Integrating Assembly Design, Sequence Optimization, and Advanced Path Planning

Path planning is key research topic in the field of robotics research, transportation, bioinformatics, virtual prototype designing, gaming, computer aided designs, and virtual reality estimation. In optimal path planning, it is important to determine the collision free optimal and shortest path. There may be various aspects to determine the optimal path based on workspace environment and obstacle types. In this research work, optimal path is determined based on the workspace environment having static obstacles and unknown environment area. A hybrid approach of meta-heuristic algorithm of Bat Algorithm (BA) and Cuckoo Search (CS) is used to determine the optimal path from defined source to destination. For experimentation, case study area of Alwar region, Rajasthan is considered which consist of urban and vegetation area. The reason for the selection of BA and CS for the path planning is the wide application and success of implementation of these concepts in the field of robotics and path planning. The consideration of individual BA for path planning can lead to problem of trapping between local optima. This obligates us to hybridize the concept of BA with some other efficient problem solving concept like CS. The hybridized concept of BA and CS is initially tested with standard benchmarks functions, after that considered for the application of path planning. Results of hybrid path planning concept are compared with individual CS and BA concepts in terms of simulation time and minimum number of iteration required to achieve the optimal path from defined source to destination. The evaluated results comparison of hybrid approach with individual concepts indicates the dominance of proposed hybrid concept in terms of standard benchmarks functions and other parameters as well.

Assembly sequence planning is a typical of combinatorial optimization problem which is difficult to be tackled when the number of parts of assembly becomes large. To reduce the searching space of assembly sequence planning of complex products, assembly sequences merging based on assembly unit partitioning is suggested. Assembly unit partitioning is presented to decompose the complex products into a group of assembly units containing a reduced number of parts or components, and the assembly design constraints and the assembly process constraints are comprehensively taken into account. The global optimal assembly sequences can be acquired through three steps. Firstly, the assembly units and decision graph of assembly unit are generated utilizing fuzzy analytical hierarchy process approach. Secondly, the optimal or near-optimal subsequences of assembly units can be obtained with current efficient methods of assembly sequence planning. Thirdly, under the assembly interference of assembly relations (geometrical constraints) of the whole products and the assembly precedence concluded by subsequences of assembly units, the assembly sequence merging is implemented to generate the global assembly sequences, and the optimal sequence is obtained through assembly sequences evaluation. The assembly constraints considered at the two previous steps is represented by the evaluation function. The effectiveness of the method is verified by an illustrative example and the results show that the searching space of assembly sequence merging of complex products is reduced remarkably and the optimal assembly sequence of the whole produces is obtained.

ABSTRACTDue to interference phenomena among unnatural dimensions of the motion robots’ operations space, optimal path planning of them has to satisfy not just one criterion, but rather multi-objects. In this paper, we propose a novel multi-object approach for optimal mobile robot path planning, based on bees pollen optimizer (BPO). We consider two objects of distance and smooth path of the special plan for motion robots for constructing a minimization one. In operation environment for action robots, the location of the target and the obstacles are set up for the solution of BPO. The selected sequence of the mobile robot is a set of the chosen global best settlement in each iteration, which updates its archived data throughout the movement for motion robots in order. A series of simulations are executed in some environments for the best pathway once the robot reaches its goal. The results indicate that the proposed approach offered the robot path to its target without touching the obstacles, and the proposed method may be an alternative approach to optimize the motion robot path planning.

Under harsh geographical conditions where manned flight is not possible, the ability of the unmanned aerial vehicle (UAV) to successfully carry out the payload hold–release mission by avoiding obstacles depends on the optimal path planning and tracking performance of the UAV. The ability of the UAV to plan and track the path with minimum energy and time consumption is possible by using the flight parameters. This study performs the optimum path planning and tracking using Harris hawk optimization (HHO)–grey wolf optimization (GWO), a hybrid metaheuristic optimization algorithm, to enable the UAV to actualize the payload hold–release mission avoiding obstacles. In the study, the hybrid HHO–GWO algorithm, which stands out with its avoidance of local minima and speed convergence, is used to successfully obtain the feasible and effective path. In addition, the effect of the mass change uncertainty of the UAV on optimal path planning and tracking performance is determined. The effectiveness of the proposed approach is tested by comparing it with the metaheuristic swarm optimization algorithms such as particle swarm optimization (PSO) and GWO. The experimental results obtained indicate that the proposed algorithm generates a fast and safe optimal path without becoming stuck with local minima, and the quadcopter tracks the generated path with minimum energy and time consumption.

