Deterministic hierarchical task network (HTN) planning assumes that planning evolves along a fully predictable path and neglects the quality of the plan in the partially observable environment. To bridge this research gap, this paper proposes an inno...
This paper focuses on motion planning for mobile manipulators, which includes planning for both the mobile base and the manipulator. A hierarchical motion planner is proposed that allows the manipulator to change its configuration autonomously in rea...
Viewpoint planning is crucial to ensure both inspection efficiency and observation precision in UAV inspection tasks. To address the issues of excessive waypoints and inadequate observation precision in traditional methods, this paper proposes a hier...
Multi-robot systems have been used in many fields by utilizing parallel working robots to perform missions by allocating tasks and cooperating. For task planning, multi-robot systems need to solve complex problems that simultaneously consider the mov...
Hierarchical Reinforcement Learning (HRL) for UAV Coverage Path Planning (CPP) is hindered by the “subgoal space explosion”, causing inefficient exploration. To address this, we propose a two-stage framework, Hierarchical Reinforcement Le...
Herein, to address the challenges faced by Automatic Guided Vehicles (AGVs) in construction site environments, including heavy vehicle loads, extensive road search areas, and randomly distributed obstacles, this paper presents a hierarchical trajecto...
Path planning for robots based on reinforcement learning encounters challenges in integrating semantic information about environments into the training process. In unseen or complex environmental information, agents often perform sub-optimally and re...
This study proposed the 3D path planning of an autonomous underwater vehicle (AUV) by using the hierarchical deep Q network (HDQN) combined with the prioritized experience replay. The path planning task was divided into three layers, which realized t...
Autonomous exploration in unknown environments remains a challenging problem for UAVs. This paper proposes a hierarchical exploration planning framework that explicitly leverages real-time acquired prior knowledge to improve exploration efficiency. T...
Efficient route planning technology is the core support for ensuring the successful execution of unmanned aerial vehicle (UAV) flight missions. In this paper, the coordination issue of global route planning and local real-time obstacle avoidance in c...
This paper presents a compute-efficient human-following robotic mobility system that integrates multimodal target recognition, LiDAR-based obstacle perception, hierarchical local planning, and low-level motion control into a unified perception–...
Aiming to improve on the poor smoothness and longer paths generated by the traditional Hybrid A-star algorithm in unstructured environments with multiple obstacles, especially in confined areas for autonomous vehicles, a Hybrid A-star path planning m...
Unmanned aerial vehicles (UAVs) received an unprecedented surge of people’s interest worldwide in recent years. This paper investigates the specific problem of cooperative mission planning for multiple UAVs on the battlefield from a hierarchica...
Trajectory planning is crucial for solar aircraft endurance. The multi-wing morphing solar aircraft can enhance solar energy acquisition through wing deflection, which simultaneously incurs aerodynamic losses, complicating energy coupling and challen...
Unmanned Aerial Vehicles (UAVs) are gaining preference for mapping and monitoring ground activities, partially due to the cost efficiency and availability of lightweight high-resolution imaging sensors. Recent advances in solar-powered High Altitude...
Path planning for unmanned surface vehicles in dynamic waters requires coordination of global routing, collision avoidance, maneuverability, and maritime encounter rules. This study develops a rule-constrained hierarchical framework to integrate thes...
Outdoor mobile spraying robots are increasingly being used for precision plant protection and vector-borne disease control. Coverage planning in unstructured environments remains difficult because spray models are computationally costly, chassis moti...
The global path planning of planetary surface rovers is crucial for optimizing exploration benefits and system safety. For the cases of long-range roving or obstacle constraints that are time-varied, there is an urgent need to improve the computation...
Heterogeneous airground robotic systems, which integrate unmanned ground vehicles and unmanned aerial vehicles, have shown significant potential in complex autonomous missions. However, when deployed in urban canyons, dense high-rise buildings impose...
