Adaptive Meta-heuristic Framework for Real-time Dynamic Obstacle Avoidance in Redundant Robot Manipulators

摘要

Robotic manipulator faces a challenge in navigating dynamic environments while ensuring collision-free trajectories, especially for redundant manipulators. Inverse kinematics involves finding joint angles to reach a specific point in 3D space. The shift from classical analytical and numerical methods to optimization heuristic algorithms is driven by the increasing complexity of robotic systems and the demand for more versatile and adaptive solutions. Meta-heuristic algorithms offer a transformative approach by framing the inverse kinematics problem as an optimization challenge, providing a flexible and robust means to navigate complex solution spaces. Metaheuristic algorithms, known for their ability to explore high-dimensional search spaces and avoid local optima, offer robust solutions for these challenges. They enhance computational efficiency, enabling real-time decision-making and obstacle avoidance, making them ideal for complex robotic applications. These characteristics of the metaheuristic algorithms can used in developing an integrated framework that offers complete solution to robot manipulators. This research article presents a generalized framework leveraging meta-heuristic algorithms to address dynamic obstacle avoidance in redundant manipulators. The framework uses meta-heuristic algorithms as the inverse kinematics solver, 3D trajectory planner, and obstacle avoidance mechanism, encompassing both static and dynamic obstacles. The proposed framework is generalized and gives the user to select the type of robot manipulator, with any number of links with any custom trajectory within the workspace of the robot manipulator. Also, any metaheuristic algorithm can be used in the proposed framework. The proposed framework is implemented in MATLAB’s app designer for simulation with six different meta-heuristic algorithms. The effectiveness of the framework was evaluated in terms of its capability to generate 3d path, its ability to follow generated trajectory, while seamlessly adapting to dynamically changing environments. Through simulation, the framework showcased robust performance in navigating workspaces with moving obstacles, ensuring collision-free motion for redundant manipulators.