Adaptive Monte Carlo localization (AMCL) algorithm has a limited pose accuracy because of the nonconvexity of the laser sensor model, the complex and unstructured features of the working environment, the randomness of particle sampling, and the final pose selection problem. In this paper, an improved AMCL algorithm is proposed, aiming to build a laser radar-based robot localization system in a complex and unstructured environment, with a LIDAR point cloud scan-matching process after the particle score calculating process. The weighted mean pose of AMCL particle swarm is used as the initial pose of the scan matching process. The LIDAR point cloud is matched with the probability grid map from coarse to fine using the Gaussian-Newton method, which results in more accurate poses. Moreover, the scan-matching pose is added into the particle swarm as a high-weight particle. So the particle swarm after resampling will be more concentrated in the correct position. The particle filter and the scan-matching process form a closed loop, thus enhancing the localization accuracy of mobile robots. The experiment results demonstrate that the proposed improved AMCL algorithm is superior to the traditional AMCL algorithm in the complex and unstructured environment, by exploiting the high-accuracy characteristic of scan matching while inheriting the stability of AMCL.
This paper presents a new way to teach a robot certain motions remotely from human demonstrator. The human and robot interface is built using a Kinect sensor which is connected directly to a remote computer that runs on processing software. The Cartesian coordinates is extracted, converted into joint angles and sent to the workstation for the control of the Sawyer robot. Kinesthetic teaching was used to correct the reproduced demonstrations while only valid resolved joint angles are recorded to ensure consistence in the sent data. The recorded dataset is encoded using GMM while GMR was employed to extract and reproduce generalised trajectory with respect to the associated time-step. To evaluate the proposed approach, an experiment for a robot to follow a human arm motion was performed. This proposed approach could help non-expert users to teach a robot how to perform assembling task in more human like ways.
Simplifying the interaction between humans and computers has become intensively important. Hand gesture contains large amount information that can facilitate the communication among humans, and it can also be utilized to interact with external devices. As a result, this study aims to decode the different hand gestures from sEMG signal. The thumb plays the most important role in hand-based object manipulation, such as touch screen control for smart phones, for which many thumb-based hand involved. Therefore, studying the relationship between EMG signals and the thumb movement has certain value for the future human-computer interaction. In this paper, we focus on the identification of electrode position. The signal from which is not so related to the thumb movement, and thus these sEMG channels can be reduced. In the experiment, a 16-channels sleeve is utilized and a variance-based method was proposed to identify the redundant channels. It is found that there exist three common redundant channels across nine subjects., and all located at the inside of the forearm.
Adaptive behavioural assessments are useful in the diagnosis of autism. This research proposes a strategy of assessing autistic children's adaption skills through the change of hand behaviour complexity based on deep learning and complex systems. Specifically, we implement a sparse representation of high-dimensional features of hand movements utilize convolutional neural network (CNN) and Bag of Word model (BoW) and explain in detail how two quantify measurements (complexity and diversity) reflect adaption behavioural capacity. This paper introduces our ongoing projects and demonstrates the preliminary experimental setups and motion protocol design. Future work includes improving the interaction scenarios, establishing a data set, and enhancing the interpretability of the results of the adaption behaviour skill measurements.
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