Advanced Robotics

Komatsu, Hirone; Endo, Gen; Hodoshima, Ryuichi; Hirose, Shigeo; Fukushima, Edwardo F.

doi: 10.1080/01691864.2015.1081103pmid: N/A

Reduction of the energy consumption is one of the most important problems to utilize quadruped walking robots for various works on rugged terrain. The authors have studied basic strategy to achieve high energy efficiency when the quadruped walking robot do the motion essentially requires positive power by the analysis of body rising motion. This paper discusses the energy efficiency of the slope walking motion by the quadruped walking robot. First, we investigate the walking posture in consideration of ideal actuator characteristics where the robot consumes few negative powers at each joint which causes the main energy loss of the walking robot. Then, we investigate optimal walking posture in consideration of DC motor characteristics by the full search of three gait parameters which define the crawl gait. Furthermore, we derive the optimal walking motion by the optimization of three gait parameters which are kept constant during one cycle gait and instantaneous parameters such as body velocity and supporting forces changed at each moment simultaneously.

Tang, Yongchen; Ma, Shugen; Sun, Yi; Ge, Dingxin

doi: 10.1080/01691864.2015.1070105pmid: N/A

This paper proposes a new legged walking method for a novel passive-spine hexapod robot. This robot consists of several body segments connected by passive body joints. Each of the body segments carries two 1-DoF (degree of freedom) actuated legs. The robot is capable of achieving planar legged walking by rapidly abducting and adducting its legs. To model the mobility of a robot based on this simple design, the candidate configurations from all possible configurations are first selected in a mobility analysis of the robot based on the screw theory. All the feasible sequences of these candidate configurations are then searched to form planar locomotion gaits. Next, locomotive performance of the gaits is analyzed. Finally, the proposed locomotion design and gait planning methods are verified through simulations and experiments.

Mahmoodi, Ali; Sayadi, Amir

doi: 10.1080/01691864.2015.1076345pmid: N/A

A closed-form formulation for the workspace of N-Degrees-of-Freedom (DoF) parallel mechanisms is presented using least-square curve fitting. The concepts of multi-dimensional polynomial (MDP) and in hyper spherical coordinates (HSC) are introduced. The boundary of workspaces of those parallel linkages which are surfaces without voids and concavities can be well approximated by the MDPs in the HSC. First, the boundary points are obtained in HSC considering all physical constrains and probable singularities. Then, an MDP with proper order consisting of sine and cosine functions is fitted to those points. The presented method is general and applicable to all DoF. Three case studies of 2, 3, and 6-DoF mechanisms are investigated to demonstrate the effectiveness of the method.

Dang, Viet-Hung; Thang, Nguyen Duc; Viet, Hoang Huu; Tuan, Le Anh

doi: 10.1080/01691864.2015.1073121pmid: N/A

The development of 3D cameras and many navigation-supporting sensors has recently enabled robots to build their working maps and navigate accurately, making path planning popular not just on computer graphics, but in real environments as well. Pursuing the solutions for robot path planning, this paper presents a variant of searching method Theta* for choosing the best goal among given goals and the lowest-cost path to it, called Batch-Theta*. The novelty lies at the proposed line-of-sight checking function during the searching process and the manner that the method handles the batch of goals during one search instead of repeatedly considering a single goal or blindly doing the exhausted search. The analysis and simulations show that the proposed Batch-Theta* efficiently finds the lowest-cost path to the best goal in a given goal set under Theta*’s mechanism.

Vishwakarma, D.K.; Kapoor, Rajiv

doi: 10.1080/01691864.2015.1061701pmid: N/A

In this article, a simple yet proficient approach for the recognition of human action and Activity is presented. This method is based on the integration of translation and rotation of the human body. The proposed framework undergoes three major steps: (i) the shape of the human action/activity is represented through the computation of average energy images using edge spatial distribution of gradients along with the directional variation of the pixel values, (ii) the orientation-based rotational information of the human action is computed through -transform and (iii) a descriptor is developed by the fusion of translational features with rotational features. The fusion of features possesses the advantages exhibited by both local and global features of the silhouette and thus provides the discriminating feature representation for human activity recognition. The performance of descriptor is evaluated through a hybrid approach of support vector machine and the nearest neighbour classifiers on standard data set. The proposed method has shown superior results in terms of recognition accuracy in comparison with other state-of-the-art methods.

Shiomi, Masahiro; Hagita, Norihiro

doi: 10.1080/01691864.2015.1095651pmid: N/A

Person identification with accurate position information is essential for providing location-based services in real environments, such as a shopping mall. For this purpose, we propose a method that integrates 3D position information from environmental depth sensors and acceleration data from wearable devices to anonymously gather the trajectories of people who have wearable devices as well as others. Our proposed method identifies a person who has a wearable device by comparing two time series of acceleration data from device and position information. To do this, we extracted the behaviours of each axis using the changes of each bit of acceleration data at certain time periods. We evaluated our method with data collected at a shopping mall and a children’s playroom to investigate its effectiveness and robustness in different environments. Our evaluation results showed that it achieved an average identification of 85%, which is better than several alternative methods.