Abstract Due to uncontrolled bleeding in victims and frequently delayed emergency response times to road accidents had frequently causes severe casualties. To solve this problem, the robotic arm used in this paper, based on machine learning, is programmed to monitor and react to bleeding in accident victims. A clotting agent is applied to the wounds by the robotic arm at the touch of a button, stopping the bleeding and possibly saving lives. The system's image processing and wound classification were done in a Python-based Jupyter environment. SolidWorks was used to design the robotic arm, which was programmed to respond to input from a Python model. The robotic arm's kinematics was tested in the Simulink environment, displaying the joints' successful operation. The robotic arm design was further analyzed in the Simcape environment, where the arm's trajectory was evaluated. The results showed that the robotic arm could effectively move toward the detected wound, following the training trajectory. Object tracking was successfully performed in Matlab under the Simcape environment, as evidenced by the alignment between the expected and actual trajectory line graphs. The study concludes that the machine learning-based robotic arm can accurately move to any desired position within its workspace, enabling the precise application of the clotting agent on the wound. This system holds significant potential for improving emergency response times and outcomes in road accidents.

Robotic manipulatorforward Kinematics involves the assurance of end-effector arrangements from connecting joint boundaries. The traditional mathematical calculation of controller forward -Kinematics is monotonous and tedious. Accordingly, it is important to execute a strategy that precisely performs forward energy while wiping out the disadvantages of the mathematical calculation technique. Versatile Neuro-Fuzzy Inference System (ANFIS) is a computational knowledge strategy that has been effectively executed for expectation purposes in assorted logical orders. This present examination's essential goal was to evaluate the productivity of ANFIS in foreseeing 3-levels of opportunity automated controller Cartesian directions from connecting joint boundaries. A speculative 3-level of opportunity automated controller has been considered in this investigation. Model preparing information has been obtained by mathematical forward kinematics calculation of the controller's end effector arrangements. Nine datasets have been utilized for model preparing, while five datasets have been utilized for model testing or approval. The ANFIS model's precision has been surveyed by figuring the Mean outright Percentage Error (MAPE) between the real and anticipated end-effector Cartesian directions. Because of Mean Absolute Percentage Error (MAPE), the created ANFIS model has forecast correctness’s of 63.35% and 80.07% in foreseeing x-directions and y-organizes, separately. Accordingly, ANFIS can be dependably executed as a commendable substitute for the customary arithmetical calculation method in anticipating controller Cartesian directions. It is suggested that the precision of other computational knowledge methods like Particle Swarm Optimization (PSO) and Support Vector Machines (SVM) be evaluated.

Vehicle accidents are on the rise in the roads due to over speeding, poor roads, and misjudgment during driving, not forgetting overloading and lack of proper vehicle maintenance. Private transport has increased rapidly, thereby resulting in many accidents on the roads. Whenever there is an accident, it has been realized that some accident victims who would have survived the accident end up dead due to continuous bleeding. As a result, many lives are lost because of a lack of emergency measures to avoid that constant loss of blood through external bleeding. In this regard, there is a need to design a robotic system based on machine learning (ML) to monitor and control the external bleeding from vehicle accident victims in the shortest possible time through the utilization of software such as SolidWorks, Matlab, and proteus. The nanotechnology-based system interfaced with the robotic end-effector shall be used to apply to stop gel through the utilization of Comsol software. The robotic system shall be integrated with a monitoring system for precise and useful quantification of the bleeding wound, thus the importance of machine learning to achieve accurate information. Keyword : Robotic end effector, Machine Learning, nanotechnology, monitoring of external bleeding.