Hybrid A* path search with resource constraints and dynamic obstacles

Abstract

This paper considers path planning with resource constraints and dynamic obstacles for an unmanned aerial vehicle (UAV), modeled as a Dubins agent. Incorporating these complex constraints at the guidance stage expands the scope of operations of UAVs in challenging environments containing path-dependent integral constraints and time-varying obstacles. Path-dependent integral constraints, also known as resource constraints, can occur when the UAV is subject to a hazardous environment that exposes it to cumulative damage over its traversed path. The noise penalty function was selected as the resource constraint for this study, which was modeled as a path integral that exerts a path-dependent load on the UAV, stipulated to not exceed an upper bound. Weather phenomena such as storms, turbulence and ice are modeled as dynamic obstacles. In this paper, ice data from the Aviation Weather Service is employed to create training data sets for learning the dynamics of ice phenomena. Dynamic mode decomposition (DMD) is used to learn and forecast the evolution of ice conditions at flight level. This approach is presented as a computationally scalable means of propagating obstacle dynamics. The reduced order DMD representation of time-varying ice obstacles is integrated with a recently developed backtracking hybrid A∗ graph search algorithm. The backtracking mechanism allows us to determine a feasible path in a computationally scalable manner in the presence of resource constraints. Illustrative numerical results are presented to demonstrate the effectiveness of the proposed path-planning method.

Hybrid algorithms in path planning for autonomous navigation of unmanned aerial vehicle: a comprehensive review

“…These environments are realistically simulated in terms of complexity and obstacles. There are only very few studies to present the path planning algorithms for UAVs in realistic environments such as urban [33,35,51,59,74,77,78], rural [29], and forest environments [52].…”

“…When faced with resource limitations and dynamic obstacles with unknown characteristics, the combination of DMD and A * is used to plan the UAVs' route. This planning can generate a more realistic simulation of what an agent will experience on its task [35]. In addition, a hybrid optimization algorithm GSPSODE is proposed to be utilized for research on route planning for UAV inspection in urban pipeline corridors.…”

“…In the face of resource restrictions and time-varying obstacles with unknown dynamics [35], performed integration of DMD with BHA * graph search for UAV path planning. The dynamics of time-varying obstacles are learned through the use of dynamic mode decomposition, while resource restrictions are handled in a scalable way by the BHA * method.…”

“…In order to determine the shortest path to the agent's objective pose, a backtracking hybrid A * search is given the forecast location and form of dynamic obstacles. While [35] planned a route for UAV with dynamic obstructions [36], implemented UAV path planning in both static and dynamic environments. Planning a path in a static environment using classical path planning algorithms is feasible if the target and obstacle positions remain constant and the vehicle's instantaneous position is known.…”

Multi-Task Agent Hybrid Control in Sparse Maps and Complex Environmental Conditions

“…However, in real-world scenarios, most obstacles do not rise vertically from the ground; their elevation from the ground level typically increases gradually until they reach a point where they are considered "obstacles." Existing research on the A* algorithm in three-dimensional spaces is mostly applicable to drones, which have a high degree of mobility freedom [9][10][11][12] . Drones can ascend, descend, and move in any direction in space.…”