In this paper we study the problem of determining optimal paths in a dynamic, 2-D environment. We present a novel cell decomposition approach which generates optimal paths based on a number of di erent optimality criteria, including time-optimal, distance-optimal (shortest paths), and costoptimal paths based on a combination of time and distance. After mapping the dynamic, 2-D environment to a ...

Many robots operating in unpredictable environments require an online path planning algorithm that can quickly compute high quality paths. Asymptotically optimal planners are capable of finding the optimal path, but can be slow to converge. Local optimisation algorithms are capable of quickly improving a solution, but are not guaranteed to converge to the optimal solution. In this paper we deve...

We investigate path planning algorithms that are based on level set methods for applications in which the environment is static, but where an a priori map is inaccurate and the environment is sensed in real-time. Our principal contribution is not a new path planning algorithm, but rather a formal analysis of path planning algorithms based on level set methods. Computational costs when planning ...

Global path planning is considered as a fundamental problem for mobile robots. This master project is split into two parts. In the first part we investigate the capabilities of Genetic Algorithms (GA) for solving the global path planning problem in large-scale grid maps. First, we propose a GA approach for efficiently finding an (or near) optimal path in the grid map. We carefully designed GA o...

We present an approach for integrated decision making of vehicle agents in urban traffic systems. Planning process for a vehicle agent is separated into two stages: strategic planning for selection of the optimal route and tactical planning for passing the current street in the most optimal manner. Vehicle routing is considered as a stochastic shortest path problem with imperfect knowledge abou...

To find an optimal path for robots in an environment that is only partially known and continuously changing is a difficult problem. This paper presents a new method for generating a collision-free near-optimal path for an autonomous mobile robot in a dynamic environment containing moving and static obstacles using neural network and fuzzy logic with genetic algorithm. The mobile robot selects a...

This paper investigates possible methods of parallelizing two common single source path-finding algorithms. This research could enable faster computation of optimal path planning in complex environments. Both algorithms, A* and Bellman-Ford were found to be parallelizable. After testing, A* was shown to lack good scaling potential while Bellman-Ford showed promise as a scalable parallel path fi...

Because traditional obstacle avoidance path planning methods have a lot of problems, such as large amount of calculation, low efficiency, poor optimization capability, and lack of dealing with dynamic obstacles, a new method which implements real-time path planning of mobile robot is presented. The method builds a neural network model for the robot workspace, and then it uses the model to obtai...

This paper studies the impact of pattern databases for solving shortest path planning problems with limited memory. It contributes a bucket implementation of Dijkstra’s algorithm for the construction of shortest path planning pattern databases and their use in symbolic A* search. For improved efficiency, the paper analyzes the locality for weighted problem graphs and shows that it matches the d...