Model-driven path planning for robotic plasma cutting of branch pipe with single Y-groove based on pipe-rotating scheme

The automatic cutting of intersecting pipes is a challenging task in manufacturing.For improved automation and accuracy,this paper proposes a model-driven path planning approach for the robotic plasma cutting of a branch pipe with a single Y-groove.Firstly,it summarizes the intersection forms and introduces a dual-pipe intersection model.Based on this model,the moving three-plane structure(a description unit of the geometric characteristics of the intersecting curve)is constructed,and a geometric model of the branch pipe with a single Y-groove is defined.Secondly,a novel mathematical model for plasma radius and taper compensation is established.Then,the compensation model and groove model are integrated by establishing movable frames.Thirdly,to prevent collisions between the plasma torch and workpiece,the torch height is planned and a branch pipe-rotating scheme is proposed.Through the established models and moving frames,the planned path description of cutting robot is provided in this novel scheme.The accuracy of the proposed method is verified by simulations and robotic cutting experiments.

Generative Adversarial Network Based Heuristics for Sampling-Based Path Planning

Sampling-based path planning is a popular methodology for robot path planning.With a uniform sampling strategy to explore the state space,a feasible path can be found without the complex geometric modeling of the configuration space.However,the quality of the initial solution is not guaranteed,and the convergence speed to the optimal solution is slow.In this paper,we present a novel image-based path planning algorithm to overcome these limitations.Specifically,a generative adversarial network(GAN)is designed to take the environment map(denoted as RGB image)as the input without other preprocessing works.The output is also an RGB image where the promising region(where a feasible path probably exists)is segmented.This promising region is utilized as a heuristic to achieve non-uniform sampling for the path planner.We conduct a number of simulation experiments to validate the effectiveness of the proposed method,and the results demonstrate that our method performs much better in terms of the quality of the initial solution and the convergence speed to the optimal solution.Furthermore,apart from the environments similar to the training set,our method also works well on the environments which are very different from the training set.

Mobile robot path planning based on adaptive bacterial foraging algorithm

The utilization of biomimicry of bacterial foraging strategy was considered to develop an adaptive control strategy for mobile robot, and a bacterial foraging approach was proposed for robot path planning. In the proposed model, robot that mimics the behavior of bacteria is able to determine an optimal collision-free path between a start and a target point in the environment surrounded by obstacles. In the simulation, two test scenarios of static environment with different number obstacles were adopted to evaluate the performance of the proposed method. Simulation results show that the robot which reflects the bacterial foraging behavior can adapt to complex environments in the planned trajectories with both satisfactory accuracy and stability.

Intelligent learning technique based-on fuzzy logic for multi-robot path planning

Soccer robot system is a tremendously challenging intelligent system developed to mimic human soccer competition based on the multi discipline research: robotics, intelligent control, computer vision, etc. robot path planning strategy is a very important subject concerning to the performance and intelligence degree of the multi robot system. Therefore, this paper studies the path planning strategy of soccer system by using fuzzy logic. After setting up two fuzziers and two sorts of fuzzy rules for soccer system, fuzzy logic is applied to workspace partition and path revision. The experiment results show that this technique can well enhance the performance and intelligence degree of the system.

NOVEL APPROACH FOR ROBOT PATH PLANNING BASED ON NUMERICAL ARTIFICIAL POTENTIAL FIELD AND GENETIC ALGORITHM

A novel approach for collision-free path planning of a multiple degree-of-freedom （DOF） articulated robot in a complex environment is proposed. Firstly, based on visual neighbor point （VNP）, a numerical artificial potential field is constructed in Cartesian space, which provides the heuristic information, effective distance to the goal and the motion direction for the motion of the robot joints. Secondly, a genetic algorithm, combined with the heuristic rules, is used in joint space to determine a series of contiguous configurations piecewise from initial configuration until the goal configuration is attained. A simulation shows that the method can not only handle issues on path planning of the articulated robots in environment with complex obstacles, but also improve the efficiency and quality of path planning.

