This paper describes a visual perception system for a social robot. The central part of this system is an artificial attention mechanism that discriminates the most relevant information from all the visual information perceived by the robot. It is composed by three stages. At the preattentive stage, the concept of saliency is implemented based on &#x2018;proto-objects&#x2019; [37]. From these objects, different saliency maps are generated. Then, the semiattentive stage identifies and tracks significant items according to the tasks to accomplish. This tracking process allows to implement the &#x2018;inhibition of return&#x2019;. Finally, the attentive stage fixes the field of attention to the most relevant object depending on the behaviours to carry out. Three behaviours have been implemented and tested which allow the robot to detect visual landmarks in an initially unknown environment, and to recognize and capture the upper-body motion of people interested in interact with it.

Juan Pedro Bandera

R. Marfil

Antonio Jes&#xfa;s Palomino

Ricardo V&#xe1;zquez-Mart&#xed;n

Antonio Bandera

Visual Attention Mechanism for a Social Robot

Learning by imitation represents a useful and promising alternative to program robots. The approach presented here is based on two functional elements used by humans to understand and perform actions. These elements are: the body schema and the body percept. The first one is a representation of the body that contains information of the body&#x2019;s capabilities. The body percept is a snapshot of the body and its relation with the environment at a given instant. These elements are believed to interact within humans to generate several abilities, among them the ability to imitate. This paper presents an approach to robot imitation based on these two functional elements, which approaches imitation by identifying the goals from the perceived actions.

Carlos A. Acosta Calderon

Rajesh E. Mohan

Task Learning Based on the Imitation of Object&#x2019;s Goal

This paper exploits optimal position of the mobile sensor to improve the target tracking performance of wireless sensor networks and simultaneously localize both of the static sensor nodes and mobile sensor nodes when tracking the human motion. In our approach, mobile sensors collaborate with static sensors and move optimally to achieve the required detection performance. The accuracy of final tracking result is then improved as the measurements of mobile sensors have higher signal-to-noise ratios after the movement. Specifically, we can simultaneously localize the mobile sensor and static sensors position when localizing the human&#x2019;s position based on augmented extended Kalman filters (EKF). In the algorithm, we develop a sensor movement optimization algorithm that achieves near-optimal system tracking performance. We also presented an sensor nodes management scheme in order to deduce the computation complexity when localizing the static sensor nodes. The effectiveness of our approach is validated by extensive simulations using the simulations.

Sen Zhang

Wendong Xiao

Jun Gong

Yixin Yin

Mobile Sensing and Simultaneously Node Localization in Wireless Sensor Networks for Human Motion Tracking

3D upper body pose estimation is a topic greatly studied by the computer vision society because it is useful in a great number of applications, mainly for human robots interactions including communications with companion robots. However there is a challenging problem: the complexity of classical algorithms that increases exponentially with the dimension of the vectors&#x2019; state becomes too difficult to handle. To tackle this problem, we propose a new approach that combines several annealing particle filters defined independently for each limb and belief propagation method to add geometrical constraints between individual filters. Experimental results on a real human gestures sequence will show that this combined approach leads to reliable results.

Ilaria Renna

Ryad Chellali

Catherine Achard

Combination of Annealing Particle Filter and Belief Propagation for 3D Upper Body Tracking

In this manuscript, an autonomous navigation algorithm for wheeled mobile robots (WMR) operating in dynamic environments (indoors or structured outdoors) is formulated. The planning scheme is of critical importance for autonomous navigational tasks in complex dynamic environments. In fast dynamic environments, path planning needs algorithms able to sense simultaneously a diversity of obstacles, and use such sensory information to improve real-time navigation control, while moving towards a desired goal destination. The framework tackles 4 issues. 1) Reformulation of the Social Force Model (SFM) adapted to WMR; 2) the cohesion of a general inertial scheme to represents motion in any coordinate system; 3) control of actuators rotational speed as a general model regardless kinematic restrictions; 4) assuming detection of features (obstacles/goals), adaptive numeric weights are formulated to affect navigational exponential components. Simulation and experimental outdoors results are presented to show the feasibility of the proposed framework.

Edgar A. Mart&#xed;nez-Garc&#xed;a

Rafael Torres-Cordoba

Exponential Fields Formulation for WMR Navigation

Some wheelchair users have difficulty looking backward when backing up in confined spaces due to limited neck range of motion or low vision, which can lead to collisions which may result in personal injury or property damage. The Drive Safe System (DSS) was evaluated in a controlled laboratory setting with blindfolded able-bodied individuals on various backward driving tasks. Performance with the DSS was compared with a standard white cane used for navigation assistance by people with visual impairment. Results indicate that the DSS significantly reduced the number of collisions compared to using a cane (p = 0.0001) alone. There was no difference in task completion time when participants were using the cane or the DSS (p = 0.915). Users rated the DSS favourably as they experience less total workload (p = 0.026), less physical demand (p = 0.006), felt less frustrated (p = 0.002) and put less effort (p = 0.007) to achieve better performance when using the DSS, compared to using a cane. These findings suggest that the DSS can be a viable powered mobility solution for wheelchair users with visual impairments.

Vinod Sharma

Richard Simpson

Edmund LoPresti

Mark Schmeler

Driving Backwards Using a Semi-Autonomous Smart Wheelchair System (DSS): A Clinical Evaluation

This paper proposes a fast colour-based object recognition and localization for soccer robots. The traditional HSL colour model is modified for better colour segmentation and edge detection in a colour coded environment. The object recognition is based on only the edge pixels to speed up the computation. The edge pixels are detected by intelligently scanning a small part of whole image pixels which is distributed over the image. A fast method for line and circle centre detection is also discussed. For object localization, 26 key points are defined on the soccer field. While two or more key points can be seen from the robot camera view, the three rotation angles are adjusted to achieve a precise localization of robots and other objects. If no key point is detected, the robot position is estimated according to the history of robot movement and the feedback from the motors and sensors. The experiments on NAO and RoboErectus teen-size humanoid robots show that the proposed vision system is robust and accurate under different lighting conditions and can effectively and precisely locate robots and other objects.

Tianwu Yang

Changjiu Zhou

Mohan Rajesh

A Fast Vision System for Soccer Robot

A novel gait planning method using geodesics for humanoid robot is given in this paper. Both the linear inverted pendulum model and the exact Single Support Phase (SSP) are studied in our energy optimal gait planning based on geodesics. The kinetic energy of a 2-dimension linear inverted pendulum is obtained at first. We regard the kinetic energy as the Riemannian metric and the geodesic on this metric is studied and this is the shortest line between two points on the Riemannian surface. This geodesic is the optimal kinetic energy gait for the COG because the kinetic energy along geodesic is invariant according to the geometric property of geodesics and the walking is smooth and energy saving. Then the walking in Single Support Phase is studied and the energy optimal gait for the swing leg is obtained using our geodesics method. Finally, experiments using state-of-the-art method and using our geodesics optimization method are carried out respectively and the corresponding currents of the joint motors are recorded. With the currents comparing results, the feasibility of this new gait planning method is verified.

Liandong Zhang

Smooth and Energy Saving Gait Planning for Humanoid Robot Using Geodesics

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Applied Bionics and Biomechanics publishes original research articles as well as review articles that seek to understand the mechanics of biological systems, or that use the functions of living organisms as inspiration for the design of new devices.

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Chief Editor, Professor Qiguo Rong, is the Vice Chair of the Department of Mechanics and Engineering Science at Peking University, China. His research focuses on the biomechanical behaviors of musculoskeletal systems.

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