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Robotic autonomous navigation in dynamic environments is a complex problem, as traditional planners may fail to take dynamic obstacles and their variables into account. The Strategy-based Dynamic Object Velocity Space (S-DOVS) planner has been proposed as a solution to navigate in such scenarios. However, it has a number of limitations, such as inability to reach a goal in a large known map, avoid convex objects, or handle trap situations. In this article, we present a modified version of the S-DOVS planner that is integrated into a full navigation stack, which includes a localization system, obstacle tracker, and novel waypoint generator. The complete system takes into account robot kinodynamic constraints and is capable of navigating through large scenarios with known map information in the presence of dynamic obstacles. Extensive simulation and ground robot experiments demonstrate the effectiveness of our system even in environments with dynamic obstacles and replanning requirements, and show that our waypoint generator outperforms other approaches in terms of success rate and time to reach the goal when combined with the S-DOVS planner. Overall, our work represents a step forward in the development of robust and reliable autonomous navigation systems for real-world scenarios. [ABSTRACT FROM AUTHOR]

Robotic autonomous navigation in dynamic environments is a complex problem, as traditional planners may fail to take dynamic obstacles and their variables into account. The Strategy-based Dynamic Object Velocity Space (S-DOVS) planner has been proposed as a solution to navigate in such scenarios. However, it has a number of limitations, such as inability to reach a goal in a large known map, avoid convex objects, or handle trap situations. In this article, we present a modified version of the S-DOVS planner that is integrated into a full navigation stack, which includes a localization system, obstacle tracker, and novel waypoint generator. The complete system takes into account robot kinodynamic constraints and is capable of navigating through large scenarios with known map information in the presence of dynamic obstacles. Extensive simulation and ground robot experiments demonstrate the effectiveness of our system even in environments with dynamic obstacles and replanning requirements, and show that our waypoint generator outperforms other approaches in terms of success rate and time to reach the goal when combined with the S-DOVS planner. Overall, our work represents a step forward in the development of robust and reliable autonomous navigation systems for real-world scenarios.

In this work, the authors propose a novel decentralised coordination scheme for autonomous ground vehicles to enable map building and path planning with a network of smart overhead cameras. Decentralised indoor smart camera mapping and hierarchical navigation supports the automatic generation of waypoint graphs for each camera in an environment and allows path planning through the environment across multiple camera fields of view, or subviews. The proposed solution utilises the growing neural gas algorithm to learn the topology of unoccupied working space in each subview for maintaining a dynamic waypoint graph on each camera. The authors' pathing solution leverages a modified version of the A* algorithm to compute paths in a decentralised and hierarchical fashion. Waypoint generation was simulated and analysed on a generated environment to ensure it is both effective and efficient, while path planning was simulated on various randomised hierarchical graphs to effectively compare the proposed Decentralised‐A* (D‐A*) algorithm against standard greedy search. The proposed method efficiently handles the cases where other robot navigation methods are otherwise weak and ineffective, while still providing avenues for further optimisation of resource overhead for both the smart camera network as well as the robots themselves. [ABSTRACT FROM AUTHOR]

Abstract This paper systematically investigates the safety implications of a new 4-dimensional air traffic management paradigm by quantitatively identifying conditions where conflict-free trajectories can be guaranteed by only using 4-dimensional waypoints. Towards this end, a concept called Trajectory Inclusion is first introduced based on geometry and physics-driven proofs and analysis. Strategies to achieve conflict-free trajectories are then developed and further explained with a leader–follower example. It is found out that the time-based waypoints alone can guarantee conflict-free trajectories if certain initial conditions are satisfied. In general, the results of this paper enhance the understanding of time-based management capabilities, and help formulate better time-based instructions to reduce unnecessary tactical maneuvers and improve the overall performance of the airspace, one of the main promises Trajectory Based Operations (TBO). Specifically, the algorithmic scheme created for the leader–follower example can be applied directly to Flight-deck Interval Management, a future concept of TBO. [ABSTRACT FROM AUTHOR]

