detects objects in the environment and puts them in the map generated by the SLAM system. The metric map is also split into topological entities corresponding to rooms. In this way, the user can command the robot to retrieve a certain object from a certain room. We present the results of map building and an extensive evaluation of the object detection algorithm performed in an indoor setting.

The proposed algorithm integrates in a single planner the global motion planning and local obstacle avoidance capabilities. It efficiently guides the robot in a dynamic environment. This eliminates some of the traditional problems of planned architectures (model-plan-act scheme) while obtaining many of the qualities of behavior-based architectures. The computational efficiency of the method allows the planner to operate at high-rate sensor frequencies. This avoids the need for using both a collision-avoidance algorithm and a global motion planner for navigation in a cluttered environment. The method combines map-based and sensor-based planning operations to provide a smooth and reliable motion plan. Operating on a simple grid-based world model, the method uses a fast marching technique to determine a motion plan on a Voronoi extended transform extracted from the environment model. In addition to this real-time response ability, the method produces smooth and safe robot trajectories.

This paper deals with sensor-based motion planning method for a robot arm manipulator operating among unknown obstacles of arbitrary shape. It can be applied to online collision avoidance with no prior knowledge of the obstacles. Infrared sensors are used to build a description of the robot's surroundings. This approach is based on the configuration space but the construction of the C-obstacle surface is avoided. The point automation is confined on some planes with square grids in the C-space. A path-searching algorithm based on square grids is used to guide the automation maneuvering around the C-obstacles on the selected planes. To avoid the construction of the C-obstacle surface, the robot geometry model is expanded, and the static collision detection method is used. Hence, the computation time is reduced and the algorithm can work in real time. The effectiveness of the proposed method is verified by a series of experiments.

Mobile manipulation involves the most important key issue in robotics: integration. While hardware integration seems to be nearly solved due to the increasing dominance of PC-compatible systems, software integration is still a challenge, since a lot of issues arise with the variety of operating systems, device drivers, application libraries, and programming languages which need to be merged in any real-world robotic system. This paper presents a software architecture, which seamlessly integrates robot arms, mobile bases, vision systems and sensing devices, in a distributed, homogeneous agent framework. Based on the Java platform, the agent-based architecture allows great flexibility in the integration of components, and provides a simple yet extensible and powerful software layer to develop further mobile manipulating environments. Detailed software issues, as well as preliminary results are shown, which pave the way towards the development of network-ready applications involving mobile and manipulating artifacts.

Mobile robotic manipulators can augment the strength and dexterity of human operators in unstructured environments. Here, the control system for a six degree-of-freedom heavy-lift mobile manipulator for lifting and inserting payloads on the deck of a ship is described. The robotic hardware and the application present several control challenges, including structural resonances, high joint friction that varies with time, limited sensors for measuring the joint friction, complex interaction with the environment, tight tolerances for the insertion tasks, lack of bilateral force feedback of the contact forces, and ship motions. The control system enables an operator to perform insertion tasks using feedback of tactile clues of the manipulator position, and reduces the effects of friction with a combination of sensor-based, adaptive, and model-based methods of friction compensation. The control architecture is validated in simulation and on a laboratory manipulator.

This paper presents an approach to reduce the technical complexity of a service robotic system by means of systematic and well-balanced user-involvement. By taking advantage of the user's cognitive capabilities during task execution, a technically manageable robotic system, which is able to execute tasks on a high level of abstraction reliably and robustly, emerges. For the realisation of this approach, the control architecture MASSiVE has been implemented, which is used for the control of the rehabilitation robot FRIEND II. It supports task execution on the basis of a priori defined and formally verified task-knowledge. This task-knowledge contains all possible sequences of operations as well as the symbolic representation of objects required for the execution of a specific task. The seamless integration of user interactions into this task-knowledge, in combination with MASSiVE's user-adapted human–machine interface layer, enables the system to deliberately interact with the user during run-time.

This paper presents a modular control architecture that enables a mobile manipulator to be compliant and perform manipulation tasks in partially known everyday human environments. An impedance velocity/force controller that allows the execution of a great variety of tasks under the Task Frame Formalism (TFF) is implemented.

Tasks are represented as a net of basic abilities which are performed by the robot using the impedance controller. Mechanisms for switching between abilities according to the robot's perceptual state are defined.

We show the validity of our approach on the UJI Service Robot, making it to perform a common daily task such as opening a door. Finally, we apply this framework to make progress on the new version of the UJI Librarian Robot, making a great step forward in the way the robot manipulates books.