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SLAM (Simultaneous Localization and Mapping) is the core technology enabling autonomous exploration by mobile robots in unknown environments. While there have been numerous impressive SLAM systems developed, many of them are primarily based on the assumption of static environments, limiting their applicability in real-world settings. In order to enhance the robustness and accuracy of systems in dynamic real-world scenarios, we have introduced a parallelized real-time SLAM system called PR-SLAM, building upon the foundation of ORB-SLAM3. This algorithm introduces a dynamic probability update strategy within the semantic segmentation thread, completely decoupling the semantic segmentation thread from the tracking thread. Theoretically, the processing time per frame is solely dependent on the runtime of the tracking thread. Furthermore, we employ a geometric approach based on reprojection error to compensate for semantic gaps generated during semantic segmentation model inference. We have also designed a semantic optimization thread based on the dynamic probability of map points to optimize camera poses during semantic gaps. Finally, to reduce semantic gaps, we have performed lightweight modifications to SOLOV2. Comparative experiments were conducted against the state-of-the-art SLAM systems using the TUM dataset. The results indicate significant improvements in both accuracy and real-time performance for PR-SLAM. When compared to ORB-SLAM3, PR-SLAM achieved a remarkable 97.83% improvement in absolute trajectory accuracy and demonstrated an impressive 86.71% increase in runtime speed compared to DynaSLAM.

In this paper, we present a novel 3D autonomous exploration planner called the Autonomous Semantic Exploration Planner (ASEP), designed for GPS-denied indoor environments. ASEP combines real-time mapping, exploration, navigation, object detection, and object labeling onboard an Unmanned Aerial Vehicle (UAV) with limited resources. The planner is based on a frontier exploration strategy that utilizes semantic information about the environment in the exploration policy. The policy is extended to incorporate both geometric and semantic information provided by a deep convolutional neural network (DCNN) for semantic segmentation. This semantically-enhanced exploration algorithm directs the exploration toward the quick labeling of all objects of interest in the environment. An extended path planning algorithm continuously checks for path validity, enabling safe navigation in challenging environments. The overall system is designed to be modular and easily extended or replaced with custom modules. The proposed planner is evaluated and analyzed in both simulation and real-world environments using a UAV. Experimental studies demonstrate the effectiveness of the ASEP strategy compared to state-of-the-art methods. Results show that the objects in the environment are explored faster and total exploration time is reduced while the computational time remains consistent regardless of the semantic segmentation processing involved.

Autonomous exploration in unknown environment has remained challenging due to unexpected collisions, stuckness and slowdowns around obstacles. This paper reports a novel approach based on Signed Distance Field (SDF), to optimize path planning algorithms and autonomous exploration strategy for safe and adaptive navigation in search and rescue missions. A quantitative criterion is established for evaluating the safety of planned trajectories. Simulation results show that the proposed SDF-A* path planner outperforms traditional methods with a 30.10% increase in path safety (i.e. average distance from robot to obstacles) and a 64.11% reduction in time consumption; The proposed text SDF-based Safe Autonomous Exploration Strategy, combined with SDF-A* path planner, outperform traditional methods, leading to significant increases (47.06%) in path safety and reductions (44.75% and 15.32%) in exploration time and path length, respectively. The viability, efficiency, and safety of the proposed methods are further validated through text real-world experiments on a text three-wheeled differential steering robot equipped with Jetson Nano and RPLIDAR-A3 lidar. Results show that the proposed approach adapts to different indoor environments and map configurations without prior parameter settings.

