scholarly journals Parallel Swarm Intelligent Motion Planning with Energy-Balanced for Multirobot in Obstacle Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Shoubao Su ◽  
Wei Zhao ◽  
Chishe Wang
Keyword(s):  
Energy Consumption ◽  
Motion Planning ◽  
Trajectory Optimization ◽  
Intelligent Systems ◽  
Optimal Path ◽  
Complex Scene ◽  
Dynamic Constraints ◽  
Device Architecture ◽  
Real Scene ◽  
Path Search

Multirobot motion planning is always one of the critical techniques in edge intelligent systems, which involve a variety of algorithms, such as map modeling, path search, and trajectory optimization and smoothing. To overcome the slow running speed and imbalance of energy consumption, a swarm intelligence solution based on parallel computing is proposed to plan motion paths for multirobot with many task nodes in a complex scene that have multiple irregularly-shaped obstacles, which objective is to find a smooth trajectory under the constraints of the shortest total distance and the energy-balanced consumption for all robots to travel between nodes. In a practical scenario, the imbalance of task allocation will inevitably lead to some robots stopping on the way. Thus, we firstly model a gridded scene as a weighted MTSP (multitraveling salesman problem) in which the weights are the energies of obstacle constraints and path length. Then, a hybridization of particle swarm and ant colony optimization (GPSO-AC) based on a platform of Compute Unified Device Architecture (CUDA) is presented to find the optimal path for the weighted MTSPs. Next, we improve the A ∗ algorithm to generate a weighted obstacle avoidance path on the gridded map, but there are still many sharp turns on it. Therefore, an improved smooth grid path algorithm is proposed by integrating the dynamic constraints in this paper to optimize the trajectory smoothly, to be more in line with the law of robot motion, which can more realistically simulate the multirobot in a real scene. Finally, experimental comparisons with other methods on the designed platform of GPUs demonstrate the applicability of the proposed algorithm in different scenarios, and our method strikes a good balance between energy consumption and optimality, with significantly faster and better performance than other considered approaches, and the effects of the adjustment coefficient q on the performance of the algorithm are also discussed in the experiments.

scholarly journals PATH PLACEMENT OPTIMIZATION OF MANIPULATORS BASED ON ENERGY CONSUMPTION: APPLICATION TO THE ORTHOGLIDE 3-AXIS

2009 ◽  
Vol 33 (3) ◽  
pp. 523-541 ◽  
Author(s):  
Raza Ur-Rehman ◽  
Stéphane Caro ◽  
Damien Chablat ◽  
Philippe Wenger
Keyword(s):  
Energy Consumption ◽  
Optimal Path ◽  
Electric Energy ◽  
Optimal Location ◽  
Degree Of Freedom ◽  
Parallel Kinematic Machine ◽  
Dynamic Constraints ◽  
Placement Optimization ◽  
Parallel Kinematic ◽  
Three Degree Of Freedom

This paper deals with the optimal path placement for a manipulator based on energy consumption. It proposes a methodology to determine the optimal location of a given test path within the workspace of a manipulator with minimal electric energy used by the actuators while taking into account the geometric, kinematic and dynamic constraints. The proposed methodology is applied to the Orthoglide 3-axis, a three-degree-of-freedom translational parallel kinematic machine (PKM), as an illustrative example.

A survey of energy-efficient motion planning for wheeled mobile robots

2020 ◽  
Vol 47 (4) ◽  
pp. 607-621
Author(s):  
Haojie Zhang ◽  
Yudong Zhang ◽  
Tiantian Yang
Keyword(s):  
Energy Consumption ◽  
Motion Planning ◽  
Mobile Robots ◽  
Energy Efficient ◽  
Wheeled Mobile Robots ◽  
Content Type ◽  
Dynamic Constraints ◽  
Time Optimization ◽  
Energy Consumption Model ◽  
Real Time Optimization

