A novel non-collision trajectory planning algorithm based on velocity potential field for robotic manipulator

Abstract

This article presents a non-collision trajectory planning algorithm in three-dimensional space based on velocity potential field for robotic manipulators, which can be applied to collision avoidance among serial industrial robots and obstacles, and path optimization in multi-robot collaborative operation. The algorithm is achieved by planning joint velocities of manipulators based on attractive, repulsive, and tangential velocity of velocity potential field. To avoid oscillating at goal point, a saturated function is suggested to the attractive velocity potential field that slows down to the goal progressively. In repulsive velocity potential field, a spring damping system is designed to eliminate the chattering phenomenon near obstacles. Moreover, a fuzzy logic approach is used to optimize the spring damping coefficients for different velocities of manipulators. Different from the usual tangential velocity perpendicular to the repulsive velocity vector for avoiding the local minima problem, an innovative tangential velocity potential field is introduced that is considering the relative position and moving direction of obstacles for minimum avoidance path in three-dimensional space. In addition, a path priority strategy of collision avoidance is taken into account for better performance and higher efficiency when multi-robots cooperation is scheduled. The improvements for local minima and oscillation are verified by simulations in MATLAB. The adaptabilities of the algorithm in different velocities and priority strategies are demonstrated by simulations of two ABB robots in Robot Studio. The method is further implemented in an experimental platform with a SCARA and an ABB robot cooperation around a stationary obstacle and a moving object, and the result shows real time and effectiveness of the algorithms.

Research on motion planning for an indoor spray arm based on an improved potential field method

“…wherê L þ s --pseudo-inverse of estimation of image Jacobian matrix L s . The visual system block diagram is shown in Fig 5. According to reference [16], the controller can make the system robust to model error and noise disturbance.…”

“…The common methods in the trajectory planning include genetic algorithm [9][10], simulated annealing [11][12], artificial neural network [13][14], A � algorithm [15], vector field method [16], adaptive algorithm [17][18], particle swarm optimization algorithm [19][20], artificial potential field method [21][22], etc. Among these algorithms, the artificial potential field method has a simple structure, is convenient for real-time control on hardware entities, and can usually plan smoother and safer paths.…”

Artificial Intelligence Control in 4D Cylindrical Space for Industrial Robotic Applications

“…These are just a few examples, out of hundreds. A subset of these and other algorithms are more specifically tied to industrial robots, such as the already named double A* [8], non-probabilistic anytime algorithm [25], potential fields [26,27], probabilistic roadmaps [18,19] and RRT [17]. Kallman et al [28] proposed the dynamic roadmaps with bidirectional RRT to deal with changing environments.…”

A Collision Avoidance Strategy for Redundant Manipulators in Dynamically Variable Environments: On-Line Perturbations of Off-Line Generated Trajectories

“…Furthermore, a model predictive control is presented in [6] with a BIT (Batch Informed Trees) method that improves the path point connections, whereas in [7], collision-free paths are generated using an RRT (Rapidly-Exploring Random Trees) algorithm. Moreover, a very common approach to path planning is to define artificial potential fields, which drive the robot to the target inside the workspace [8][9][10][11]. The result of the potential fields is a set of forces that are attractive toward the goal and repulsive from the obstacle regions.…”

“…Typically, such forces are associated with velocities applied to the end-effector of the manipulator; then, the trajectory can be obtained by numerical integration. An example of a path planning strategy based on potential fields method is proposed in [9], where a saturation function for the attractive velocity is introduced in order to avoid oscillations around the goal point; furthermore, for the repulsive velocity, a spring-damper system is used to eliminate noise in proximity of the obstacles. A further problem in optimal path planning for automation and robotics is the generation of smooth trajectories, as highlighted in [3,12]: an optimal algorithm for trajectory generation must guarantee smoothness in terms of position and velocity in order to be implemented in the controller of a real system.…”