In this article, a novel informative autonomous exploration strategy with uniform sampling is proposed to efficiently reduce the unknown volume of 3-D environments and provide an accurate truncated-signed-distance-function-based reconstruction for quadrotors. Different from existing methods, the proposed method locally samples candidate viewpoints uniformly and achieves a global coverage efficiently by backtracking history nodes when there is no unmapped space near the quadrotor. Specifically, by sampling viewpoints with a fixed radius and assigning priority, the best one is selected to which a collision-free path will be planned. Then, a history list is designed to store the executed nodes, which is backtracked to find the unmapped space, resulting in a complete exploration of the environment. In addition, a hierarchical information gain is proposed to balance the space exploration efficiency and reconstruction accuracy by considering the uncertainty of the surface and volume of the unknown space. Comparative experiments in simulation show that the proposed method outperforms the sampling-based state-of-the-art method in terms of both the exploration time and reconstruction accuracy. Moreover, the effectiveness of the proposed approach is further evaluated in a real-world experiment. [ABSTRACT FROM AUTHOR]

Coverage Path Planning (CPP) is an essential capability for autonomous robots operating in various critical applications such as fire fighting, and inspection. Performing autonomous coverage using a single robot system consumes time and energy. In particular, 3D large structures might contain some complex and occluded areas that shall be scanned rapidly in certain application domains. In this paper, a new Hybrid Coverage Path Planning (HCPP) approach is proposed to explore and cover unknown 3D large structures using a decentralized multi-robot system. The HCPP approach combines a guided Next Best View (NBV) approach with a developed Long Short Term Memory (LSTM) waypoint prediction approach to decrease the CPP exploration time at each iteration and simultaneously achieve high coverage. The hybrid approach is the new ML paradigm which fosters intelligence by balancing between data efficiency and generality allowing the exchange of some CPP parts with a learned model. The HCPP uses a stateful LSTM network architecture which is trained based on collected paths that cover different 3D structures to predict the next viewpoint. This architecture captures the dynamic dependencies of adjacent viewpoints in the long term sequences like the coverage paths. The HCPP switches between these methods triggered by either the number of iterations or an entropy threshold. In the decentralized multi-robot system, the proposed HCPP is embedded in each robot where each one of them shares its global 3D map ensuring robustness. The results performed in a realistic Gazebo robotic simulator confirmed the advantage of the proposed HCPP approach by achieving high coverage on different 3D unknown structures in a shorter time compared to conventional NBV.

To shorten the scanning time by focusing on the local scanning for such specimens as cells, this article proposes an advanced scanning strategy based on autonomous exploration, so as to detect the specimen in real time and achieve fast imaging for an atomic force microscopy. More specifically, fast raster scanning is first performed to locate the initial boundary point of the specimen. On this basis, a boundary tracking algorithm is proposed to construct the internal boundary of the specimen online through the autonomous exploration of the probe. Afterward, the boundary is expanded according to the internal boundary tracking direction, based on which a convex hull of the specimen is further constructed for the local scanning. Furthermore, the local slow scanning is performed for the specimen according to the generated scanning trajectory. Subsequently, unscanned areas in the sample can also be scanned by this autonomous method. Experimental results verify that the proposed method can achieve fast scanning while ensuring high-quality imaging. [ABSTRACT FROM AUTHOR]

Autonomous exploration is an important application of multi-vehicle systems, where a team of networked robots are coordinated to explore an unknown environment collaboratively. This technique has earned significant research interest due to its usefulness in search and rescue, fault detection and monitoring, localization and mapping, etc. In this paper, a novel cooperative exploration strategy is proposed for multiple mobile robots, which reduces the overall task completion time and energy costs compared to conventional methods. To efficiently navigate the networked robots during the collaborative tasks, a hierarchical control architecture is designed which contains a high-level decision making layer and a low-level target tracking layer. The proposed cooperative exploration approach is developed using dynamic Voronoi partitions, which minimizes duplicated exploration areas by assigning different target locations to individual robots. To deal with sudden obstacles in the unknown environment, an integrated deep reinforcement learning based collision avoidance algorithm is then proposed, which enables the control policy to learn from human demonstration data and thus improve the learning speed and performance. Finally, simulation and experimental results are provided to demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Autonomous robotic exploration of an unknown environment is a key technology for robot intelligence. In order to improve the efficiency of it, we propose a strategy based on frontier point optimization and multistep path planning in this paper. In the frontier points' optimization section, we present a random frontier points' optimization (RFPO) algorithm to select the frontier point with the highest evaluation value as the target frontier point. The evaluation function of frontier points is defined by considering information gain, navigation cost, and the precision of the localization of the robots. In the path planning section, we propose a multistep exploration strategy. Instead of planning the global path from the current position of the robot to the target frontier point directly, we set a local exploration path step size. When the robot's movement distance reaches the local exploration path step size, we reselect the current optimal frontier point for path planning to reduce the possibility that the robot may take some repetitive paths. Finally, the relevant experiments are carried out to verify the effectiveness of this strategy.

