A Novel Sampling-Based Optimal Motion Planning Algorithm for Energy-Efficient Robotic Pick and Place

Abstract

Energy usage in robotic applications is rapidly increasing as industrial robot installations grow. This research introduces a novel approach, using the rapidly exploring random tree (RRT)-based scheme for optimizing the robot’s motion planning and minimizing energy consumption. Sampling-based algorithms for path planning, such as RRT and its many other variants, are widely used in robotic motion planning due to their efficiency in solving complex high-dimensional problems efficiently. However, standard versions of these algorithms cannot guarantee that the generated trajectories are always optimum and mostly ignore the energy consumption in robotic applications. This paper proposes an energy-efficient industrial robotics motion planning approach using the novel flight cost-based RRT (FC-RRT*) algorithm in pick-and-place operation to generate nodes in a predetermined direction and then calculate energy consumption using the circle point method. After optimizing the motion trajectory, power consumption is computed for the rotary axes of a six degree of freedom (6DOF) serial type of industrial robot using the work–energy hypothesis for the rotational motion of a rigid body. The results are compared to the traditional RRT and RRT* (RRT-star) algorithm as well as the kinematic solutions. The experimental results of axis indexing tests indicate that by employing the sampling-based FC-RRT* algorithm, the robot joints consume less energy (1.6% to 16.5% less) compared to both the kinematic solution and the conventional RRT* algorithm.

Recent advances in Rapidly-exploring random tree: A review

“…To minimize the energy consumption of the robot, Yu et al [ 23 ] propose a cylinder-based informed-RRT* (Cyl-iRRT*) algorithm, which seeks to find the optimal homotopy path by focusing the search space on the designed gradually shrinking cylinder. Alam et al [ 24 ] present a pick-and-place RRT* under the novel flight cost (FC-RRT*), which generates nodes in a predetermined direction and then calculates the energy consumption using the circle-point method. Modular self-reconfiguring robots can change their configurations to efficiently adapt to various tasks.…”

“…In Ref. [ 24 ], an energy-efficient industrial robot motion planning method is proposed and the energy consumption is calculated using the circle point method.…”

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

“…The research [26] proposed a novel approach that used the rapidly exploring random tree (RRT)-based scheme for optimizing the robot's motion planning and minimizing energy consumption. Sampling-based algorithms for path planning, such as RRT and many other variants, were widely used in robotic motion planning due to their efficiency in solving complex high-dimensional problems.…”

A Fusion Approach for UAV Onboard Flight Trajectory Management and Decision Making Based on the Combination of Enhanced A* Algorithm and Quadratic Programming

“…One is flight path planning performing at a global level. Some conventional and intelligent algorithms are used for global optimal path seeking according to selected criteria (e.g., energy consumption, flight time, or operational costs minimization) and the imposed UAV dynamic and kinematic characteristics constraints [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. In this case, the main focus is economical path seeking.…”

“…Gammell et al [4] used an informed strategy to enhance the optimality of the RRT algorithm. Alam et al [5] proposed an energy-efficient motion planning approach using a flight cost-based RRT (FC-RRT*) algorithm to optimize the trajectory flight cost compared with the conventional RRT and RRT* algorithms as well as the kinematic solutions. Chen et al [6] established a double-tree to enhance the efficiency of the RRT algorithm.…”