A tree-model for assembly sequences planning is proposed after analyzing the characteristic of assembly sequence evaluation, and the generation algorithm of the tree-model is proposed. Based on the tree-model of assembly sequence, the two-phase method for synthetically evaluating and optimizing assembly sequence layer by layer is expounded. Weights of all evaluation indexes are calculated by using the entropy method. Then, by using the fuzzy comprehensive evaluation, the assembly sequence of all data sub-nodes under A or AP in every layer of the assembly sequence tree-model is optimized. Finally, the optimal assembly sequence is generated. Using this method, the calculation extension of the assembly sequence is reduced, and the evaluation efficiency is heightened. The theory is validated by an example of gear reducer.

Optimal path planning is a key component in recent autonomous driving technology. Many navigation companies have developed services for predicting and navigating the optimal path by receiving information from users in real-time. However, these global path-based methods cannot predict the local, refined path due the resolution limitation of the GPS. In this paper, we present an object detection and lane detection system to predict the optimal local path using point cloud data(PCD) acquired by an autonomous dataset collection vehicle.

Realizing efficient and accurate positional movement of industrial robots is one of the key technologies to accelerate industrial development. In this paper, a four-wheel-drive McNamee mobile robot is selected as the research object, and the improved IPSO-IDE algorithm is proposed, which is combined with a PID controller to realize the accurate positioning and optimal path planning of industrial machines. The experimental results of robot kinematic parameter calibration show that the distance error of the robot calibrated with the algorithm is greatly reduced, and not only that, the IPSO-IDE algorithm reduces the average distance error of the robot to only 0.124 mm, which is much smaller than that of the PSO algorithm and the DE algorithm. The IPSO-IDE algorithm is capable of planning the optimal motion path in a short time, as demonstrated by the results of robot map environment simulation experiments. In the simulated map environment of 10 m × 10 m, the convergence speed of the algorithm’s planning path is 12.69 seconds, and the generated optimal path length is 15.0221 m, which is better than other algorithms. In conclusion, the proposed IPSO-IDE algorithm has an excellent performance in terms of path-planning capabilities for industrial robots.

The rapid expansion in the industrial revolution has been aroused to utilize of the robot in various high accuracy and productivity applications. Optimal path planning is an important concern in navigation of mobile robot interesting with finding an optimal path. The firefly algorithm is considered as an increasingly promising tool of Swarm Intelligence, which is used in various optimization areas. The objective of the proposed method is to find the free-collision points in mobile robot environment and generate the optimal path based on firefly algorithm and D* algorithm. D* algorithm has been applied to find the shortest path. The essential function of applying the firefly algorithm and Quadratic parametric equations is to iterative globally search and determine the accurate positions of a set of intermediate via points within the free space and generating the corresponding smooth trajectory at a specified time respectively. The collision with obstacles can be efficiently avoided by constructing the free Cartesian space, which guarantee a free collision path planning. The simulation results illustrate the efficiency of proposed methods performance in finding and determining the optimal path and trajectory even in various degrees of environmental complexity.

In-line inspection of advanced components remains a challenging task in industry. The authors will describe an automated methodology that uses numerical simulations to automatically determine the best set of experimental parameters to inspect the structure on defects using active thermography. The inspection is performed using a robotic arm and advanced path-planning tools to determine the optimal positions of the measurement points and excitation points. During the path planning, the directional emissivity is considered for the complex surface, and a minimization of the amount of measurement points is performed. The numerical simulation optimization used a genetic algorithm and spline regression model to optimize the heat power, robot speed, camera frame rate, and excitation timing to fulfill the automatic inspection.

Main study areas for robotics research can be given as: mapping, localization, navigation and exploration. Given a robot’s current position, partial map of the environment and a goal position; navigation problem can be defined as optimal path planning and path following. Path planning and path following problem should be handled according to environment being static or dynamic, robot's mobility capabilities, sensors used on the robot and the roughness of the environment. In the study a four wheeled, skid-steering robot with laser range finder and depth sensor is built for Gazebo simulation environment. Also a statically structured labyrinth that consists of 15 degree continuous ramps, 15 degree discontinuous ramps, amorphous holes that robot cannot autonomously escape from if fallen into, walls and discontinuous obstacles that are below the robot laser height. 2D simultaneous localization and mapping, 3D mapping, path planning and path following with respect to the 3D map are implemented on Robot Operating System (ROS). Optimal path planning in rough terrain is accomplished by combining A* heuristic with a function of height difference of the 3D map nodes. Path following is carried out by turning-to and moving-towards actions on each sequential path node pairs. Tests performed on the labyrinth shows that obstacle avoidance, path planning and path following can be carried out successfully with the given implementation.

