As robots are increasingly integrated into large and dynamic environments alongside humans, there is a pressing need for efficient onboard solutions to fundamental robotic operations, such as navigation and decision-making. Existing solutions often rely on computationally intensive processes that do not scale well in larger environments, leading to long computation times. This can result in unsafe and non-adaptive behaviors, as during the planning phase the robot continues to move along an old and potentially dangerous path increasing the risks of accidents or emergency.This thesis addresses this challenge by developing human-inspired light-weight methods, that enhance robotic navigation and environment understanding.

The central framework presented in this thesis introduces a novel human-like method for navigation and environment segmentation using 2D grid maps, focusing on extracting structural-semantics, such as intersections, pathways, dead ends, and paths to unexplored areas. The framework also generates sparse topometric maps for lightweight robotic navigation by using structural-semantic information. Compared to the state-of-the-art, where map segmentation either utilizes features that are specific to some indoor environments or segments into arbitrary regions that do not convey semantically meaningful information about the environment, the semantic topometric map captures structural-semantic information, which can easily be utilized by robots in a variety of missions. The proposed framework has been validated on multiple maps of different sizes and types of environments. In comparison with the state-of-the-art topological maps generated by Voronoi-based solutions, the proposed framework shows a significant reduction in complexity and computation times required in solving navigation problems. 

The utility of structural semantics is demonstrated through a novel autonomous exploration strategy that integrates structural-semantic information with conventional metric data for goal/frontier selection and employs the semantic topometric map for navigating to a frontier. The effectiveness of the exploration strategy is demonstrated in real-world experiments, showcasing improved exploration speed and computational efficiency compared to frontier-based exploration methods using only metric information. 

In order to enable the methods presented in this thesis to operate over 3D maps, this thesis introduces an approach for converting 3D voxel maps into 2D occupancy maps augmented with height and slope information. Moreover, a method for converting paths generated in 2D into 3D paths is proposed. This allows for the use of structural-semantic segmentation and efficient topometric map-based navigation planning for both UAVs and UGVs. These contributions together enable lightweight and fast environment segmentation and navigation planning for a multitude of robot types, and leveraging structural-semantic information leads to a more human-like approach toward robotic navigation and environment understanding. 

In this article, we propose a body-aware local navigation strategy for asymmetric holonomic robots for collision-free navigation in narrow pathways with sharp turns. In such scenarios, a robot with non-circular or asymmetric footprint that is comparable to the dimension of the pathways collides with walls when tracking Voronoi paths or risk-aware paths. This problem is addressed in this article through a novel multi-control barrier functions (CBF) based control strategy that achieves the objective of safe collision-free maneuvering at sharp turns. The proposed method is significantly computationally light in comparison to approaches based on model predictive control and online occupancy-grid based free-space and collision detection. In the proposed approach, a minimal set of parameters that characterize a sharp turn and the robot footprint are used to define six control barrier functions that define safe and unsafe regions of operation for a robot. A quadratic programming based CBF safety filter is designed that takes a nominal goal-reaching control as input and returns a minimally-deviating output that enforces the control barrier constraints and renders the safe set forward invariant throughout the turning maneuver. The three kinematic control inputs of the holonomic robot are shared in a conflict-free manner among the six control barrier constraints. The proposed local navigation approach was thoroughly validated in multiple scenarios in a simulated environment, where a robot with asymmetric footprint achieves collision-free maneuvering along multiple sharp turns, while respecting the safety and actuation constraints.

As the routine operations are starting to become highly automated, it is crucial to develop autonomous solutions that are infrastructure independent. Achieving this is challenging due to the ever-changing landscape of mines, which complicates infrastructure development. In response, this paper introduces a robust framework employing drones to gather data from hard-to-access areas in mines and deliver the data back to the base station for routine monitoring purposes. These tasks include gathering data from operational vehicles (mine trucks, loaders etc.), as well as various sensors (e.g. monitoring rock bolts) and relaying the data to the mine’s base station for monitoring purposes. The proposed framework is based on autonomous navigation using a known point cloud map of the mine, proximity detection via Ultra WideBand (UWB) radios and the data transfer is accomplished through the IEEE 802.15.4 communication standard, operating in the 868 MHz ISM band, with the aim to guarantee long range operation. On the mission level, the drones act as data mules capable of autonomously extracting data from operating vehicles, storing the data onboard and eventually delivering the data to the base station, which is enabled through a Point and Click (PAC) autonomy framework based on global planning, reactive navigation, communication link and behavior management. The efficacy of this framework has been demonstrated through real-world experiments conducted at a test mine in Sweden, validating the overall architecture of the proposed solution.

