Please note this is an old version of this entry, which may differ significantly from the current revision.
Path planning is an essential component of robot motion planning and is a research hotspot in the field of robotics and other related intelligent fields [1
]. Among them, the manipulator, as an important industrial robot, has autonomy and intelligence levels that are crucial for improving production efficiency and quality. Path planning can help the manipulator automatically plan the optimal path, reduce dependence on staff, and improve the autonomy of the manipulator. Path planning can be flexibly adjusted and optimized according to different task characteristics, thereby improving the motion accuracy and speed of the manipulator, which directly affects production efficiency and quality. Compared with a single manipulator, the form of a dual manipulator collaborative operation can meet the needs of complex, intelligent, and compliant modern industrial systems, and dual manipulators have more advantages in efficiency and performance [2
] and are gradually gaining attention in the industry. Path planning for dual manipulators is an important work of collaborative operations [4
]. To enhance the adaptability and flexibility of dual-manipulator systems, it is necessary to flexibly adjust and optimize paths based on various production environments and task requirements. The equipment transformation and upgrading of production lines are of great significance, but there are high requirements for efficiency, real-time performance, and safety in collaborative operations [5
]. To address these challenges, researchers are continuously developing and improving various path planning methods for dual manipulators to enhance their efficiency and precision and meet different demands in industrial production environments. Therefore, different path planning algorithms need to be designed or selected for various fields to achieve the goal of fast matching and application.
2. Rapidly Exploring Random Tree for a Dual Manipulator
The classic 3D path planning algorithms for robots can be roughly divided into three categories. The first type of path planning algorithm is based on searches, such as Dijkstra and A* algorithms [6
]. This algorithm is based on a graph structure in which each node represents the robot’s location, and each edge represents its movement path. By searching through the graph structure and calculating a heuristic function for each node to evaluate the distance to the endpoint, the optimal path is found. Qing et al. [8
] proposed an improved Dijkstra algorithm that saves all equidistant shortest paths during the path search process, although it can solve several shortest path planning problems; in some cases, it may be difficult to obtain the complete graph structure, and there are issues such as large search space, high computational complexity, and poor real-time performance.
The second type of path planning algorithm is based on rules, such as the artificial potential field method [9
]. The main idea is to design an artificial potential field to simulate the perception and decision-making process of the robot during movement and achieve path planning. The artificial potential field method has the advantages of simple algorithm implementation, easy understanding and use, rapid calculation of robot movement paths, and high real-time performance. Therefore, Xia et al. [11
] proposed an improved velocity potential field (IVPF) algorithm based on the artificial potential field method to address the inherent drawbacks of traditional algorithms. However, utilizing tangential velocity to avoid local minimum problems leads to poor path quality. The artificial potential field method only considers the relationship between the robot and obstacles, ignoring the constraints among robots themselves, which may lead to locally optimal solutions in some cases.
To address this issue, the third type of path planning algorithm based on sampling is widely applied in various fields, such as the rapidly exploring random tree (RRT) [12
] and probabilistic roadmap (PRM) [13
]. The main idea is to search for the optimal feasible path through random sampling in the environment. Sampling-based algorithms are not limited by the type of environment and can be applied to path planning problems in various complex environments, with high robustness and reliability. Li et al. [14
] improved the PRM algorithm by using a pseudorandom sampling strategy with the spatial principal axis as a reference axis and optimized the path using Bezier curves. However, the roadmap construction rate is unstable in three-dimensional environments. Liu et al. [15
] proposed a grid-local PRM method, which has high efficiency and real-time performance. However, this type of algorithm has weak scalability and a low roadmap reuse rate. To address this issue, the RRT algorithm and its variations have been proposed. The RRT algorithm has wide applicability, high efficiency, strong scalability, good determinism, and real-time computation, which effectively solves the path planning problem with high-dimensional space constraints. As a result, the RRT algorithm has become one of the most commonly used and effective algorithms in path planning.
This entry is adapted from the peer-reviewed paper 10.3390/s23115172