Autonomous 3-D exploration with unmanned aerial vehicles (UAVs) is increasingly prevalent for environment monitoring without human intervention. In this article, we present a systematic solution toward efficient UAV exploration in 3-D environments. Innovatively, a road map is incrementally built and maintained along with the exploration process, which explicitly exhibits the topological structure of the 3-D environment. By simplifying the environment, the road map can efficiently provide the information gain and the cost-to-go for a candidate region to be explored, which are two quantities for next-best-view (NBV) evaluation, thus prompting the efficiency for NBV determination. In addition, with reference to the global plan queried on the road map, we propose a local planner based on the potential field method that drives the robot to the information-rich area during the navigation process, which further improves the exploration efficiency. The proposed framework and its composed modules are verified in various 3-D environments, which exhibit their distinctive features in NBV selection and better performance in improving the exploration efficiency than other methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel path planning framework for autonomous exploration in unknown environments using a mobile robot. A graph structure is incrementally constructed along with the exploration process. The structure is the road map that represents the topology of the explored environment. To construct the road map, we design a sampling strategy to get random points in the explored environment uniformly. A global path from the current location of the robot to the target area can be found on this road map efficiently. We utilize a lazy collision checking method that only checks the feasibility of the generated global path to improve the planning efficiency. The feasible global path is further optimized with our proposed trajectory optimization method considering the motion constraints of the robot. This mechanism can facilitate the path cost evaluation for the next best view selection. In order to select the next best target region, we propose a utility function that takes into account both the path cost and the information gain of a candidate target region. Moreover, we present a target reselection mechanism to evaluate the target region and reduce the extra path cost. The efficiency and effectiveness of our approach are demonstrated using a mobile robot in both simulation and real experimental studies. Note to Practitioners—This paper is motivated by the efficient exploration problem, which plays a key role in various areas such as the information gathering and environment monitoring. In these applications, the mission length and executing time are often restricted by the battery capacity of the robot. In this paper, an efficient path planning framework is introduced to reduce the path length and exploration time. The robot keeps a road map of the environment to facilitate the path planning. The proposed target selection mechanism helps the robot determine the next best target to explore. Furthermore, the proposed trajectory optimization algorithm helps in reducing the path cost. Overall, this framework enables efficiently and autonomously exploration with a novel path planning framework. [ABSTRACT FROM AUTHOR]

