Adaptive sampling for generalized probabilistic roadmaps

In this paper, an adaptive sampling strategy is presented for the generalized sampling-based motion plan- ner, generalized probabilistic roadmap （GPRM）. These planners are designed to account for stochastic map and model uncertainty and provide a feedback solution to the motion planning problem. Intelligently sampling in this framework can result in large speedups when compared to naive uniform sampling. By using the information of transition probabilities, encoded in these generalized planners, the proposed strategy biases sampling to improve the efficiency of sampling, and increase the overall success probability of GPRM. The strategy is used to solve the motion planning problem of a fully actuated point robot and a 3-DOF fixed-base manipulator on several maps of varying difficulty levels, and results show that the strategy helps solve the problem efficiently, while simultaneously increasing the success probability of the solution. Results also indicate that these rewards increase with an increase in map complexity.

A Configuration Deactivation Algorithm for Boosting Probabilistic Roadmap Planning of Robots

We present a method to improve the execution time used to build the roadmap in probabilistic roadmap planners. Our method intelligently deactivates some of the configurations during the learning phase and allows the planner to concentrate on those configurations that axe most likely going to be useful when building the roadmap. The method can be used with many of the existing sampling algorithms. We ran tests with four simulated robot problems typical in robotics literature. The sampling methods applied were purely random, using Halton numbers, Gaussian distribution, and bridge test technique. In our tests, the deactivation method clearly improved the execution times. Compared with pure random selections, the deactivation method also significantly decreased the size of the roadmap, which is a useful property to simplify roadmap planning tasks.

Multi-Branch Cable Harness Layout Design Based on Genetic Algorithm with Probabilistic Roadmap Method

Current studies on cable harness layouts have mainly focused on cable harness route planning.However,the topological structure of a cable harness is also extremely complex,and the branch structure of the cable harness can affect the route of the cable harness layout.The topological structure design of the cable harness is a key to such a layout.In this paper,a novel multi-branch cable harness layout design method is presented,which unites the probabilistic roadmap method(PRM)and the genetic algorithm.First,the engineering constraints of the cable harness layout are presented.An obstacle-based PRM used to construct non-interference and near to the surface roadmap is then described.In addition,a new genetic algorithm is proposed,and the algorithm structure of which is redesigned.In addition,the operation probability formula related to fitness is proposed to promote the efficiency of the branch structure design of the cable harness.A prototype system of a cable harness layout design was developed based on the method described in this study,and the method is applied to two scenarios to verify that a quality cable harness layout can be efficiently obtained using the proposed method.In summary,the cable harness layout design method described in this study can be used to quickly design a reasonable topological structure of a cable harness and to search for the corresponding routes of such a harness.

EPL-PRM:Equipotential line sampling strategy for probabilistic roadmap planners in narrow passages

Path planning is a crucial concern in the field of mobile robotics,particularly in complex scenarios featuring narrow passages.Sampling-based planners,such as the widely utilized probabilistic roadmap(PRM),have been extensively employed in various robot applications.However,PRM’s utilization of random node sampling often results in disconnected graphs,posing a significant challenge when dealing with narrow passages.In order to tackle this issue,we present equipotential line sampling strategy for probabilistic roadmap(EPL-PRM),a novel approach derived from PRM.This paper initially proposes a sampling potential field,followed by the construction of equipotential lines that are denser in the proximity of obstacles and narrow passages.Random sampling is subsequently conducted along these lines.Consequently,the sampling strategy enhances the likelihood of sampling nodes around obstacles and narrow passages,thereby addressing the issue of sparsity encountered in traditional sampling-based planners.Furthermore,we introduce a nodal optimization method based on an artificial repulsive field,which prompts sampled nodes to move in the direction of repulsion.As a result,nodes around obstacles are distributed more uniformly,while nodes within narrow passages gravitate toward the middle of the passages.Finally,extensive simulations are conducted to evaluate the proposed method.The results demonstrate that our approach achieves path planning with superior efficiency,lower cost,and higher reliability compared with traditional algorithms.

Path Planning in Complex 3D Environments Using a Probabilistic Roadmap Method

This paper presents a 3D path planning algorithm for an unmanned aerial vehicle （UAV） in complex environments. In this algorithm, the environments are divided into voxels by octree algorithm. In order to satisfy the safety requirement of the UAV, free space is represented by free voxels, which have enough space margin for the UAV to pass through. A bounding box array is created in the whole 3D space to evaluate the free voxel connectivity. The probabilistic roadmap method （PRM） is improved by random sampling in the bounding box array to ensure a more efficient distribution of roadmap nodes in 3D space. According to the connectivity evaluation, the roadmap is used to plan a feasible path by using A＊ algorithm. Experimental results indicate that the proposed algorithm is valid in complex 3D environments.

Multi-objective robot motion planning using a particle swarm optimization model

Two new heuristic models are developed for motion planning of point robots in known environments.The first model is a combination of an improved particle swarm optimization (PSO) algorithm used as a global planner and the probabilistic roadmap (PRM) method acting as a local obstacle avoidance planner.For the PSO component,new improvements are proposed in initial particle generation,the weighting mechanism,and position-and velocity-updating processes.Moreover,two objective functions which aim to minimize the path length and oscillations,govern the robot’s movements towards its goal.The PSO and PRM components are further intertwined by incorporating the best PSO particles into the randomly generated PRM.The second model combines a genetic algorithm component with the PRM method.In this model,new specific selection,mutation,and crossover operators are designed to evolve the population of discrete particles located in continuous space.Thorough comparisons of the developed models with each other,and against the standard PRM method,show the advantages of the PSO method.