Applying MAPP Algorithm for Cooperative Path Finding in Urban Environments

The paper considers the problem of planning a set of non-conflict trajectories for the coalition of intelligent agents (mobile robots). Two divergent approaches, e.g. centralized and decentralized, are surveyed and analyzed. Decentralized planner - MAPP is described and applied to the task of finding trajectories for dozens UAVs performing nap-of-the-earth flight in urban environments. Results of the experimental studies provide an opportunity to claim that MAPP is a highly efficient planner for solving considered types of tasks

Exploratory Path Planning for Mobile Robots in Dynamic Environments with Ant Colony Optimization

In the path planning task for autonomous mobile robots, robots should be able to plan their trajectory to leave the start position and reach the goal, safely. There are several path planning approaches for mobile robots in the literature. Ant Colony Optimization algorithms have been investigated for this problem, giving promising results. In this paper, we propose the Max-Min Ant System for Dynamic Path Planning algorithm for the exploratory path planning task for autonomous mobile robots based on topological maps. A topological map is an environment representation whose focus is the main reference points of the environment and their connections. Based on this representation, the path can be composed by a sequence of state/actions pairs, which facilitates the navigability of the path, with no need to have the information of the complete map. The proposed algorithm was evaluated in static and dynamic envi- ronments, showing promising results in both of them. Experiments in dynamic environments show the adaptability of our proposal

School Logo Cleveland State University Logo Title Evolutionary Optimization for Safe Navigation of an Autonomous Robot in Cluttered Dynamic Unknown Environments

We present a path planning approach based on probabilistic methods for a robot to navigate in a cluttered, dynamic, unknown environment. There are dynamic obstacles moving around and static obstacles located in the map. The robot does not have any prior information about them but should be able to navigate through the map beginning from a known starting point and safely ending at a known target point. The only information the robot has is the location of the starting point and the target point and it uses sensory information to collect information about its surroundings. Our method is compared to the D* Lite algorithm and results are presented. In the last section, the parameters of the robot are optimized using biogeography-based optimization (BBO). This is an efficient multivariable optimizer and it is shown that the results of optimization achieve significant improvement in robot navigation performance. In this thesis, we show that using evolutionary optimization methods like BBO can reduce the risk of collision and the navigation time by about 25% each. The resulting risk of collision indicates safe navigation by the robot which leads to the conclusion that this is a feasible method for real-world robots

Evolutionary Optimization for Safe Navigation of an Autonomous Robot in Cluttered Dynamic Unknown Environments

We present a path planning approach based on probabilistic methods for a robot to navigate in a cluttered, dynamic, unknown environment. There are dynamic obstacles moving around and static obstacles located in the map. The robot does not have any prior information about them but should be able to navigate through the map beginning from a known starting point and safely ending at a known target point. The only information the robot has is the location of the starting point and the target point and it uses sensory information to collect information about its surroundings. Our method is compared to the D* Lite algorithm and results are presented. In the last section, the parameters of the robot are optimized using biogeography-based optimization (BBO). This is an efficient multivariable optimizer and it is shown that the results of optimization achieve significant improvement in robot navigation performance. In this thesis, we show that using evolutionary optimization methods like BBO can reduce the risk of collision and the navigation time by about 25% each. The resulting risk of collision indicates safe navigation by the robot which leads to the conclusion that this is a feasible method for real-world robots

Trajectory Planning on Grids: Considering Speed Limit Constraints

Trajectory (path) planning is a well known and thoroughly studied field of automated planning. It is usually used in computer games, robotics or autonomous agent simulations. Grids are often used for regular discretization of continuous space. Many methods exist for trajectory (path) planning on grids, we address the well known A* algorithm and the state-of-the-art Theta* algorithm. Theta* algorithm, as opposed to A*, provides ‘any-angle‘ paths that look more realistic. In this paper, we provide an extension of both these algorithms to enable support for speed limit constraints.We experimentally evaluate and thoroughly discuss how the extensions affect the planning process showing reasonability and justification of our approach