Purpose As wheeled mobile robots find increasing use in outdoor applications, it becomes more important to reduce energy consumption to perform more missions efficiently with limit energy supply. The purpose of this paper is to survey the current state-of-the-art on energy-efficient motion planning (EEMP) for wheeled mobile robots. Design/methodology/approach The use of wheeled mobile robots has been increased to replace humans in performing risky missions in outdoor applications, and the requirement of motion planning with efficient energy consumption is necessary. This study analyses a lot of motion planning technologies in terms of energy efficiency for wheeled mobile robots from 2000 to present. The dynamic constraints play a key role in EEMP problem, which derive the power model related to energy consumption. The surveyed approaches differ in the used steering mechanisms for wheeled mobile robots, in assumptions on the structure of the environment and in computational requirements. The comparison among different EEMP methods is proposed in optimal, computation time and completeness. Findings According to lots of literature in EEMP problem, the research results can be roughly divided into online real-time optimization and offline optimization. The energy consumption is considered during online real-time optimization, which is computationally expensive and time-consuming. The energy consumption model is used to evaluate the candidate motions offline and to obtain the optimal energy consumption motion. Sometimes, this optimization method may cause local minimal problem and even fail to track. Therefore, integrating the energy consumption model into the online motion planning will be the research trend of EEMP problem, and more comprehensive approach to EEMP problem is presented. Research limitations/implications EEMP is closely related to robot’s dynamic constraints. This paper mainly surveyed in EEMP problem for differential steered, Ackermann-steered, skid-steered and omni-directional steered robots. Other steering mechanisms of wheeled mobile robots are not discussed in this study. Practical implications The survey of performance of various EEMP serves as a reference for robots with different steering mechanisms using in special scenarios. Originality/value This paper analyses a lot of motion planning technologies in terms of energy efficiency for wheeled mobile robots from 2000 to present.

scholarly journals Anti-rollover Motion Planning for Automated Heavy Trucks

2020 ◽  
Author(s):  
Zhilin Jin ◽  
Jingxuan Li ◽  
Hong Wang ◽  
Jun Li ◽  
Chaosheng Huang
Keyword(s):  
Model Predictive Control ◽  
Motion Planning ◽  
Predictive Control ◽  
Degrees Of Freedom ◽  
Optimal Path ◽  
Front Axle ◽  
Dynamic Constraints ◽  
Rollover Risk ◽  
Heavy Trucks ◽  
Planning Algorithm

Abstract Anti-rollover is an important performance for automated heavy trucks, which has been seldomly considered in the motion planning. This paper proposes an anti-rollover motion planning based on model predictive control (MPC) for automated heavy trucks. Taking the coupling of roll motion of sprung mass of the front axle with that of the drive axle into consideration, a seven degrees of freedom rollover dynamics model is established, and an evaluation index that can accurately describe the rollover motion is derived for heavy trucks. Then, a model predictive control strategy is designed for motion planning that combines the rollover dynamics, the artificial potential field for obstacle avoidance, and the trajectory tracking. In addition, the optimal path is calculated that considers collision avoidance, anti-rollover and vehicle dynamic constraints. Furthermore, three typical scenarios are applied to validate the performance of the proposed motion planning algorithm. The obtained results demonstrate that the proposed anti-rollover motion planning can effectively avoid collisions and reduce the rollover risk simultaneously when confronting edge scenarios.

scholarly journals Identification of Optimal Path in Power System Network Using Bellman Ford Algorithm

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
S. Hemalatha ◽  
P. Valsalal
Keyword(s):  
Power System ◽  
Transmission Line ◽  
Shortest Path ◽  
Voltage Stability ◽  
Optimal Path ◽  
Power Balance ◽  
Line Voltage ◽  
System Components ◽  
Path Search ◽  
Search Guidance

Power system network can undergo outages during which there may be a partial or total blackout in the system. In that condition, transmission of power through the optimal path is an important problem in the process of reconfiguration of power system components. For a given set of generation, load pair, there could be many possible paths to transmit the power. The optimal path needs to consider the shortest path (minimum losses), capacity of the transmission line, voltage stability, priority of loads, and power balance between the generation and demand. In this paper, the Bellman Ford Algorithm (BFA) is applied to find out the optimal path and also the several alternative paths by considering all the constraints. In order to demonstrate the capability of BFA, it has been applied to a practical 230 kV network. This restorative path search guidance tool is quite efficient in finding the optimal and also the alternate paths for transmitting the power from a generating station to demand.