In domestic robotics, passing through narrow areas becomes critical for safe and effective robot navigation. Due to factors like sensor noise or miscalibration, even if the free space is sufficient for the robot to pass through, it may not see enough clearance to navigate, hence limiting its operational space. An approach to facing this is to insert waypoints strategically placed within the problematic areas in the map, which are considered by the robot planner when generating a trajectory and help to successfully traverse them. This is typically carried out by a human operator either by relying on their experience or by trial-and-error. In this paper, we present an automatic procedure to perform this task that: (i) detects problematic areas in the map and (ii) generates a set of auxiliary navigation waypoints from which more suitable trajectories can be generated by the robot planner. Our proposal, fully compatible with the robotic operating system (ROS), has been successfully applied to robots deployed in different houses within the H2020 MoveCare project. Moreover, we have performed extensive simulations with four state-of-the-art robots operating within real maps. The results reveal significant improvements in the number of successful navigations for the evaluated scenarios, demonstrating its efficacy in realistic situations.

The airborne launch vehicle, studied in this article, has to find an appropriate route to reach the mission goal, considering environment requirements and system constraints, for mission safety and efficiency. A method for three-dimensional waypoint generation based on an improved version of the A* algorithm with avoidance of detected obstacles is presented in this article. As the mission proceeds, the information about the environment is regularly updated. This information is considered in the mission plan, yielding a revised sequence of waypoints, to reach one or multiple goal points, depending on their order of priority. The output of the algorithm is twofold: a dynamic waypoint generation and a shortest route refined regularly. Diverse obstacles such as turbulence zones, no-fly zones, storms, etc., are considered in the flight plan as soon as they are detected. Their locations and shapes are introduced into the path search space. The improved A* capabilities are tested via simulation in different scenarios. [ABSTRACT FROM AUTHOR]

In domestic robotics, passing through narrow areas becomes critical for safe and effective robot navigation. Due to factors like sensor noise or miscalibration, even if the free space is sufficient for the robot to pass through, it may not see enough clearance to navigate, hence limiting its operational space. An approach to facing this is to insert waypoints strategically placed within the problematic areas in the map, which are considered by the robot planner when generating a trajectory and help to successfully traverse them. This is typically carried out by a human operator either by relying on their experience or by trial-and-error. In this paper, we present an automatic procedure to perform this task that: (i) detects problematic areas in the map and (ii) generates a set of auxiliary navigation waypoints from which more suitable trajectories can be generated by the robot planner. Our proposal, fully compatible with the robotic operating system (ROS), has been successfully applied to robots deployed in different houses within the H2020 MoveCare project. Moreover, we have performed extensive simulations with four state-of-the-art robots operating within real maps. The results reveal significant improvements in the number of successful navigations for the evaluated scenarios, demonstrating its efficacy in realistic situations. [ABSTRACT FROM AUTHOR]

In domestic robotics, passing through narrow areas becomes critical for safe and effective robot navigation. Due to factors like sensor noise or miscalibration, even if the free space is sufficient for the robot to pass through, it may not see enough clearance to navigate, hence limiting its operational space. An approach to facing this is to insert waypoints strategically placed within the problematic areas in the map, which are considered by the robot planner when generating a trajectory and help to successfully traverse them. This is typically carried out by a human operator either by relying on their experience or by trial-and-error. In this paper, we present an automatic procedure to perform this task that: (i) detects problematic areas in the map and (ii) generates a set of auxiliary navigation waypoints from which more suitable trajectories can be generated by the robot planner. Our proposal, fully compatible with the robotic operating system (ROS), has been successfully applied to robots deployed in different houses within the H2020 MoveCare project. Moreover, we have performed extensive simulations with four state-of-the-art robots operating within real maps. The results reveal significant improvements in the number of successful navigations for the evaluated scenarios, demonstrating its efficacy in realistic situations.