Today, robots are finding more and more applications in areas such as industrial and agricultural production, environmental exploration, and disaster relief. To be effective in these roles, robots must be able to autonomously navigate complex and unstructured environments. Manipulative robotic arms can navigate through confined spaces and have the ability to explore diverse scenes. Therefore, the exploration of unknown environments, facilitated by the synergy between robotic arms and wheeled chassis, provides a more efficient and accurate approach. We have designed a comprehensive arm-chassis collaboration system that uses an information gain-based utility function to support exploration. By coordinating the motion of the robotic arm with the mobility of the chassis, this system efficiently performs complex exploration tasks. It enables autonomous exploration of unknown environments with reduced time and energy consumption. The code is published at: https://github.com/Southyang/Arm-Chassis.

The Object goal Navigation (ObjectNav) task requires an agent to navigate through a previously unknown domestic scenario using spatial and semantic contextual information, where the goal is specified by a semantic label (e.g., find a TV). Such a task is especially challenging as it requires formulating and understanding the complex co-occurrence relations among objects in diverse settings, which is critical for long-sequence navigational decision-making. Existing methods learn to either explicitly represent co-occurrence relationships as discrete semantic priors, or implicitly encode them from raw observations, thus can not benefit from the rich environmental semantics. In this work, we propose a novel Deep Reinforcement Learning (DRL) based ObjectNav strategy by actively imagining spatial and semantic clues outside the agent’s Field of View (FoV) and further mining Continuous Environmental Representations (CER) using self-supervised learning. Additionally, the illusion of spatial and semantic patterns allows the agent to perform Multi-Step Forward-Looking Planning (MSFLP) by considering the temporal evolution of egocentric local observations. Our approach is thoroughly evaluated and ablated in the visually realistic environments of the Matterport3D (MP3D) dataset. The experimental results reflect that our method combining CER and imagination-based MSFLP facilitates learning complicated semantic priors and navigation skills, thus achieving state-of-the-art performance on the ObjectNav task. In addition, adequate quantitative and qualitative analyses validate the excellent generalization ability and superiority of our method.

Target-Driven Semantic Navigation (TDSN) shows great potential to be applied in intelligent domestic assistants supporting humans with daily activities. Although numerous methods have been explored to achieve efficient static TDSN, socially aware TDSN in dynamic and crowded scenarios remains challenging and has not been adequately investigated. The main challenges come from the complex human–robot interaction mechanisms and the semantic relation exploitation, which requires the robot to understand the surroundings and perform foresighted behaviors. In this paper, a TDSN strategy named SemNav-HRO is proposed by considering Human–Robot–Object (HRO) ternary feature fusion. Specifically, a Deep Reinforcement Learning (DRL) based Dual-Channel Value Estimation Network (DCVEN) is first proposed by integrating multi-granularity map features and social awareness to learn crowded TDSN strategies. Meanwhile, the tricky and socially aware TDSN problem is slackened by eliminating the dependence on costly features (e.g., pedestrian speed) and introducing a pedestrian trajectory predictor. For the learning and evaluation of crowded TDSN strategies, a novel and semantic-rich simulator with complex layouts is constructed based on realistic domestic scenes, instead of employing the previous naive simulation settings. Experimental results show that our method relatively improves the navigation success rates by 12.8%∼25.5% and 14.6%∼19.2% compared to the baselines on the MP3D and Gibson datasets, respectively. Furthermore, we experimentally verify the promising generalization and interpretability of our method.

Autonomous exploration is a critical technology to realize robotic intelligence as it allows unsupervised preparation for future tasks and facilitates flexible deployment. In this paper, a novel Deep Reinforcement Learning (DRL) based autonomous exploration strategy is proposed to efficiently reduce the unknown area of the workspace and provide accurate 2D map construction for mobile robots. Different from existing human-designed exploration techniques that usually make strong assumptions about the scenarios and the tasks, we utilize a model-free method to directly learn an exploration strategy through trial-and-error interactions with complex environments. To be specific, the Generalized Voronoi Diagram (GVD) is first utilized for domain conversion to obtain a high-dimensional Topological Environmental Representation (TER). Then, the Generalized Voronoi Networks (GVN) with spatial awareness and episodic memory is designed to learn autonomous exploration policies interactively online. For complete and efficient exploration, Invalid Action Masking (IAM) is employed to reshape the configuration space of exploration tasks to cope with the explosion of action space and observation space caused by the expansion of the exploration range. Furthermore, a well-designed reward function is leveraged to guide the learning of policies. Extensive baseline tests and comparative simulations show that our strategy outperforms the state-of-the-art strategies in terms of map quality and exploration speed. Sufficient ablation studies and mobile robot experiments demonstrate the effectiveness and superiority of our strategy.

