Path planning in the presence of dynamic obstacles is a challenging problem due to the added time dimension in search space. In approaches that ignore the time dimension and treat dynamic obstacles as static, frequent re-planning is unavoidable as the obstacles move, and their solutions are generally sub-optimal and can be incomplete. To achieve both optimality and completeness, it is necessary to consider the time dimension during planning. The notion of adaptive dimensionality has been successfully used in high-dimensional motion planning such as manipulation of robot arms, but has not been used in the context of path planning in dynamic environments. In this paper, we apply the idea of adaptive dimensionality to speed up path planning in dynamic environments for a robot with no assumptions on its dynamic model. Specifically, our approach considers the time dimension only in those regions of the environment where a potential collision may occur, and plans in a low-dimensional state-space elsewhere. We show that our approach is complete and is guaranteed to find a solution, if one exists, within a cost sub-optimality bound. We experimentally validate our method on the problem of 3D vehicle navigation (x, y, heading) in dynamic environments. Our results show that the presented approach achieves substantial speedups in planning time over 4D heuristic-based A*, especially when the resulting plan deviates significantly from the one suggested by the heuristic.
A common approach used for efficient path planning in dynamic environments involves modeling moving obstacles as static objects with a small window of high cost around the beginning of their projected trajectories @cite_7 @cite_17 .
@cite_7 @cite_17
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We present an efficient, anytime method for path planning in dynamic environments. Current approaches to planning in such domains either assume that the environment is static and replan when changes are observed, or assume that the dynamics of the environment are perfectly known a priori. Our approach takes into account all prior information about both the static and dynamic elements of the environment, and efficiently updates the solution when changes to either are observed. As a result, it is well suited to robotic path planning in known or unknown environments in which there are mobile objects, agents or adversaries Path planning in dynamic environments with moving obstacles is computationally complex since it requires modeling time as an additional dimension. While in other domains there are state dominance relationships that can significantly reduce the complexity of the search, in dynamic environments such relationships do not exist. This paper presents a novel state dominance relationship tailored specifically for dynamic environments, and presents a planner that uses that property to plan paths over ten times faster than without using state dominance. Vehicle navigation in dynamic environments is an important challenge, especially when the motion of the objects populating the environment is unknown. Traditional motion planning approaches are too slow to be applied in real-time to this domain, hence, new techniques are needed. Recently, iterative planning has emerged as a promising approach. Nevertheless, existing iterative methods do not provide a way to estimating the future behaviour of moving obstacles and to use the resulting estimates in trajectory computation. This paper presents an iterative planning approach that addresses these two issues. It consists of two complementary methods: 1) A motion prediction method which learns typical behaviours of objects in a given environment. 2) An iterative motion planning technique based on the concept of Velocity Obstacles. This contribution presents a novel approach for efficient online planning of topologically distinctive mobile robot trajectories. Trajectory optimization deforms an initial coarse path drafted by a global planner with respect to robot motion related objectives and constraints. The primary objective is to reach a goal state in minimal time on a collision free path that adheres to the kinematic and dynamic constraints of the mobile robot. Conventional local planners get often stuck in a local optimal trajectory as they are unable to transit across obstacles. Our approach seeks the globally optimal trajectory as it maintains and optimizes a subset of admissible candidate trajectories of distinctive topologies in parallel. In case of dynamic environments the planner switches to the current globally optimal trajectory among the candidate set. The online trajectory planning with timed elastic bands is tightly integrated with the robot motion feedback control. The comparative analysis with conventional local planners confirms the advantages of maintaining distinctive topologies to circumnavigate dynamic obstacles.