This article introduces a novel approach to constructing a topometric map that allows for efficient navigation and decision-making in mobile robotics applications. The method generates the topometric map from a 2D grid-based map. The topometric map segments areas of the input map into different structural-semantic classes: intersections, pathways, dead ends, and pathways leading to unexplored areas. This method is grounded in a new technique for intersection detection that identifies the area and the openings of intersections in a semantically meaningful way. The framework introduces two levels of pre-filtering with minimal computational cost to eliminate small openings and objects from the map which are unimportant in the context of high-level map segmentation and decision making. The topological map generated by GRID-FAST enables fast navigation in large-scale environments, and the structural semantics can aid in mission planning, autonomous exploration, and human-to-robot cooperation. The efficacy of the proposed method is demonstrated through validation on real maps gathered from robotic experiments: 1) a structured indoor environment, 2) an unstructured cave-like subterranean environment, and 3) a large-scale outdoor environment, which comprises pathways, buildings, and scattered objects. Additionally, the proposed framework has been compared with state-of-the-art topological mapping solutions and is able to produce a topometric and topological map with up to 92% fewer nodes than the next best solution. The method proposed in this article has been implemented in the robotics framework ROS and is open-sourced. The code is available at: https://github.com/LTU-RAI/GRID-FAST.

In this article, we introduce a novel strategy for robotic exploration in unknown environments using a semantic topometric map. As it will be presented, the semantic topometric map is generated by segmenting the grid map of the currently explored parts of the environment into regions, such as intersections, pathways, dead-ends, and unexplored frontiers, which constitute the structural semantics of an environment. The proposed exploration strategy leverages metric information of the frontier, such as distance and angle to the frontier, similar to existing frameworks, with the key difference being the additional utilization of structural semantic information, such as properties of the intersections leading to frontiers. The algorithm for generating semantic topometric mapping utilized by the proposed method is lightweight, resulting in the method’s online execution being both rapid and computationally efficient. Moreover, the proposed framework can be applied to both structured and unstructured indoor and outdoor environments, which enhances the versatility of the proposed exploration algorithm. We validate our exploration strategy and demonstrate the utility of structural semantics in exploration in two complex indoor environments by utilizing a Turtlebot3 as the robotic agent. Compared to traditional frontier-based methods, our findings indicate that the proposed approach leads to faster exploration and requires less computation time.

This article introduces a novel method for converting 3D voxel maps, commonly utilized by robots for localization and navigation, into 2D occupancy maps for both autonomous aerial vehicles (AAVs) and autonomous ground vehicles (AGVs). The generated 2D maps can be used for more efficient global navigation for both AAVs and AGVs, in enabling algorithms developed for 2D maps to be useful in 3D applications, and allowing for faster transfer of maps between multiple agents in bandwidth-limited scenarios. During the 3D to 2D map conversion, the method conducts safety checks with respect to the robot's safety margins. This ensures that an aerial or ground robot can navigate safely, relying primarily on the 2D map generated by the method. Additionally, the method extracts the height of navigable free space and a local estimate of the slope of the floor from the 3D voxel map. The height data is utilized in converting paths generated using the 2D map into paths in 3D space for both AAVs and AGVs. The slope data identifies areas too steep for a ground robot to traverse, marking them as occupied, thus enabling a more accurate representation of the terrain for ground robots. The proposed method is compared to the existing state-of-the-art fixed projection method in two different environments, over static maps and with progressively expanding maps. The methods proposed in this article have been implemented in the widely-used robotics frameworks ROS and ROS2, and are open-sourced.

This article presents a novel approach to identifying and classifying intersections for semantic and topological mapping. More specifically, the proposed novel approach has the merit of generating a semantically meaningful map containing intersections, pathways, dead ends, and pathways leading to unexplored frontiers. Furthermore, the resulting semantic map can be used to generate a sparse topological map representation, that can be utilized by robots for global navigation. The proposed solution also introduces a built-in filtering to handle noises in the environment, to remove openings in the map that the robot cannot pass, and to remove small objects to optimize and simplify the overall mapping results. The efficacy of the proposed semantic and topological mapping method is demonstrated over a map of an indoor structured environment that is built from experimental data. The proposed framework, when compared with similar state-of-the-art topological mapping solutions, is able to produce a map with up to 89% fewer nodes than the next best solution.