Navigating safely and efficiently in complex and crowded scenarios is a challenging problem of practical significance. A realistic and cluttered environmental layout usually significantly impacts crowd distribution and robotic motion decision-making during crowded navigation. However, previous methods almost either learn and evaluate navigation strategies in unrealistic barrier-free settings or assume that expensive features like pedestrian speed are available. Although accurately measuring pedestrian speed in large-scale scenarios is itself a difficult problem. To fully investigate the impact of static environment layouts on crowded navigation and alleviate the reliance of robots on costly features, we propose a novel crowded navigation framework with H uman- E nvironment- Ro bot (HERO) ternary fusion named CrowdNav-HERO. Specifically, (i) a simulator that integrates an agent, a variable number of pedestrians, and a series of realistic environments is customized to train and evaluate crowded navigation strategies. (ii) Then, a pedestrian trajectory prediction module is introduced to eliminate the dependence of navigation strategies on pedestrian speed features. (iii) Finally, a novel crowded navigation strategy is designed by combining the pedestrian trajectory predictor and a layout feature extractor. Convincing comparative analysis and sufficient benchmark tests demonstrate the superiority of our approach in terms of success rate, collision rate, and cumulative rewards. The code is published at https://github.com/SiyiLoo/CrowdNav-HERO .

The autonomous exploration task we consider requires Unmanned Aerial Vehicles (UAVs) to actively navigate through unknown environments with the goal of fully perceiving and mapping the environments. Some existing exploration strategies suffer from rough cost budgets, ambiguous Information Gain (IG), and unnecessary backtracking exploration caused by Fragmented Regions (FRs). In our work, a hierarchical spatio-temporal-aware exploration framework is proposed to alleviate these problems. At the local exploration level, the Asymmetrical Traveling Salesman Problem (ATSP) is solved by comprehensively considering exploration time, IG, and heading consistency to avoid blindly exploring. Specifically, the exploration time is reasonably budgeted by fast marching in an artificial potential field. Meanwhile, a transformer-based map occupancy predictor is designed to assist in IG calculation by imagining spatial clues out of the Field of View (FoV), facilitating the prescient exploration. We verify that our local exploration is effective in alleviating the unnecessary back-and-forth movements caused by FRs and the interference of potential obstacle occlusion on the IG calculation. At the global exploration level, the classical Next Best View Points (NBVP) are generalized to Next Best Sub-Regions (NBSR) to choose informative sub-regions for further forward-looking exploration based on a well-designed utility function. Safe flight paths and dynamically feasible trajectories are reasonably generated throughout the exploration process by fast marching and B-spline curve optimization. Comparative simulations and benchmark tests demonstrate that our proposed exploration strategy is quite competitive in terms of exploration path length, total exploration time, and exploration ratio.

Efficient autonomous exploration in unknown environments is a challenging and basic problem in the field of robotics. Due to the lack of priori information of the environment, the robot cannot clearly select the region with high information gain for exploration. In addition, the existing greedy exploration strategy leads to repeated back-and-forth movements of the robot and inefficient exploration. Intelligent bodies like humans often rely on past experience to infer the structural characteristics of the environment which are used to assist exploration. To give the robot spatial awareness, we propose a method of predicting map occupancy by using Generative Adversarial Networks (GANs). The adversarial loss and the feature extraction loss used in the training of GANs are introduced in detail. Further, we propose a novel utility function for the evaluation of exploration goals based on path length and structural characteristics of the environment. The utility function is used to guide the robot to efficiently explore step by step. We also propose a space-heuristic path planning method named CI-RRT* for robot navigation. We demonstrate the superiority of the proposed methods through comparison and ablation experiments in simulation environments. The experimental results prove that our method is superior to the existing methods.