Cooperative Dynamic Motion Planning for Dual Manipulator Arms Based on RRT*Smart-AD Algorithm

Abstract

Intelligent manufacturing requires robots to adapt to increasingly complex tasks, and dual-arm cooperative operation can provide a more flexible and effective solution. Motion planning serves as a crucial foundation for dual-arm cooperative operation. The rapidly exploring random tree (RRT) algorithm based on random sampling has been widely used in high-dimensional manipulator path planning due to its probability completeness, handling of high-dimensional problems, scalability, and faster exploration speed compared with other planning methods. As a variant of RRT, the RRT*Smart algorithm introduces asymptotic optimality, improved sampling techniques, and better path optimization. However, existing research does not adequately address the cooperative motion planning requirements for dual manipulator arms in terms of sampling methods, path optimization, and dynamic adaptability. It also cannot handle dual-manipulator collaborative motion planning in dynamic scenarios. Therefore, in this paper, a novel motion planner named RRT*Smart-AD is proposed to ensure that the dual-arm robot satisfies obstacle avoidance constraints and dynamic characteristics in dynamic environments. This planner is capable of generating smooth motion trajectories that comply with differential constraints and physical collision constraints for a dual-arm robot. The proposed method includes several key components. First, a dynamic A* cost function sampling method, combined with an intelligent beacon sampling method, is introduced for sampling. A path-pruning strategy is employed to improve the computational efficiency. Strategies for dynamic region path repair and regrowth are also proposed to enhance adaptability in dynamic scenarios. Additionally, practical constraints such as maximum velocity, maximum acceleration, and collision constraints in robotic arm applications are analyzed. Particle swarm optimization (PSO) is utilized to optimize the motion trajectories by optimizing the parameters of quintic non-uniform rational B-splines (NURBSs). Static and dynamic simulation experiments verified that the RRT*Smart-AD algorithm for cooperative dynamic path planning of dual robotic arms outperformed biased RRT* and RRT*Smart. This method not only holds significant practical engineering significance for obstacle avoidance in dual-arm manipulators in intelligent factories but also provides a theoretical reference value for the path planning of other types of robots.

Recent advances in Rapidly-exploring random tree: A review

“…To enable the spacecraft to plan time-optimal attitude trajectories under complex pointing constraints, Xu et al [ 79 ] propose an intelligent branching model based on geometric-level spherical and quadrilateral interpolation, which is capable of planning suboptimal solutions within a few milliseconds. Long et al [ 80 ] propose RRT*-Smart based adaptive regional dynamics (RRT*Smart-AD) to deal with dynamic characteristics in dynamic environments. Some literature design branching modes depend on kinematic constraints, for example, Song et al [ 81 ] develop the kinodynamic RRT*-smart algorithm for collision avoidance strategy for unmanned surface vessels.…”

Collision Avoidance for a Selective Compliance Assembly Robot Arm Manipulator Using Topological Path Planning

“…The path is smooth. [28,29] APF This potential-based global model present a low probability of random paths, but the solution is highly dependent on the attractive and repulsive forces adjustments. The path is short and smooth.…”

“…By introducing the virtual force field of the APF into the search tree expansion stage of the IRRT* algorithm, the guidance of the algorithm increased, which greatly improved the convergence rate and search efficiency of the IRRT* algorithm. Paper [29] proposed a novel motion planner named RRT*Smart-AD to ensure that the dual-arm robot satisfies obstacle avoidance constraints and dynamic characteristics in dynamic environments.…”

Quad-Rotor Unmanned Aerial Vehicle Path Planning Based on the Target Bias Extension and Dynamic Step Size RRT* Algorithm

“…The adaptive target bias function method was used to make the random trees continuously approach the target point; then, a method combining heuristic cyclic sampling and the direction deviation strategy is designed to resample the nodes next to obstacles. In [35], the authors propose a path-planning algorithm named RRT*Smart-AD; it satisfies the two characteristics of obstacle avoidance constraints and dynamic characteristics in a dynamic environment. In [36], the authors propose an upgrade-based RRT* algorithm.…”