Planning approaches to constraint‐aware navigation in dynamic environments

Abstract

Path planning is a fundamental problem in many areas, ranging from robotics and artificial intelligence to computer graphics and animation. Although there is extensive literature for computing optimal, collision-free paths, there is relatively little work that explores the satisfaction of spatial constraints between objects and agents at the global navigation layer. This paper presents a planning framework that satisfies multiple spatial constraints imposed on the path. The type of constraints specified can include staying behind a building, walking along walls, or avoiding the line of sight of patrolling agents. We introduce two hybrid environment representations that balance computational efficiency and search space density to provide a minimal, yet sufficient, discretization of the search graph for constraint-aware navigation. An extended anytime dynamic planner is used to compute constraint-aware paths, while efficiently repairing solutions to account for varying dynamic constraints or an updating world model. We demonstrate the benefits of our method on challenging navigation problems in complex environments for dynamic agents using combinations of hard and soft, attracting and repelling constraints, defined by both static obstacles and moving obstacles.Keywords path planning, spatial constraints, navigation, anytime dynamic planning, potential fields Disciplines Computer Sciences | Engineering | Graphics and Human Computer Interfaces CommentsThe preprint version title is Constraint-Aware Navigation in Dynamic Environments; it was published in its final form as Planning Approaches to Constraint-Aware Navigation in Dynamic Environments.This journal article is available at ScholarlyCommons: http://repository.upenn.edu/hms/139 Constraint-Aware Navigation in Dynamic Environments AbstractPath planning is a fundamental problem in many areas ranging from robotics and artificial intelligence to computer graphics and animation. While there is extensive literature for computing optimal, collision-free paths, there is little work that explores the satisfaction of spatial constraints between objects and agents at the global navigation layer. This paper presents a planning framework that satisfies multiple spatial constraints imposed on the path. The type of constraints specified could include staying behind a building, walking along walls, or avoiding the line of sight of patrolling agents. We introduce a hybrid environment representation that balances computational efficiency and discretization resolution, to provide a minimal, yet sufficient discretization of the search graph for constraintaware navigation. An extended anytime-dynamic planner is used to compute constraint-aware paths, while efficiently repairing solutions to account for dynamic constraints. We demonstrate the benefits of our method on challenging navigation problems in complex environments for dynamic agents using combinations of hard and soft constraints, attracting and repelling constraints, on static obstacles and moving obstacles.

Human Centric Accessibility Graph For Environment Analysis

“…Another approach defines a Navgraph using a convex hull around vertices of a 3D object projected into a two-dimensional (2D) plane [13] . In particular for path-planning, a dense graph can be created first with weights associated to locations, then construct a hybrid graph after [34,53].…”

“…To decrease this angle, Φ can incorporate additional locations, i.e., (-2,-1), which would correspond to node 3 connecting to 8 in Figure 8. An alternative approach to expanding the neighboring nodes could be to iteratively expand while some condition remains true, such as no stepping, through an adaptive highway graph [53]. Algorithm 1 and 2 describe the three-dimensional breadth-first process in general terms.…”

Evacuation Simulation Incorporating Safety Signs and Information Sharing

“…The velocity-based model [2] derived from robotics tries to find a collision-free velocities by solving a geometric optimization problem. A planning framework that satisfied multiple spatial constraints imposed on the path is presented in [11]. [20] presents a prototype of a computer simulation system that uses agent-based model to simulate an emergency environment with crowd evacuation and provides for testing of multiple disaster scenarios at virtually no cost.…”

Multi-AUV Collaborative Operation Based on Time-Varying Navigation Map and Dynamic Grid Model

“…After years of research and exploration, many control algorithms have emerged. Currently, widely used path planning algorithms include ant colony algorithms [4][5][6], bee swarm algorithms [7][8], the virtual artificial potential field method [9][10], quasi-annealing algorithms [11], Neural network algorithms [12][13][14] and particle swarm optimization [15][16][17]. However, the most commonly used task allocation strategies are artificial self-organizing neural network algorithms (SOM) [18] and tree structure algorithms [19].…”