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.

An Adaptive Rapidly-Exploring Random Tree

Sampling-based planning algorithms play an important role in high degree-of-freedom motion planning(MP)problems,in which rapidly-exploring random tree(RRT)and the faster bidirectional RRT(named RRT-Connect)algorithms have achieved good results in many planning tasks.However,sampling-based methods have the inherent defect of having difficultly in solving planning problems with narrow passages.Therefore,several algorithms have been proposed to overcome these drawbacks.As one of the improved algorithms,Rapidlyexploring random vines(RRV)can achieve better results,but it may perform worse in cluttered environments and has a certain environmental selectivity.In this paper,we present a new improved planning method based on RRT-Connect and RRV,named adaptive RRT-Connect(ARRT-Connect),which deals well with the narrow passage environments while retaining the ability of RRT algorithms to plan paths in other environments.The proposed planner is shown to be adaptable to a variety of environments and can accomplish path planning in a short time.

Partition sampling strategy for robot motion planning under uncertainty

In order to solve the sensing and motion uncertainty problem of motion planning in narrow passage environment,a partition sampling strategy based on partially observable Markov decision process(POMDP)was proposed.The method combines partition sampling strategy and can improve the success rate of the robot motion planning in the narrow passage.Firstly,the environment is divided into open area and narrow area by using a partition sampling strategy,and generates the initial trajectory of the robot with fewer sampling points.Secondly,the method can calculate a local optimal solution of the initial nominal trajectory by solving POMDP problem,and iterates an overall optimal trajectory of robot motion.The proposed method follows the general POMDP solution framework,in which the belief dynamics is approximated by an extended Kalman filter(EKF),and the value function is represented by an effective quadratic function in the belief space near the nominal trajectory.Using a belief space variant of iterative linear quadratic Gaussian(iLQG)to perform the value iteration,which results in a linear control policy over the belief space that is locally optimal around the nominal trajectory.A new nominal trajectory is generated by executing the control strategy iteration,and the process is repeated until it converges to a locally optimal solution.Finally,the robot gets the optimal trajectory to safely pass through a narrow passage.The experimental results show that the proposed method can efficiently improves the performance of motion planning under uncertainty.