An Enhanced Equilibrium Optimizer for Solving Optimization Tasks

The equilibrium optimizer(EO)represents a new,physics-inspired metaheuristic optimization approach that draws inspiration from the principles governing the control of volume-based mixing to achieve dynamic mass equilibrium.Despite its innovative foundation,the EO exhibits certain limitations,including imbalances between exploration and exploitation,the tendency to local optima,and the susceptibility to loss of population diversity.To alleviate these drawbacks,this paper introduces an improved EO that adopts three strategies:adaptive inertia weight,Cauchy mutation,and adaptive sine cosine mechanism,called SCEO.Firstly,a new update formula is conceived by incorporating an adaptive inertia weight to reach an appropriate balance between exploration and exploitation.Next,an adaptive sine cosine mechanism is embedded to boost the global exploratory capacity.Finally,the Cauchy mutation is utilized to prevent the loss of population diversity during searching.To validate the efficacy of the proposed SCEO,a comprehensive evaluation is conducted on 15 classical benchmark functions and the CEC2017 test suite.The outcomes are subsequently benchmarked against both the conventional EO,its variants,and other cutting-edge metaheuristic techniques.The comparisons reveal that the SCEO method provides significantly superior results against the standard EO and other competitors.In addition,the developed SCEO is implemented to deal with a mobile robot path planning(MRPP)task,and compared to some classical metaheuristic approaches.The analysis results demonstrate that the SCEO approach provides the best performance and is a prospective tool for MRPP.

Robot path planning in globally unknown environments based on rolling windows

In this paper, robot path planning in globally unknown environments is studied. Using the rolling optimization concept in predictive control for reference, a new strategy of path planning for a mobile robot, based on rolling windows, is proposed. The method makes full use of the real-time local environmental information detected by the robot and the on-line path planning is carried on in a rolling style. Optimization and feedback are combined in a reasonable way. The convergence of the planning algorithm is also discussed.

Adaptive pass planning and optimization for robotic welding of complex joints

Current industrial robotic welding systems can- not achieve automated solutions for multi-layer multi-pass welding of complex joints due to the presence of non- uniform and irregular welding groove geometries. This paper presents an adaptive pass planning approach for robotic welding of such complex joints. The welding groove is first segmented considering both the variation in groove dimension and the reachability of the robot welding torch. For each welding segment, the welding passes are planned to be in accordance with welding practices, viz., keeping the same number of welding passes in each layer while maintaining consistent welding parameters. An adaptive pass adjustment scheme is developed to address the discrepancies between the simulated results and the actual welding deposition after finishing a few layers of welding. Corresponding robot paths are generated and optimized to ensure minimum joint movement subject to three constraints, viz., reachability, collision-free and singularity avoidance. The proposed approach has been sim- ulated with the arc welding of a Y-type joint found typically in offshore structures.

Adaptive Cockroach Colony Optimization for Rod-Like Robot Navigation

Based on the Cockroach Swarm Optimization （CSO） algorithm, a new Cockroach Colony Optimization （CCO） algorithm is presented and applied to the Robot Path Planning （RPP） problem in this paper. In the CCO algorithm, an improved grid map is used for environment modeling, and 16-geometry and 8-geometry are introduced, respectively, in food division and cockroach search operation. Moreover, the CCO algorithm adopts a non-probabilistic search strategy, which avoids a lot of invalid searches. Furthermore, by introducing a novel rotation scheme in the above CCO algorithm, an Adaptive Cockroach Colony Optimization （ACCO） algorithm is presented for the 2-D Rod-Like Robot Path Planning （RLRPP） problem. The simulation results show that the CCO algorithm can plan an optimal or approximately optimal collision-free path with linear time com- plexities. With the ACCO algorithm, the robot can accomplish intelligent and adaptive rotations to avoid obstacles and pass through narrow passages along the better path.

Obstacle avoidance for a hexapod robot in unknown environment

Obstacle avoidance is quite an important issue in the field of legged robotic applications, such as rescuing and detecting in complicated environment. Most related researchers focused on the legged robot’s gait generation after ssuming that obstacles have been detected and the walking path has been given. In this paper we propose and validate a novel obstacle avoidance framework for a six-legged walking robot Hexapod-III in unknown environment. Throughout the paper we highlight three themes: (1) The terrain map modeling and the obstacle detection; (2) the obstacle avoidance path planning method; (3) motion planning for the legged robot. Concretely, a novel geometric feature grid map (GFGM) is proposed to describe the terrain. Based on the GFGM, the obstacle detection algorithm is presented. Then the concepts of virtual obstacles and safe conversion pose are introduced. Virtual obstacles restrict the robot to walk on the detection terrain. A safe path based on Bezier curves, passing through safe conversion poses, is obtained by minimizing a penalty function taking into account the path length subjected to obstacle avoidance. Thirdly, motion planning for the legged robot to walk along the generated path is discussed in detail. At last, we apply the proposed framework to the Hexapod-III robot. The experimental result shows that our methodology allows the robot to walk safely without encountering with any obstacles in unknown environment.