3D Field D: Improved Path Planning and Replanning in Three Dimensions

Abstract

Abstract-We present an interpolation-based planning and replanning algorithm that is able to produce direct, lowcost paths through three-dimensional environments. Our algorithm builds upon recent advances in 2D grid-based path planning and extends these techniques to 3D grids. It is often the case for robots navigating in full three-dimensional environments that moving in some directions is significantly more difficult than others (e.g. moving upwards is more expensive for most aerial vehicles). Thus, we also provide a facility to incorporate such characteristics into the planning process. Along with the derivation of the 3D interpolation function used by our planner, we present a number of results demonstrating its advantages and real-time capabilities.

A novel versatile architecture for autonomous underwater vehicle’s motion planning and task assignment

“…Methods like D* or A* algorithms have been employed for AUV optimum path generation (Carsten et al 2006;Likhachev et al2005). Another approach to solve this problem is the Fast Marching (FM) algorithm, which uses a first order numerical approximation of the nonlinear Eikonal equation.…”

Unmanned Aerial Vehicles

“…Much of the recent work for vehicle planning has focused on techniques in computational geometry using a grid [9,23,24,[27][28][29][30][31][32][33][34][35]. However, for conventional grid based planning (using a 4/8-connected or 6/26-connected neighbourhood for 2D or 3D respectively), the resultant flight trajectory has limited track angle resolution.…”

“…A number of methods have been proposed which increase the angular resolution of the search space [27,28,30,32,35]. However, [30] does not find the least cost path and [35] requires a priori cell decomposition; this is computationally infeasible given the presence of dynamic obstacles.…”

“…However, [30] does not find the least cost path and [35] requires a priori cell decomposition; this is computationally infeasible given the presence of dynamic obstacles. As well, it is not possible to use the methods described in [27,28] as the track angle is derived from a priori knowledge of path costs. This is not possible since, for en-route planning, the path cost is itself dependent on the track angle (due to wind effects).…”

Extension of Self-organization for Swarm Systems to Three Dimensions

“…경로 계획에는 A* 알고리즘, D* 알고리즘 [2], 벡터장 히 스토그램(vector field histogram) 등 다양한 기법이 이용되 어 왔으나 이번 연구에는 인공 포텐셜장(Artificial Potential Field, APF) 방법이 이용되었다 [7]. 인공 포텐셜 장 기법은 주변 환경에 따라 공간상의 각 좌표에 포텐셜을 부여하고 이를 로봇의 변위를 결정짓는 요소로 사용함으로 써 로봇이 원하는 경로로 이동하도록 하는 것이다.…”