Improved RRT*-Connect Manipulator Path Planning in a Multi-Obstacle Narrow Environment

Improved RRT*-Connect Manipulator Path Planning in a Multi-Obstacle Narrow Environment

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This paper proposes an improved RRT*-Connect algorithm (IRRT*-Connect) for robotic arm sweet nothings acai bowl path planning in narrow environments with multiple obstacles.A heuristic sampling strategy is adopted with the integration of the ellipsoidal subset sampling and goal-biased sampling strategies, which can continuously compress the sampling space to enhance the sampling efficiency.During the node expansion process, an adaptive step-size method is introduced to dynamically adjust the step size based on the obstacle information, while a node rejection strategy is used to accelerate the search process so as to generate a near-optimal collision-free path.A pruning optimization strategy is also proposed to eliminate the redundant nodes from the 1994 toyota camry green path.

Furthermore, a cubic non-uniform B-spline interpolation algorithm is applied to smooth the generated path.Finally, simulation experiments of the IRRT*-Connect algorithm are conducted in Python and ROS, and physical experiments are performed on a UR5 robotic arm.By comparing with the existing algorithms, it is demonstrated that the proposed method can achieve shorter planning times and lower path costs of the manipulator operation.