Trajectory Planning for Concrete Pump Truck Based on Intelligent Hill Climbing and Genetic Algorithm

This paper takes the five –link concrete pump boom as the research object, and transforms its trajectory planning issue into a multi-object optimization problem. Using intelligent hill climbing algorithm and genetic algorithm, and integrating them closely to ensure real-time online planning for the pump truck effectively, and make the planned motion trajectory for the boom is global optimized under particular constrained conditions. Simulation and performance comparison experiments show that this hybrid algorithm is practical and effective, which offers a new approach for the trajectory planning problem of concrete pump truck.

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Path planning of a mobile robot using genetic heuristics

Robotica ◽

10.1017/s0263574798000289 ◽

1998 ◽

Vol 16 (5) ◽

pp. 575-588 ◽

Cited By ~ 45

Author(s):

Andreas C. Nearchou

Keyword(s):

Genetic Algorithm ◽

Path Planning ◽

Mobile Robot ◽

Optimal Path ◽

Hill Climbing ◽

Planning Problem ◽

Well Performance ◽

Simulation Results ◽

Task Oriented ◽

Path Planning Problem

A genetic algorithm for the path planning problem of a mobile robot which is moving and picking up loads on its way is presented. Assuming a findpath problem in a graph, the proposed algorithm determines a near-optimal path solution using a bit-string encoding of selected graph vertices. Several simulation results of specific task-oriented variants of the basic path planning problem using the proposed genetic algorithm are provided. The results obtained are compared with ones yielded by hill-climbing and simulated annealing techniques, showing a higher or at least equally well performance for the genetic algorithm.

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Design and performance comparison of variable parameter nonlinear PID controller and genetic algorithm based PID controller

2012 International Symposium on Innovations in Intelligent Systems and Applications ◽

10.1109/inista.2012.6246935 ◽

2012 ◽

Cited By ~ 23

Author(s):

Mehmet Korkmaz ◽

Omer Aydogdu ◽

Huseyin Dogan

Keyword(s):

Genetic Algorithm ◽

Pid Controller ◽

Variable Parameter ◽

Performance Comparison ◽

Nonlinear Pid Controller ◽

And Performance

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Online trajectory planning for UCAV performing ground attacks based on threat modeling

Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University ◽

10.1051/jnwpu/20213920382 ◽

2021 ◽

Vol 39 (2) ◽

pp. 382-391

Author(s):

Jiayu Tang ◽

Xiangmin Li ◽

Jinjin Dai

Keyword(s):

Trajectory Planning ◽

Aerodynamic Characteristics ◽

Planning Method ◽

Planning Problem ◽

Threat Modeling ◽

Performance Constraints ◽

Engine Thrust ◽

Online Planning ◽

Aerial Vehicle ◽

Kinematic Models

The paper studies how to plan the trajectories of an unmanned combat aerial vehicle (UCAV) that releases its airborne weapons and presents an online trajectory planning method based on threat modeling. Firstly, it analyzes the aerodynamic characteristics, engine thrust and fuel characteristics of the UCAV and builds its dynamic and kinematic models. Secondly, the trajectory planning model of the UCAV is formulated with flight performance constraints and battlefield threat constraints considered. To improve the accuracy of ground attacks, the envelope of a guided bomb's acceptable region of weapon release is studied, and the release center and posture of the guided bomb work as terminal planning conditions. Thirdly, an online trajectory planning method is proposed. With the help of threat modeling, the complicated trajectory planning problem is transformed into a simplified situation classification. Finally, the simulation results demonstrate that the online planning method proposed in the paper can provide feasible trajectories for a UCAV to succeed in releasing its airborne weapons.

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Collision-Free and Energy-Saving Trajectory Planning for Large-Scale Redundant Manipulator Using Improved PSO

Mathematical Problems in Engineering ◽

10.1155/2013/208628 ◽

2013 ◽

Vol 2013 ◽

pp. 1-8 ◽

Cited By ~ 4

Author(s):

Min Jin ◽

Dan Wu

Keyword(s):

Energy Saving ◽

Trajectory Planning ◽

Large Scale ◽

Pso Algorithm ◽

Inverse Kinematic ◽

Outdoor Environment ◽

Inverse Kinematic Problem ◽

Concrete Pump Truck ◽

Concrete Pump ◽

Improved Pso

The large-scale boom system, such as the five-arm concrete pump truck with the arm length of 36–65 meters, usually operates in an unknown dynamic outdoor environment. The motion safety and the energy consumption are thus the two vital measurements to the effectiveness of the trajectory planning for the large-scale boom system. Due to the redundancy of the large-scale boom system and some drawbacks of the original particle swarm optimization (PSO) algorithm, an improved PSO algorithm is presented to solve the inverse kinematic problem of the redundant large-scale boom system. By the improved PSO algorithm, the energy-saving trajectory planning of the large-scale boom system that operates in a workspace without obstacles and with obstacles is optimized, which considers different important degrees of the subgoals, respectively. The optimal results from the simulation study and the practical application verify the effectiveness of the proposed planning strategy. At the same time, the performance of the improved strategy is compared with that of the traditional, and the superiority is further demonstrated.

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Multitask-Based Trajectory Planning for Redundant Space Robotics Using Improved Genetic Algorithm

Applied Sciences ◽

10.3390/app9112226 ◽

2019 ◽

Vol 9 (11) ◽

pp. 2226 ◽

Cited By ~ 2

Author(s):

Suping Zhao ◽

Zhanxia Zhu ◽

Jianjun Luo

Keyword(s):

Genetic Algorithm ◽

Trajectory Planning ◽

Optimization Problem ◽

Space Station ◽

Planning Problem ◽

Space Robotics ◽

Improved Genetic Algorithm ◽

Unknown Parameters ◽

Special Value ◽

Sine Functions

This work addresses the multitask-based trajectory-planning problem (MTTP) for space robotics, which is an emerging application of successively executing tasks in assembly of the International Space Station. The MTTP is transformed into a parameter-optimization problem, where piecewise continuous-sine functions are employed to depict the joint trajectories. An improved genetic algorithm (IGA) is developed to optimize the unknown parameters. In the IGA, each chromosome consists of three parts, namely the waypoint sequence, the sequence of the joint configurations, and a special value for the depiction of the joint trajectories. Numerical simulations, including comparisons with two other approaches, are developed to test IGA validity.

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