Ricardo Carelli

Passivity-based visual feedback control with dynamic compensation of mobile manipulators

This paper addresses the problem of visual dynamic control based on passivity to solve the target tracking problem of mobile manipulators with eyes-in-hand configuration in the 3D-workspace. The redundancy of the system is used for obstacles avoidance and singular configuration prevention through the systems manipulability control. The design of the stable control system is based on its passivity properties. A robustness analysis and an L 2 -gain performance analysis are also presented. Finally, simulation and experimental results are reported to verify the stability and L 2 -gain performance of the dynamic visual feedback system. We address the problem of visual control to solve the target tracking problem.We consider both the kinematic and dynamic models in the control system design.The design of the stable control system is based on its passivity properties.A robustness analysis and an L 2 -gain performance analysis are also presented.Simulations and experimental results are shown to verify the systems performance.

Adaptive Coordinated Cooperative Control of Multi-Mobile Manipulators

A coordinated group of robots can execute certain tasks, e.g. surveillance of large areas (Hougen et al., 2000), search and rescue (Jennings et al., 1997), and large objectstransportation (Stouten and De Graaf, 2004), more efficiently than a single specialized robot (Cao et al., 1997). Other tasks are simply not accomplishable by a single mobile robot, demanding a group of coordinated robots to perform it, like the problem of sensors and actuators positioning (Bicchi et al., 2008), and the entrapment/escorting mission (Antonelli et al., 2008). In such context, the term formation control arises, which can be defined as the problem of controlling the relative postures of the robots of a platoon that moves as a single structure (Consolini et al., 2007).

Nonlinear Stable Formation Control Using Omnidirectional Images

There are a lot of applications that are better performed by a multi-robot team than a single agent. Multi-robot systems may execute tasks in a faster and more efficient way and may also be more robust to failure than a single robot. There are even some applications that can not be achieved by only one robot and just by a group of them (Parker, 2003; Cao et al., 1997). Another known advantage of multi-robot systems is that instead of using one expensive robot with high processing capacity and many sensors, sometimes one can use a team of simpler and inexpensive robots to solve the same task. Some examples of tasks that are well performed by cooperative robots are search and rescue missions, load pushing, perimeter surveillance or cleaning, surrounding tasks, mapping and exploring. In these cases, robots may share information in order to complement their data, preventing double searching at an already visited area or alerting the others to concentrate their efforts in a specific place. Also the group may get into a desired position or arrangement to perform the task or join their forces to pull or push loads. Although multi-robot systems provide additional facilities and functionalities, such systems bring new challenges. One of these challenges is formation control. Many times, to successfully perform a task, it is necessary to make robots get to specific positions and orientations. Within the field of robot formation control, control is typically done either in a centralized or decentralized way. In a centralized approach a leader, which can be a robot or an external computer, monitores and controls the other robots, usually called followers. It coordinates tasks, poses and actions of the teammates. Most of the time, the leader concentrates all relevant information and decides for the whole group. The centralized approach represents a good strategy for small teams of robots, specially when the team is implemented with simple robots, only one computer and few sensors to control the entire group. In (Carelli et al., 2003) a centralized control is applied to coordinate the movement of a number of non-holonomic mobile robots to make them reach a pre-established desired formation that can be fixed or dynamic. There are also the so called leader-follower formation control as (Oliver & Labrosse, 2007; Consolini et al., 2007), in which the followers must track and follow the leader robot. The

Design of a robotic cane to assist people with disabilities

In the present days a percentage increase of the population with disability issues is observed. Not only are motor disabilities, sensory and cognitive too. One reason is the growth of the elderly population. There are mechanical devices for their assistance, the most used is the cane. This paper intends to apply technologies in the area of robotics to develop a robotic cane considering the hypothesis that it is more efficient to facilitate and induce a normal gait, and simultaneously address sensory and cognitive disabilities.

