Optimal Trajectory Planning of a Mobile Manipulator for Disinfection Using Multi-objective Genetic Algorithm

AbstractIn this paper, a methodology for path distance and time synthetic optimal trajectory planning is described in order to improve the work efficiency of a robotic chainsaw when dealing with cutting complex timber joints. To demonstrate this approach one specific complicated timber joint is used as an example. The trajectory is interpolated in the joint space by using a quantic polynomial function which enables the trajectory to be constrained in the kinematic limits of velocity, acceleration, and jerk. The particle swarm optimization (PSO) is applied to optimize the path of all cutting surfaces of the timber joint in operating space to achieve the shortest path. Based on the optimal path, an adaptive genetic algorithm (AGA) is used to optimize the time interval of interpolation points of every joint to realize the time-optimal trajectory. The results of the simulation show that the PSO method shortens the distance of the trajectory and that the AGA algorithm reduces time intervals and helps to obtain smooth trajectories, validating the effectiveness and practicability of the two proposed methodology on path and time optimization for 6-DOF robots when used in cutting tasks.

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Digging Trajectory Optimization for Cable Shovel Robotic Excavation Based on a Multi-Objective Genetic Algorithm

Energies ◽

10.3390/en13123118 ◽

2020 ◽

Vol 13 (12) ◽

pp. 3118

Author(s):

Qiushi Bi ◽

Guoqiang Wang ◽

Yongpeng Wang ◽

Zongwei Yao ◽

Robert Hall

Keyword(s):

Genetic Algorithm ◽

Energy Consumption ◽

Trajectory Planning ◽

Open Pit ◽

Open Pit Mining ◽

Second Phase ◽

Multi Objective ◽

Multi Objective Genetic Algorithm ◽

Phase Optimization ◽

Cable Shovel

As one of the most essential earth-moving equipment, cable shovels significantly influence the efficiency and economy in the open-pit mining industry. The optimal digging trajectory planning for each cycle is the base for achieving effective and energy-saving operation, especially for robotic excavation, in which case, the digging trajectory can be precisely tracked. In this paper, to serve the vision of cable shovel automation, a two-phase multi-objective genetic algorithm was established for optimal digging trajectory planning. To be more specific, the optimization took digging time and energy consumption per payload as objects with the constraints of the limitations of the driving system and geometrical conditions. The WK-55-type cable shovel was applied for the validation of the effectiveness of the multi-objective optimization method for digging trajectories. The digging performance of the WK-55 cable shovel was tested in the Anjialing mining site to establish the constraints. Besides, the digging parameters of the material were selected based on the tested data to make the optimization in line with the condition of the real digging operations. The optimization results for different digging conditions indicate that the digging time decreased from an average of 20 s to 10 s after the first phase optimization, and the energy consumption per payload reduced by 13.28% after the second phase optimization, which validated the effectiveness and adaptivity of the optimization algorithm established in this paper.

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A Dual-Thread Method for Time-Optimal Trajectory Planning in Joint Space Based on Improved NGA

Journal of Robotics ◽

10.1155/2020/6859589 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Kaipeng Zhang ◽

Ning Liu ◽

Gao Wang

Keyword(s):

Genetic Algorithm ◽

Trajectory Planning ◽

Optimal Trajectory ◽

Joint Space ◽

Time Saving ◽

Expected Time ◽

Fitness Sharing ◽

Elite Strategy ◽

Time Optimal ◽

Optimal Trajectory Planning

To solve the problem that the time-consuming optimization process of Genetic Algorithm (GA) can erode the expected time-saving brought by the algorithm, time-optimal trajectory planning based on cubic spline was used, after the modification to classical fitness sharing function of NGA, a dual-threaded method utilizing elite strategy characteristic was designed which was based on Niche Genetic Algorithm (NGA) with the fitness sharing technique. The simulation results show that the proposed method can mitigate the contradiction of the long term the optimization algorithm takes but a short running time the trajectory gets, demonstrating the effectiveness of the proposed method. Besides, the improved fitness sharing technique has reduced the subjective process of determining relevant parameters and the optimized trajectory results met performance constraints of the robot joints.

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