This paper mainly completes a research on real-time optimal path planning and guidance method for ground targets to be evaded, proposes an artificial repulsive field modeling method for different area sizes and different importance levels of ground targets, and generates the current optimal evasive flight path and corresponding guidance instructions based on real-time landing point estimation information. In this paper, the repulsion field model for a single area and the repulsion field modeling method for multiple areas are given, as well as the derivation process of the optimal avoidance path guidance instruction.KeywordsArtificial repulsion fieldTarget avoidancePath planning

Background: Path planning technology has a wide range of applications in many fields. Applications in the field of high technology include: autonomous and touchless action of robots; obstacle avoidance and defense flight of drones; cruise missiles to avoid radar search, anti- bouncing attacks, and to complete the task of sudden defense and demolition. Applications in daily life include: GPS navigation; road planning based on GIS system; urban road network planning and navigation. Applications in the field of decision-making management include: vehicle problem (VRP) in logistics management and similar resource allocation problems in resource management. Routing problems in the field of communication technology. Any planning problem that can be topologized as a point and line network can basically be solved by the method of path planning. Different intelligent algorithms have different characteristics, and their application scope and fields are also different, so it is of great significance for the development of path planning technology to study the path planning intelligent algorithms from the characteristics of the algorithms themselves and their applications. Objective: Analyze the advantages and disadvantages of various types of planning algorithms, look forward to the future development trend of mobile robot path planning, and provide certain ideas for the research of mobile robot path planning. Methods: Search journals, patents, conferences, and papers related to mobile robot path planning, and summarize and analyze the advantages and disadvantages of various planning algorithms. Results: Based on the research results of many scholars, this study summarizes different mobile robot path planning methods. It is divided into four categories: traditional planning, intelligent search, artificial intelligence and local obstacle avoidance. This paper introduces and analyzes the latest research results of these types, including their design ideas, advantages and disadvantages, and improvement measures. The research methods adopted are analyzed in order to maximize the advantages of each algorithm and expand the application field of robot path planning to provide ideas and references. Conclusion: This paper provides guidance for the design and optimization of robot path planning. Finally, this paper summarizes the future development trend of robot path planning, and looks forward to the future development trend and key areas of robot path planning.

The robotic mobile fulfillment system (RMFS) is a new automatic warehousing system, a type of green technology, and an emerging trend in the logistics industry. In this study, we take an RMFS as the research object and combine the connected issues of storage location assignment and path planning into one optimization problem from the perspective of collaborative optimization. A sustainable mathematical model for the collaborative optimization of storage location assignment and path planning (COSLAPP) is established, which considers the relationship between the location assignment of goods and rack storage and path planning in an RMFS. On this basis, we propose a location assignment strategy for goods clustering and rack turnover, which utilizes reservation tables, sets AGV operation rules to resolve AGV running conflicts, and improves the A-star(A*) algorithm based on the node load to find the shortest path by which the AGV handling the racks can complete the order picking. Ultimately, simulation studies were performed to ascertain the effectiveness of COSLAPP in the RMFS; the results show that the new approach can significantly improve order picking efficiency, reduce energy consumption, and lessen the operating costs of the warehouse of a distribution center.

In this paper, we propose a novel deep reinforcement learning (DRL) method for optimal path planning for mobile robots using dynamic programming (DP)-based data collection. The proposed method can overcome the slow learning process and improve training data quality inherently in DRL algorithms. The main idea of our approach is as follows. First, we mapped the dynamic programming method to typical optimal path planning problems for mobile robots, and created a new efficient DP-based method to find an exact, analytical, optimal solution for the path planning problem. Then, we used high-quality training data gathered using the DP method for DRL, which greatly improves training data quality and learning efficiency. Next, we established a two-stage reinforcement learning method where, prior to the DRL, we employed extreme learning machines (ELM) to initialize the parameters of actor and critic neural networks to a near-optimal solution in order to significantly improve the learning performance. Finally, we illustrated our method using some typical path planning tasks. The experimental results show that our DRL method can converge much easier and faster than other methods. The resulting action neural network is able to successfully guide robots from any start position in the environment to the goal position while following the optimal path and avoiding collision with obstacles.

This article addresses the problem of optimal flight path planning when surveying the field using several unmanned aerial vehicles (UAVs). The trajectories of the drones are configured in order to minimize the time of the sub-areas coverage (assigned to each drone in the group), provided that the whole field is completely covered. The algorithm consists of four stages: converting the coordinates of a given field into Cartesian (metric system); splitting the field into sub-areas assigned to be covered by each drone in the group; forming the flight trajectory (plan) for each drone; and iterative minimization of the maximum time needed to cover the assigned sub-areas. The algorithm for optimal flight path planning is more time-effective than the ones where the drone moves straight from one end of the field to the other with shifts (moving along the boundary of the field at a certain distance). To implement the developed, an additional software module of the virtual simulator has been developed. The workability and the adequacy of the proposed algorithm were experimentally confirmed using a three-dimensional model of a real agriculture field in the virtual simulator.