Detecting thermal faults is critical to the safety of lithium-ion batteries. This paper, therefore, proposes a neural network-based approach. The approach relies on the long short-term memory (LSTM) neural network, in conjunction with an alteration to the walk-forward technique, to accurately estimate the surface temperature of the cell. It also relies on a residual monitor to detect the faults in real-time. This data-driven method is introduced to expand the available options in thermal fault detection. It offers an easy-to-implement option that does not require expert understanding in battery physics, complex mathematical modeling, and tedious parameter tuning processes. The experimental results demonstrate that this approach can detect thermal faults accurately. It is adaptive to different battery chemistries and form factors, and thanks to its online training capability, it can also automatically retrain itself to capture changes in the battery over time.

This paper presents a motion planning algorithm for generating optimal collision-free paths for robotic vehicle with two trailers moving autonomously. The proposed algorithm is based on combination between artificial potential field method (APF) and optimal control theory. The optimal control theory is applied to generate an optimal collision-free path for robotic vehicle from a starting point to the goal point. On the other hand, the proposed APF is based on two-dimensional Gaussian function to represent goal location as attractor and obstacles as repulsors and consequently, will control the steering angle of the robotic vehicle so that it can reach to its target location safely avoiding collision. A linear two-degree-of-freedom vehicle model with linear tire characteristics is derived to represent the vehicle motion considering the lateral and yaw dynamics. Several simulations are carried out to check the fidelity of the proposed technique and the illustrated results demonstrated the generated path for the robotic vehicle with two trailers satisfy vehicle dynamics constraints, avoid collision with the obstacles and reach the target location safely. The simulations results demonstrated the efficiency of the proposed algorithm and its success in dealing with complex environments with different obstacles.

Feedback in robotic systems is imperative to accomplishing the required task. Speed controllers, visual feedback and other sensors that offer a closed-loop control system dramatically increases the effectiveness of the system. Having a sense of touch greatly increases the ability to pick-up and manipulate objects in robotic manipulation systems. Currently, precise manipulation tasks in various industries still rely on human operators, but a sense of touch on robot hands can improve the systems capabilities to perform like a human. Measuring force, contact location and detecting slip are all abilities to be desired from a tactile sensor. However, current sensors are complex to manufacture as they require different technologies to accomplish different readings. This research presents a pneumatic tactile sensor that is easy to produce and offers multiple abilities such as force feedback and slip detection using machine learning processes. The sensor showed consistent relations between its pressure readings and applied force. Slip detection was also found to be 80% confident using a decision tree classifier. The implementation of machine learning provides the improvement on other sensors to categorize data from more realistic scenarios. The results show the potential to have a one-dimensional sensor perform multiple tasks by using advanced analysis techniques.

Mobile robots that integrate visual servo control for facilitating autonomous grasping nd manipulation are the focus of this paper. In view of mobility, they have wider pplication than traditional fixed-based robots with visual servoing. Visual servoing s widely used in mobile robot navigation. However, there are not so many report or applying it to mobile manipulation. In this paper, challenges and limitations of pplying visual servoing in mobile manipulation are discussed. Next, two classical pproaches (image-based visual servoing (IBVS) and position-based visual servoing (PBVS)) are introduced aloing with their advantages and disadvantages. Simulations n Matlab are carried out using the two methods, there advantages and drawbacks are llustrated and discussed. On this basis, a suggested system in mobile manipulation s proposed including an IBVS with an eye-in-hand camera configuration system. imulations and experimentations are carried with this robot configuration in a earch and rescue scenario, which show good performance.