Simulating development in a real robot: on the concurrent increase of sensory, motor, and neural complexity

We present a quantitative investigation on the effects of a discrete developmental progression on the acquisition of a foveation behavior by a robotic hand-arm-eyes system. Development is simulated by (a) increasing the resolution of visual and tactile systems, (b) freezing and freeing mechanical degrees of freedom, and (c) adding neuronal units to the neural control architecture. Our experimental results show that a system starting with a low-resolution sensory system, a low precision motor system, and a low complexity neural structure, learns faster that a system which is more complex at the beginning

Autonomous Movement Control of Coaxial Mobile Robot based on Aspect Ratio of Human Face for Public Relation Activity Using Stereo Thermal Camera

In recent years, robots that recognize people around them and provide guidance, information, and monitoring have been attracting attention. The mainstream of conventional human recognition technology is the method using a camera or laser range finder. However, it is difficult to recognize with a camera due to fluctuations in lighting 1), and it is often affected by the recognition environment such as misrecognition 2) with a person's leg and a chair's leg with a laser range finder. Therefore, we propose a human recognition method using a thermal camera that can visualize human heat. This study aims to realize human-following autonomous movement based on human recognition. In addition, the distance from the robot to the person is measured with a stereo thermal camera that uses two thermal cameras. A coaxial two-wheeled robot that is compact and capable of super-credit turning is used as a mobile robot. Finally, we conduct an autonomous movement experiment of a coaxial mobile robot based on human recognition by combining these. We performed human-following experiments on a coaxial two-wheeled robot based on human recognition using a stereo thermal camera and confirmed that it moves appropriately to the location where the recognized person is in multiple use cases (scenarios). However, the accuracy of distance measurement by stereo vision is inferior to that of laser measurement. It is necessary to improve it in the case of movement that requires more accuracy

A Fully Implantable Wireless ECoG 128-Channel Recording Device for Human Brain–Machine Interfaces: W-HERBS

Brain–machine interfaces (BMIs) are promising devices that can be used as neuroprostheses by severely disabled individuals. Brain surface electroencephalograms (electrocorticograms, ECoGs) can provide input signals that can then be decoded to enable communication with others and to control intelligent prostheses and home electronics. However, conventional systems use wired ECoG recordings. Therefore, the development of wireless systems for clinical ECoG BMIs is a major goal in the field. We developed a fully implantable ECoG signal recording device for human ECoG BMI, i.e., a wireless human ECoG-based real-time BMI system (W-HERBS). In this system, three-dimensional (3D) high-density subdural multiple electrodes are fitted to the brain surface and ECoG measurement units record 128-channel (ch) ECoG signals at a sampling rate of 1 kHz. The units transfer data to the data and power management unit implanted subcutaneously in the abdomen through a subcutaneous stretchable spiral cable. The data and power management unit then communicates with a workstation outside the body and wirelessly receives 400 mW of power from an external wireless transmitter. The workstation records and analyzes the received data in the frequency domain and controls external devices based on analyses. We investigated the performance of the proposed system. We were able to use W-HERBS to detect sine waves with a 4.8-μV amplitude and a 60–200-Hz bandwidth from the ECoG BMIs. W-HERBS is the first fully implantable ECoG-based BMI system with more than 100 ch. It is capable of recording 128-ch subdural ECoG signals with sufficient input-referred noise (3 μVrms) and with an acceptable time delay (250 ms). The system contributes to the clinical application of high-performance BMIs and to experimental brain research

Enhanced vibration control of a multilink flexible manipulator using filtered inverse controller

Abstract Flexible manipulators have numerous advantages such as lightweight, high operation speed, and low power consumption. However, they suffer from link vibrations, especially when operated at high speeds followed by sudden stops. This limitation has been addressed using techniques such as adaptive filters, adaptive strain feedback gain, state feedback control, etc. This article presents a filtered inverse controller for the mitigation of link vibrations in a multi-link flexible manipulator. To this end, the plant model, developed and linearized in Maple/Maplesim was inverted in MATLAB. The internal dynamics of the inverse model were stabilized using the state feedback technique. For safe and high-speed operations, the inverse model was augmented with a low pass filter to form the filtered inverse which was used as feedforward controller. Practical experiments were carried out in the dSPACE environment. Results show that filtered inverse controller yield not only faster response but relatively minimal link vibration when compared with the manipulator without vibration controller

Effect of Instructions on Parts' Positions during an Assembly Task on Efficiency and Workload

This paper examines the effect of instructions on parts' positions during an assembly task having numerous parts for someone to remember where they are located. Hypothesizing that instructions relating to the positions of the parts would enhance work efficiency and reduce workload, we designed an experimental method to test this theory. In this experiment using educational blocks, visual instructions were given by illuminating the space where the parts are kept, while auditory instructions were provided to aid locating the parts to compare the efficiency of visual and auditory instructions. Experiments with six participants showed that the visual instruction significantly shortened not only the search time but also the assembly time (which was one way in which efficiency was assessed in this study). The use of parts' positions instructions tended to reduce the workload as evaluated with NASA-TLX compared with the cases without instructions

Trend analysis and fatality causes in Kenyan roads: A review of road traffic accident data between 2015 and 2020

With increasing population and motorization, Kenya as well as other African countries are faced with a tragic road traffic accidents (RTA). This paper looks at 5-year (2015–2020) data downloaded from National Transport and Safety Authority (NTSA) website, to identify trends and review progress of the traffic accidents in the country. The objective is to assess the prevalence of accidents within affected groups and location to identify trends and generalized causative agency from the reported data. From literature review, research activity focused on RTA in the country is minimal compared to the social significance accidents poses. The data were extracted and classified using Latent Dirichlet Allocation, a machine learning algorithm modelled in Matlab to group reported accident briefs into general categories/topic which are closely related. Four categories were identified as leading causes of fatality in the country: Knocking down victims, hit-and-run, losing control and head on collision. The identified causes point to preventable driver’s errors which agrees with other researchers. From trend analysis, fatalities and injuries have increased by 26% and 46.5%, respectively since January 2015 to January 2020. This paper found that injuries in vulnerable road users: pedestrians, pillion passengers and motorcyclist, has seen a foldfold increment compared to 2015 data. From the discussion, urgent fine-tuning of policing to protect vulnerable road user as well as curb the overly decried driver behavior is needed. The paper recommends fine-tuning of data collection, capturing details of accident that will be useful in modeling and data analysis for future planning

Locomoting with less computation but more morphology

Abstract – Biped walking is one of the most graceful movements observed in humans. Today’s humanoid robots, despite their undeniably impressive performance, are still a long way from the elegance and grace found in Nature. To narrow the gap between natural and artificial systems, we propose to rely more on morphology, intrinsic dynamics, and less on raw computation. This paper documents a series of simulated and real “pseudo-passive ” dynamic biped walkers in which computation is traded off for good morphology, that is, adequate mechanical design and appropriate material properties These two factors are parameterized, and the resulting solution space is explored in simulation. Interesting solutions are then realized in the real world. Our experiments show that successful pseudo-passive walkers with a good morphology locomote by converting oscillatory energy into forward movement. Index Terms – biped locomotion, new artificial intelligence, morphology, materials, mechanical design I