ODS-Bot: Mobile Robot Navigation for Outdoor Delivery Services

Abstract

Autonomous mobile robots have been used in outdoor delivery services. Delivery robots have to cope with dynamic obstacles and various environmental conditions. Although several successful technological solutions are available for indoor applications, there are still plenty of unsolved problems in outdoor environments. In this study, we concentrate on three technological challenges hindering the development of campus delivery robots. The first challenge is robust localization in various dynamic outdoor environments. Localization results obtained using lidars and Global Navigation Satellite System (GNSS) sensors show complementary advantages and disadvantages. The proposed localization strategy efficiently combines information from multiple sensors. The second challenge is safe navigation based on the detection of the traversable region. The presented terrain traversability analysis provides clear, real-time positive and negative obstacle information. The third challenge is the requirement of an effective path planning strategy to reduce the risk of collisions or deadlocks. We collected the appearance history of regional dynamic obstacles in the map. The resultant path was carefully generated to avoid crowded areas. Experimental verifications have been successfully conducted on the Korea University campus. The presented results clearly indicate that the proposed methods are essential to achieve safe and reliable navigation in dynamic, real-world environments.

Optimal Kinodynamic Trajectory Planner for Mobile Robots in an Unknown Environment Using Bézier Contours

“…Continuing with our optimization model formulation, the robot's position is represented by a Bézier curve. This automatically makes its orientation, linear and angular velocities, tangential and radial accelerations, and curvature Bézier curves because of the relationships described in equations (2). In each iteration of the algorithm, the control points of the Bézier curve for the robot's position alone are tuned while satisfying all the constraints.…”

“…Figures 6 and 7 show the robot's forward and angular velocities for each case, where the robot's initial and final velocities are satisfied, and the velocity limits are not exceeded over the entire trajectory. is for a robot to score a goal by kicking the ball into the net located at [2,8] at an angle ψ k = π 2 . The kick location is given by C k = [2,6], and all three robots are equidistant from this location.…”

“…T HE last decade has witnessed remarkable advancements in the application of autonomous mobile robots in various fields, such as the warehousing industry [1], food delivery [2], search and rescue [3], and planetary exploration [4]. Companies in the commercial sector, including established ones like Amazon and some startups [5], are actively deploying autonomous robots for delivery purposes.…”

Development of an Indoor Delivery Mobile Robot for a Multi-Floor Environment

“…In particular, mobile robots are considered an effective solution in the last mile delivery service field [7], [8], with companies like Amazon, Starship, and FedEx actively involved in research and development [9], [10]. The robots researched in this way perform tasks by moving indoor and outdoor environments for delivery services [11], [12]. Mobile robots significantly enhance efficiency in last-mile delivery by automating the delivery process, thus reducing the time required to transport packages to their final destination.…”

An embarrassingly simple approach for visual navigation of forest environments

“…In another example, a large-sized rover in simulation used 3D point cloud data from LiDAR sensors to estimate the gradient of uneven terrain, and consequently quantify the mechanical effort in traversing the terrain Lourenço et al (2020) ; Carvalho et al (2022) . Importantly, while geometry-based approaches for terrain traversability have demonstrated some success in navigating rigid terrains such as on well paved paths in structured urban environments (e.g., Bellone et al, 2017 ; Tang et al, 2019 ; Liu L. et al, 2020 ; Lee and Chung, 2021 ; Lee et al, 2022 ), they may face potential challenges on compliant terrains such as a forest floor, at a low-viewpoint, with an abundance of grass and other soft vegetation where geometry-based features are unreliable ( Haddeler et al, 2022 ). In such environments, these approaches would potentially result in incomplete elevation maps due to the limitations of the depth sensor hardware.…”