Trajectory Optimization of Energy Consumption and Expected Service Life of a Robotic System

2021 ◽  
Author(s):  
Florian Stuhlenmiller ◽  
Debora Clever ◽  
Stephan Rinderknecht ◽  
Michael Lutter ◽  
Jan Peters
Keyword(s):  
Energy Consumption ◽  
Service Life ◽  
Trajectory Optimization ◽  
Robotic System

Design and Control of Drones

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Mark W. Mueller ◽  
Seung Jae Lee ◽  
Raffaello D’Andrea
Keyword(s):  
Energy Consumption ◽  
Motion Planning ◽  
Recent Progress ◽  
Scaling Laws ◽  
Autonomous Systems ◽  
Annual Review ◽  
Publication Date ◽  
Trade Offs ◽  
Active Research ◽  
And Control

The design and control of drones remain areas of active research, and here we review recent progress in this field. In this article, we discuss the design objectives and related physical scaling laws, focusing on energy consumption, agility and speed, and survivability and robustness. We divide the control of such vehicles into low-level stabilization and higher-level planning such as motion planning, and we argue that a highly relevant problem is the integration of sensing with control and planning. Lastly, we describe some vehicle morphologies and the trade-offs that they represent. We specifically compare multicopters with winged designs and consider the effects of multivehicle teams. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 5 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Minimum Energy Trajectory Optimization for Driving Systems of Palletizing Robot Joints

2018 ◽  
Vol 2018 ◽  
pp. 1-26
Author(s):  
Ying He ◽  
Jiangping Mei ◽  
Zhiwei Fang ◽  
Fan Zhang ◽  
Yanqin Zhao
Keyword(s):  
Fourier Series ◽  
Energy Consumption ◽  
Total Energy ◽  
Dynamic Model ◽  
Trajectory Optimization ◽  
Joint Torque ◽  
Total Energy Consumption ◽  
Energy Consumption Model ◽  
Energy Consumption Optimization ◽  
Consumption Model

Palletizing robot is widely used in logistics operation. At present, people pay attention to not only the loading capacity and working efficiency of palletizing robots, but also the energy consumption in their working process. This paper takes MD1200-YJ palletizing robot as the research object and studies the problem of low energy consumption optimization of joint driving system from the perspective of trajectory optimization. Firstly, a multifactor dynamic model of palletizing robot is established based on the conventional inverse rigid body dynamic model of the robot, the Stribeck friction model and the spring balance torque model. And then based on joint torque, friction torque, motion parameter, and joule’s law, the useful work model, thermal loss model of joint motor, friction energy consumption model of joint system, and total energy consumption model of palletizing robot are established, and through simulation and experiment, the correctness of the multifactor dynamic model and energy consumption model is verified. Secondly, based on the Fourier series approximation method to construct the joint trajectory expression, the minimum total energy consumption as the optimization objective, with coefficients of Fourier series as optimization variables, the motion parameters of initial and final position, and running time constant as constraint conditions, the genetic algorithm is used to solve the optimization problem. Finally, through the simulation analysis the optimized Fourier series motion law and the 3-4-5 polynomial motion law are comprehensively evaluated to verify the effectiveness of the optimization method. Moreover, it provides the theoretical basis for the follow-up research and points out the direction of improvement.

scholarly journals A Heuristic Evolutionary Algorithm of UAV Path Planning

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Zhangjie Fu ◽  
Jingnan Yu ◽  
Guowu Xie ◽  
Yiming Chen ◽  
Yuanhang Mao
Keyword(s):  
Energy Consumption ◽  
Evolutionary Algorithm ◽  
Rapid Development ◽  
Optimal Path ◽  
Battery Life ◽  
Intelligent Robot ◽  
Urgent Problem ◽  
Intelligent Robots ◽  
Aerial Vehicle ◽  
The Cost

With the rapid development of the network and the informatization of society, how to improve the accuracy of information is an urgent problem to be solved. The existing method is to use an intelligent robot to carry sensors to collect data and transmit the data to the server in real time. Many intelligent robots have emerged in life; the UAV (unmanned aerial vehicle) is one of them. With the popularization of UAV applications, the security of UAV has also been exposed. In addition to some human factors, there is a major factor in the UAV’s endurance. UAVs will face a problem of short battery life when performing flying missions. In order to solve this problem, the existing method is to plan the path of UAV flight. In order to find the optimal path for a UAV flight, we propose three cost functions: path security cost, length cost, and smoothness cost. The path security cost is used to determine whether the path is feasible; the length cost and smoothness cost of the path directly affect the cost of the energy consumption of the UAV flight. We proposed a heuristic evolutionary algorithm that designed several evolutionary operations: substitution operations, crossover operations, mutation operations, length operations, and smoothness operations. Through these operations to enhance our build path effect. Under the analysis of experimental results, we proved that our solution is feasible.

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