In this paper, a novel 2-D laser-based autonomous exploration approach for mobile robots is proposed, which is based on a novel polygon map construction approach and graph-based simultaneous localization and mapping (SLAM) with directional endpoint features. This approach is composed of three modules: graph-based SLAM using directional endpoint features, polygon map construction, and exploration. Different from existing approaches in the field of 2-D SLAM, the newly proposed 2-D graph-SLAM is based on 3-D “directional endpoint” features; on this basis, a well-known data structure “circular-doubly linked list” is applied to construct a novel polygon map for navigation. Note that it is efficient for circular-doubly linked list to initialize and update the polygon map. In addition, we propose a new information entropy calculation approach to quantify the entropy of the polygon map. Then for each candidate goal, we could obtain corresponding information gain and make next decision through collision detection. Comparative experimental results with respect to the well-known Gmapping and Karto SLAM are presented to show superior performance of the proposed graph-based SLAM. The autonomous exploration experiments in the office and hallway environments show the effectiveness of the proposed approach for robotic mapping and exploration tasks. Note to Practitioners—This paper is motivated by the challenges of autonomous exploration for mobile robots. We suggest a novel autonomous exploration approach through directional endpoint feature-based graph simultaneous localization and mapping (SLAM) and polygon map construction. This newly proposed approach does not require any artificial landmark or underlying occupancy grid map, which is easy to implement. The experiments are carried out in the office and hallway environments, including graph-based SLAM and autonomous exploration for mobile robots. The experimental results show the effectiveness of the proposed autonomous exploration framework. In the future research, we will address autonomous exploration in more challenging dynamic environments. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel integrated vehicular system using collaborative unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) for autonomous exploration, mapping, and navigation in GPS-denied 3-dimensional (3-D) unknown environments. The system implements a novel two-layered exploration strategy to decompose the perception task into a coarse exploration layer and a fine mapping layer. The coarse exploration makes use of a UGV to carry out fast autonomous exploration and active 2.5D simultaneous localization and mapping (SLAM) to generate a coarse environment model, which serves as a navigation reference for subsequent complementary 3-D fine mapping conducted by a UAV. The two layers share a novel optimized exploration path planning and navigation framework, which provides optimal exploration paths and integrates the collaborative exploration and mapping efforts through an OctoMap-based volumetric motion planning interface. The proposed system provides an efficient pipeline of fast environment perception taking advantages of the agility of the UAVs as well as the powerful computation resource aboard UGVs, also allowing assistive local perception with augmented object information when necessary. The effectiveness of our system is verified by both simulations and experiments, which demonstrate its capability of implementing heterogeneous UAV and UGV collaborative exploration and structural reconstruction of the environments through active SLAM, providing optimized perception for navigation tasks. [ABSTRACT FROM AUTHOR]

Inspecting surface conditions in 3-D environments such as steel bridges is a complex, time-consuming, and often hazardous undertaking that is an essential part of tasks such as bridge maintenance. Developing an autonomous exploration strategy for a mobile climbing robot would allow for such tasks to be completed more quickly and more safely than is possible with human inspectors. The exploration strategy tested in this paper, called the nearest neighbors exploration approach (NNEA), aims to reduce the overall exploration time by reducing the number of sensor position evaluations that need to be performed. NNEA achieves this by first considering at each time step only a small set of poses near to the current robot as candidates for the next best view. This approach is compared with another exploration strategy for similar robots performing the same task. The improvements between the new and previous strategy are demonstrated through trials on a test rig, and also in field trials on a ferromagnetic bridge structure. [ABSTRACT FROM AUTHOR]

The mechanical design of a novel robotic module for a self-reconfigurable modular robotic system is presented in this paper. The robotic module, named Scout robot, was designed to serve both as a fully sensorized autonomous miniaturized robot for exploration in unstructured environments and as a module of a larger robotic organism. The Scout robot has a quasi-cubic shape of 105 mm × 105 mm × 123.5 mm, and weighs less than 1 kg. It is provided with tracks for 2-D locomotion and with two rotational DoFs for reconfiguration and macrolocomotion when assembled in a modular structure. A laser sensor was incorporated to measure the distance and relative angle to an object, and image-guided locomotion was successfully demonstrated. In addition, five Scout robot prototypes were fabricated, and multimodal locomotion of assembled robots was demonstrated. [ABSTRACT FROM PUBLISHER]

World oil demand and advanced oil recovery techniques have made the rehabilitation of previously abandoned oil wells economically attractive. This requires relatively fast mapping of the shape and location of the down-hole well structures. Practical factors prohibit the use of visual and other range sensors in this situation. Here, the feasibility of using a robotic manipulator for tactile mapping is studied. A method is developed that requires only robot joint encoders and avoids the use of any force or tactile sensors, which are complex and unreliable in such a hostile environment. This paper addresses the general problem of intelligent tactile exploration of constrained internal geometries when time is critical. It is assumed that the time required to move a manipulator to acquire a new touch point outweighs computational time. This approach models the down-hole structures with geometric primitives and focuses on exploration efficiency by intelligently searching for new touch points to build the geometric models. The algorithms developed here are shown in simulations and laboratory experiments to substantially reduce the data acquisition effort for exploration with a tactile manipulator. [ABSTRACT FROM AUTHOR]