Safe Motion Planning Based on a New Encoding Technique for Tree Expansion Using Particle Swarm Optimization

Abstract

SUMMARY Robots are now among us and even though they compete with human beings in terms of performance and efficiency, they still fail to meet the challenge of performing a task optimally while providing strict motion safety guarantees. It is therefore necessary that the future generation of robots evolves in this direction. Generally, in robotics state-of-the-art approaches, the trajectory optimization and the motion safety issues have been addressed separately. An important contribution of this paper is to propose a motion planning method intended to simultaneously solve these two problems in a formal way. This motion planner is dubbed PassPMP-PSO. It is based on a periodic process that interleaves planning and execution for a regular update of the environment’s information. At each cycle, PassPMP-PSO computes a safe near-optimal partial trajectory using a new tree encoding technique based on particle swarm optimization (PSO). The performances of the proposed approach are firstly highlighted in simulation environments in the presence of moving objects that travel at high speed with arbitrary trajectories, while dealing with sensors field-of-view limits and occlusions. The PassPMP-PSO algorithm is tested for different tree expansions going from 13 to more than 200 nodes. The results show that for a population between 20 and 100 particles, the frequency of obtaining optimal trajectory is 100% with a rapid convergence of the algorithm to this solution. Furthermore, an experiment-based comparison demonstrates the performances of PassPMP-PSO over two other motion planning methods (the PassPMP, a previous variant of PassPMP-PSO, and the input space sampling). Finally, PassPMP-PSO algorithm is assessed through experimental tests performed on a real robotic platform using robot operating system in order to confirm simulation results and to prove its efficiency in real experiments.

NDT-PSO, a New NDT based SLAM Approach using Particle Swarm Optimization

“…[29]. Furthermore, from an optimization perspective, PSO method is known for its fast convergence and its ability to find a global optimum [30], which justifies the above results and the effectiveness of NDT-PSO method.…”

Bioinspired Perception and Navigation of Service Robots in Indoor Environments: A Review

“…Bouraine and Azouaoui [ 78 ] demonstrated a tree expansion algorithm based on particle swarm optimization (PSO) dubbed PASSPMP-PSO, which supported objects moving at high speed with an arbitrary path and dealt with sensors’ field-of-view limitations. It was based on the execution of regular updates of the environment and a periodic process that interleaved planning.…”

Collision-Free Motion Planning for an Aligned Multiple-turret System Operating in Extreme Environment

“…After obtaining the C-Space, the rest is related to path planning algorithms which can be A* [34,35], many variants of rapidly exploring random tree (RRT) [36], probabilistic roadmap (PRM) [37], particle swarm optimization (PSO) [38], and so forth. Apart from these, some papers published recently have also provided guidance [39,40,41,42,43]. In this study, A* path planning algorithm will be used.…”