Motion planning

About: Motion planning is a research topic. Over the lifetime, 32846 publications have been published within this topic receiving 553548 citations.

A novel hybrid grey wolf optimizer algorithm for unmanned aerial vehicle (UAV) path planning

Abstract: Unmanned aerial vehicle (UAV) path planning problem is an important component of UAV mission planning system, which needs to obtain optimal route in the complicated field. To solve this problem, a novel hybrid algorithm called HSGWO-MSOS is proposed by combining simplified grey wolf optimizer (SGWO) and modified symbiotic organisms search (MSOS). In the proposed algorithm, the exploration and exploitation abilities are combined efficiently. The phase of the GWO algorithm is simplified to accelerate the convergence rate and retain the exploration ability of the population. The commensalism phase of the SOS algorithm is modified and synthesized with the GWO to improve the exploitation ability. In addition, the convergence analysis of the proposed HSGWO-MSOS algorithm is presented based on the method of linear difference equation. The cubic B-spline curve is used to smooth the generated flight route and make the planning path be suitable for the UAV. The simulation experimental results show that the HSGWO-MSOS algorithm can acquire a feasible and effective route successfully, and its performance is superior to the GWO, SOS and SA algorithm.

Revising motion planning under Linear Temporal Logic specifications in partially known workspaces

Abstract: In this paper we propose a generic framework for real-time motion planning based on model-checking and revision. The task specification is given as a Linear Temporal Logic formula over a finite abstraction of the robot motion. A preliminary motion plan is first generated based on the initial knowledge of the system model. Then real-time information obtained during the runtime is used to update the system model, verify and further revise the motion plan. The implementation and revision of the motion plan are performed in real-time. This framework can be applied to partially-known workspaces and workspaces with large uncertainties. Computer simulations are presented to demonstrate the efficiency of the framework.

Real-Time Path Planning in Dynamic Virtual Environments Using Multiagent Navigation Graphs

Abstract: We present a novel approach for efficient path planning and navigation of multiple virtual agents in complex dynamic scenes. We introduce a new data structure, Multiagent Navigation Graph (MaNG), which is constructed using first- and second-order Voronoi diagrams. The MaNG is used to perform route planning and proximity computations for each agent in real time. Moreover, we use the path information and proximity relationships for the local dynamics computation of each agent by extending a social force model [15]. We compute the MaNG using graphics hardware and present culling techniques to accelerate the computation. We also address undersampling issues and present techniques to improve the accuracy of our algorithm. Our algorithm is used for real-time multiagent planning in pursuit-evasion, terrain exploration, and crowd simulation scenarios consisting of hundreds of moving agents, each with a distinct goal.

A Bat Algorithm with Mutation for UCAV Path Planning

Abstract: Path planning for uninhabited combat air vehicle (UCAV) is a complicated high dimension optimization problem, which mainly centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments Original bat algorithm (BA) is used to solve the UCAV path planning problem Furthermore, a new bat algorithm with mutation (BAM) is proposed to solve the UCAV path planning problem, and a modification is applied to mutate between bats during the process of the new solutions updating Then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic BA The realization procedure for original BA and this improved metaheuristic approach BAM is also presented To prove the performance of this proposed metaheuristic method, BAM is compared with BA and other population-based optimization methods, such as ACO, BBO, DE, ES, GA, PBIL, PSO, and SGA The experiment shows that the proposed approach is more effective and feasible in UCAV path planning than the other models

Bayesian Optimisation for informative continuous path planning.

Abstract: Environmental monitoring with mobile robots requires solving the informative path planning problem. A key challenge is how to compute a continuous path over space and time that will allow a robot to best sample the environment for an initially unknown phenomenon. To address this problem we devise a layered Bayesian Optimisation approach that uses two Gaussian Processes, one to model the phenomenon and the other to model the quality of selected paths. By using different acquisition functions over both models we tackle the exploration-exploitation trade off in a principled manner. Our method optimises sampling over continuous paths and allows us to find trajectories that maximise the reward over the path. We test our method on a large scale experiment for modelling ozone concentration in the US, and on a mobile robot modelling the changes in luminosity. Comparisons are presented against information based criteria and point-based strategies demonstrating the benefits of our method.