Assistive technology: autonomous wheelchair in obstacle-ridden environment

Robotic dog for navigation of a rehabilitation wheelchair robot in a highly constrained environment

Adaptation to technological advancements and intelligent digital tools can enable healthcare providers to overcome the challenges of their patient-oriented care systems and processes. One such intelligent tool is automated assistive robots, which can improve patient care and safety in the health sector. This paper presents an invariant set of continuous nonlinear control laws for an assistive robot and a rehabilitation wheelchair robot modeled as a new autonomous robotic dog and rehabilitation wheelchair system for navigating a highly constrained environment. The control laws are derived from the Lyapunov-based control scheme classified under the umbrella of artificial potential field (APF) methods, and inherently proved stability of the new heterogeneous system. The robotic dog guides the wheelchair robot during the navigation process in a cluttered environment where the avoidances are from the robotic dog and the integrated dynamic protective polygon. The wheelchair traverses the obstacle-free path traced by the dynamic polygon. The leash is flexible, and its length is bounded, which invariably provides the protective polygon to change its intrinsic dimension. Thus, the dual-robot system has increased mobility for obstacle avoidance and passing through narrow passageways. The solution proffered herein is only feasible in a highly constrained and isolated human environment where nothing else appears to be moving in the direction of the robotic dog and wheelchair. The computer simulations and associated convergence graphs present the efficacy of the unique control laws for the new heterogeneous robotic system. Adoption of such control laws and their suitable variants can make a big impact in the healthcare industry.

Adaptive Network Model for Assisting People with Disabilities through Crowd Monitoring and Control

Here, we present an effective application of adaptive cooperative networks, namely assisting disables in navigating in a crowd in a pandemic or emergency situation. To achieve this, we model crowd movement and introduce a cooperative learning approach to enable cooperation and self-organization of the crowd members with impaired health or on wheelchairs to ensure their safe movement in the crowd. Here, it is assumed that the movement path and the varying locations of the other crowd members can be estimated by each agent. Therefore, the network nodes (agents) should continuously reorganize themselves by varying their speeds and distances from each other, from the surrounding walls, and from obstacles within a predefined limit. It is also demonstrated how the available wireless trackers such as AirTags can be used for this purpose. The model effectiveness is examined with respect to the real-time changes in environmental parameters and its efficacy is verified.

Linear manipulator: Motion control of an n-link robotic arm mounted on a mobile slider

Linear manipulators are versatile linear robotics systems that can be reprogrammed to accommodate product changes quickly and are flexible to meet unique requirements. Such robotic systems tend to have higher accuracy, making them the perfect automation solution for those mundane, repetitious tasks. With the demand for linear systems in real-life applications expanding consistently, this paper addresses motion planning and control (MPC) of a new modified unanchored linear manipulator consisting of an n-link robotic arm mounted on a mobile slider along a rail. Using the method of the Lyapunov-based Control Scheme (LbCS), new centralized acceleration-based controllers are designed for the navigation of the system to an unreachable target. Via the scheme, the unanchored manipulator can perform assigned tasks with enhanced reachability. The limitations and singularities of the linear manipulator are treated as artificial obstacles in this motion control scheme. The robotic arm manipulator utilized in this research can reposition its base link to a desired location in the workplace due to changes in work requirements. The effectiveness of the motion planner and the resulting acceleration-based control laws are validated numerically using the Runge-Kutta Method and illustrated via computer simulations. The controllers devised in this research can solve specific and targeted motion control problems of smart cities' modern mechanical systems. The unanchored linear manipulator could be used in various disciplines where pick-and-place, assembly, material handling, and surgical procedures are required.

Switch controllers of an n-link revolute manipulator with a prismatic end-effector for landmark navigation

Robotic arms play an indispensable role in multiple sectors such as manufacturing, transportation and healthcare to improve human livelihoods and make possible their endeavors and innovations, which further enhance the quality of our lives. This paper considers such a robotic arm comprised of n revolute links and a prismatic end-effector, where the articulated arm is anchored in a restricted workspace. A new set of stabilizing switched velocity-based continuous controllers was derived using the Lyapunov-based Control Scheme (LbCS) from the category of classical approaches where switching of these nonlinear controllers is invoked by a new rule. The switched controllers enable the end-effector of the robotic arm to navigate autonomously via a series of landmarks, known as hierarchal landmarks, and finally converge to its equilibrium state. The interaction of the inherent attributes of LbCS that are the safeness, shortness and smoothness of paths for motion planning bring about cost and time efficiency of the controllers. The stability of the switched system was proven using Branicky's stability criteria for switched systems based on multiple Lyapunov functions and was numerically validated using the RK4 method (Runge-Kutta method). Finally, computer simulation results are presented to show the effectiveness of the continuous time-invariant velocity-based controllers.