We propose a new detection method to predict a vehicle's trajectory and use it for detecting lane changes of surrounding vehicles. According to the previous research, more than 90% of the car crashes are caused by human errors, and lane changes are the main factor. Therefore, if a lane change can be detected before a vehicle crosses the centerline, accident rates will decrease. Previously reported detection methods have the problem of frequent false alarms caused by zigzag driving that can result in user distrust in driving safety support systems. Most cases of zigzag driving are caused by the abortion of a lane change due to the presence of adjacent vehicles on the next lane. Our approach reduces false alarms by considering the possibility of a crash with adjacent vehicles by applying trajectory prediction when the target vehicle attempts to change a lane, and it reflects the result of lane-change detection. We used a traffic dataset with more than 500 lane changes and confirmed that the proposed method can considerably improve the detection performance.

Path planning field for autonomous mobile robot is an optimization problem that involves computing a collision-free path between initial location and goal location. In this paper, we present an improved artificial potential field based regression search (Improved APF-based RS) method which can obtain a global sub-optimal/optimal path efficiently without local minima and oscillations in complete known environment information. We redefine potential functions to eliminate non-reachable and local minima problems, and utilize virtual local target for robot to escape oscillations. Due to the planned path by improved APF is not the shortest/approximate shortest trajectory, we develop a regression search (RS) method to optimize the planned path. The optimization path is calculated by connecting the sequential points which produced by improved APF. Amount of simulations demonstrate that the improved APF method very easily escape from local minima and oscillatory movements. Moreover, the simulation results confirm that our proposed path planning approach could always calculate a more global optimal/near-optimal, collision-free and safety path to its destination compare with general APF. That proves our improved APF-based RS method very feasibility and efficiency to solve path planning which is a NP-hard problem for autonomous mobile robot.

Sit-to-stand (STS) motion is an important daily activity, and many post-stroke patients have difficulty performing STS motion. Previous studies found that there are four muscle synergies (synchronized muscle activations) in the STS motion of healthy adults. However, for post-stroke patients, it is unclear whether muscle synergies change and which features primarily reflect motor impairment. Here, we use a machine learning method to demonstrate that temporal features in two muscle synergies that contribute to hip rising and balance maintenance motion reflect the motor impairment of post-stroke patients. Analyzing the muscle synergies of age-matched healthy elderly people (n=12) and post-stroke patients (n=33), we found that the same four muscle synergies could account for the muscle activity of post-stroke patients. Also, we were able to distinguish post-stroke patients from healthy people on the basis of the temporal features of these muscle synergies. Furthermore, these temporal features were found to correlate with motor impairment of post-stroke patients. We conclude that post-stroke patients can still utilize the same number of muscle synergies as healthy people, but the temporal structure of muscle synergies changes as a result of motor impairment. This could lead to a new rehabilitation strategy for post-stroke patients that focuses on activation timing of muscle synergies.

In order for service robots to safely coexist with humans, collision avoidance with humans is the most important issue. On the other hand, working efficiencies are also important and cannot be ignored. In this paper, we propose a method to estimate a pedestrian's behavior. Based on the estimation, we realize smooth collision avoidances between a robot and a human. A robot detects pedestrians by using a laser range finder and tracks them by a Kalman filter. We apply the social force model to the observed trajectory for a determination whether the pedestrian intends to avoid a collision with the robot or not. The robot selects an appropriate behavior based on the estimation results. We conducted experiments that a robot and a person pass each other. Through the experiments, the usefulness of the proposed method was demonstrated.

In this article, a novel dense underwater 3-D mapping paradigm based on pose graph simultaneous localization and mapping (SLAM) using an acoustic camera mounted on a rotator is proposed. The demands of underwater tasks, such as unmanned construction using robots, are growing rapidly. In recent years, the acoustic camera, which is a state-of-the-art forward-looking imaging sonar, has been gradually applied in underwater exploration. However, distinctive imaging principles make it difficult to gain an intuitive perception of an underwater environment. In this study, an acoustic camera with a rotator was used for dense 3-D mapping of the underwater environment. The proposed method first applies a 3-D occupancy mapping framework based on the acoustic camera rotating around the acoustic axis using a rotator at a stationary position to generate 3-D local maps. Then, scan matching of adjacent local maps is implemented to calculate odometry without involving internal sensors, and an approximate dense global map is built in real time. Finally, based on a graph optimization scheme, offline refinement is performed to generate a final dense global map. Our experimental results demonstrate that our 3-D mapping framework for an acoustic camera can achieve dense 3-D mapping of underwater environments robustly and accurately.

Graphical Abstract Abstract Standing-up motion is an important daily activity. It has been known that humans can employ different strategies to stand up from a chair, but it was not clear how people control their redundant muscles to achieve different strategies. This study employs the concept of muscle synergy which suggests that humans utilize the small number of modules (called muscle synergy) to generate the movements. This study uses two approaches to understand how humans generate different movements. Firstly, measurement experiment was performed to investigate the muscle synergy structure during different strategies. Next, the finding from the measurement experiment is validated through the forward dynamic simulation using our developed neuromusculoskeletal model. Both of our results from simulation study and measurement experiment showed that four muscle synergies could generate standing-up motion. However, they adaptively changed the start time of a certain synergy to achieve different strategies.