Robot Manipulator Probabilistic Workspace Applied to Robotic Assistance

The probabilistic modelling of a robot manipulator workspace when combined with a Human-Machine Interface (HMI) allows the extraction and learning of the spatial preferences of the user. Furthermore, the knowledge of the most accessed zones of the robot’s workspace permits the bounding of the time needed for the robot to reach a given position at its workspace. From its early beginning, the use of robot manipulators within the robotic assistance field was concerned to emulate an orthopaedic arm (Fukuda et al, 2003; Zecca et al, 2002; Lopez et al, 2009). Therefore, the robot manipulator was considered as the final actuator of the assistive system where its main goal was to imitate the movements of an arm. Depending on the user/patient capabilities, the robot manipulator was commanded by either electromyographic or electro-encephalic signals (Ferreira et al, 2006a; 2006b; 2008). A robotic device controlled by a Muscle-Computer Interface (MCI) can be found in (Artemiadis & Kyriakopoulos, 2006; Lopez et al, 2007; Millan et al, 2004; Ferreira et al, 2006b; Lopez et al, 2009; Ferreira et al, 2008). In these works, the electro-miographic signal is acquired, processed, classified and converted to motion commands. The system is closed by a biofeedback loop. When used with a robot manipulator, a MCI is usually connected to a set of muscles that the patient is able to move at its own will. The number of channels used by the interface increases as increases the number of the degrees of freedom (DOF) of the robot (Yatsenko et al, 2007; Lopez et al, 2009; Ferreira et al, 2008). Thus, for a single 2DOF robot manipulator are necessary three different muscles: two to govern each DOF and a third to set a sign (if the manipulator is moving to the left or to the right), for a direct control of the robot manipulator. For robotic devices controlled by Brain-Computer Interfaces (BCI’s), the situation is analogous to the MCI; the number of signals or patterns to be extracted from the EEG (electro-encephalogram) increases as increases with the number of DOF’s –or actions– to be performed by the robot. Although most applications of BCI’s are oriented to govern a mobile robot –because of its direct analogy with a motorized wheelchair (Bastos Filho et al, 2007a; Bastos Filho, 2007; Ferreira et al, 2008; Bastos Filho et al, 2007b)– some works have been published showing the implementation of a BCI to control the movements of a robot 22

Open Software Structure for Controlling Industrial Robot Manipulators

Automatic control has become an important part of the modern industrial processes. Progress both in basic research as applied to automatic control, provide a way to obtain the optimum performance of the dynamical systems, improve the quality and reduce the costs. Robotics, as a part of automatics, represents nowadays an important research area, and it has an essential role in the productive modernization (UNECE and IFR, 2005). The inclusion of industrial manipulators in the manufacturing process allows obtaining better and cheaper products. Therefore, the development of an open software structure for the industrial robots controlling is a very important objective to be achieved (William, 1994), (Frederick and Albus, 1997). The main characteristic of an open software structure for robotics applications is the interface that relates the components of the robot with the basic internal structure. In industrial area, one of the most important works was developed in the framework of the European project OSACA (Open System Architecture for Control within Automation Systems). Similarly, significant contributions were reached in Japan through OSEC (Open System Environment for Controllers) under IROFA (International Robotics and Factory Automation Center), (Sawada and Akira, 1997), and in the United States of America through OMAC (Open Modular Architecture Control). The objective of all these research projects is to develop an open control system including the reference model of the components, the general application interface and the structure so that all the components work together. So far manufacturers do not work together to develop standard control software that could be applied to any industrial robot. On the other hand, several commercial software packages, that run under Windows, for mobile robots can be found. Among the best known ones, Advanced Robotics Interface for Applications (ARIA) is used in the robots manufactured by Mobile Robots Inc., BotController software were developed by MobotSoft and it is used for the well known Khepera and Koala robots. Even when these software packages are powerful and have many benefits, they can be applied only to the robots that were developed. 23