Volume 1, Issue 4

This paper deals with controller design issues for a neurosurgical teleoperator system. The specific application of interest consists of remotely inserting a linear-stage rigid endoscope into the patient's brain for microinvasive neurosurgery interventions. This work aims at evaluating advantages and drawbacks of using a general-purpose control architecture versus a simpler task-oriented architecture, from a point of view of stability and transparency. Experiments revealed that in spite of its simplicity, the task-oriented design allows an improvement in the trade-off between performance, transparency and stability requirements.

This paper reviews modern geometrical dynamics and control of humanoid robots. This general Lagrangian and Hamiltonian formalism starts with a proper definition of humanoid's configuration manifold, which is a set of all robot's active joint angles. Based on the ‘covariant force law’, the general humanoid's dynamics and control are developed. Autonomous Lagrangian dynamics is formulated on the associated ‘humanoid velocity phase space’, while autonomous Hamiltonian dynamics is formulated on the associated ‘humanoid momentum phase space’. Neural-like hierarchical humanoid control naturally follows this geometrical prescription. This purely rotational and autonomous dynamics and control is then generalized into the framework of modern non-autonomous biomechanics, defining the Hamiltonian fitness function. The paper concludes with several simulation examples.

This paper describes a system integration of daily assistive robots. Several tasks related to cleaning and tidying up rooms are focused on. Recognition and motion generation functions were integrated onto the robot, and these provided failure detection and in some cases, its recovery. Experiments of several daily tasks handling daily tools showed the effectiveness of our system.

Non-facial and non-verbal methods of affective expression are essential for social interaction in appearance-constrained robots such as those used in search and rescue, law enforcement, and military applications. This research identified five main methods of non-facial and non-verbal affective expression (body movements, postures, orientation, color, and sound). Based on an extensive review of literature, prescriptive design recommendations were developed for the appropriate non-facial and non-verbal affective expression methods for three proximity zones of interest (intimate, personal, and social). These design recommendations serve as guidelines to add retroactively affective expression through software with minimal or no physical modification to a robot. A large-scale, complex human-robot interaction study was conducted to validate these design recommendations using 128 participants and four methods of evaluation. The study was conducted in a high-fidelity, confined-space simulated disaster site with all robot interactions performed in the dark. Statistically significant results indicated that participants felt the robots that exhibited affective expressions were more calming, friendly, and attentive, which improved the social human-robot interactions.

Different approaches have been tried to navigate robots, including those based on visual memories. The Sparse Distributed Memory (SDM) is a kind of associative memory based on the properties of high dimensional binary spaces. It exhibits characteristics such as tolerance to noise and incomplete data, ability to work with sequences and the possibility of one-shot learning. Those characteristics make it appealing to use for robot navigation. The approach presented in this work was to navigate a robot using sequences of visual memories stored into a SDM. The robot makes intelligent decisions, such as selecting only relevant images to store during path learning, adjusting memory parameters to the level of noise and inferring new paths from learnt trajectories. The method of encoding the information may influence the tolerance of the SDM to noise and saturation. The present paper reports novel results of the limits of the model under different typical navigation problems. An algorithm to build a topological map of the environment based on the visual memories is also described.