Urban low-altitude UAV missions require efficient, risk-aware path planning and rapid response to dynamic airspace changes. This study proposes a hierarchical planning and dynamic replanning framework based on the Geographic coordinate Subdivision gr...
Space adaptability between mining equipment and coal-rock mass, to ensure the machines cut in a coal seam, is an importance technique in longwall mining automation. In order to guide the mining equipment cutting in the coal seam, a cutting path plann...
Post-disaster Search and Rescue (SAR) missions demand rapid coordination of Heterogeneous Unmanned Aerial Vehicle (UAV) fleets under stringent payload and flight range limitations. Traditional heuristic solvers struggle to solve the Large-Scale Heter...
Because the average number of missing people in our country is more than 20,000 per year, determining how to efficiently locate missing people is important. The traditional method of finding missing people involves deploying fixed cameras in some hot...
Autonomous quadrotor landing on moving ground platforms is essential for cooperative air–ground operations in confined warehouse environments. However, existing trajectory planners often compromise corridor clearance in narrow aisles or fail to...
Electromobility is central to urban decarbonisation, but its charging infrastructure must be sized under substantial uncertainty about user behaviour that varies across stations, time of day and user type. This study couples a hierarchical Bayesian m...
In this paper, a novel path planning algorithm with Reinforcement Learning is proposed based on the topological map. The proposed algorithm has a two-level structure. At the first level, the proposed method generates the topological area using the re...
Existing autonomous navigation systems for Unmanned Aerial Vehicles (UAVs) face the dual challenges of local minima entrapment and computational complexity that scales with environmental density. This paper proposes a hierarchical navigation architec...
The Monte Carlo Tree Search (MCTS) has demonstrated excellent performance in solving many planning problems. However, the state space and the branching factors are huge, and the planning horizon is long in many practical applications, especially in t...
In this paper, a hierarchical reinforcement learning (HRL) architecture, namely a “Hierarchical Deep Deterministic Policy Gradient (HDDPG)” has been proposed and studied. A HDDPG utilizes manager and worker formation similar to other HRL...
Autonomous driving in dense traffic demands policies that ensure safety, accurate path tracking, and ride comfort, yet reinforcement learning (RL) alone suffers from low sample efficiency and weak safety guarantees, while classical artificial potenti...
A hierarchical mission planning method was proposed to solve a simultaneous attack mission planning problem for multi-unmanned aerial vehicles (UAVs). The method consisted of three phases aiming to decouple and solve the mission planning problem. In...
In this paper, the cooperative multi-task online mission planning for multiple Unmanned Aerial Vehicles (UAVs) is studied. Firstly, the dynamics of unmanned aerial vehicles and the mission planning problem are studied. Secondly, a hierarchical mechan...
An efficient, hierarchical, two-dimensional (2D) path-planning method for large complex environments is presented in this paper. For mobile robots moving in 2D environments, conventional path-planning algorithms employ single-layered maps; the propos...
In today’s competing business market, companies are constantly challenged to dynamically adapt to customer expectations by diminishing the time response that goes from the beginning of the business opportunity to the satisfaction of the custome...
The rapid development of the drone industry has facilitated the emergence of concepts such as urban air mobility (UAM), driving a wave of air logistics in urban very low-level (VLL) airspace. However, existing trajectory planning algorithms do not ad...
The application of artificial intelligence (AI) to real-time strategy (RTS) games includes considerable challenges due to the very large state spaces and branching factors, limited decision times, and dynamic adversarial environments involved. To add...
In a complex and dynamic battlefield environment, enabling autonomous underwater vehicles (AUVs) to reach dynamic targets in the shortest possible time using global autonomous planning is a key issue affecting the completion of search tasks. In this...
Aiming at effectively generating safe and reliable motion paths for quadruped robots, a hierarchical path planning approach driven by dynamic 3D point clouds is proposed in this article. The developed path planning model is essentially constituted of...
Efficient operation of robotic manipulators in constrained workspaces requires careful coordination of robot placement, motion planning, and posture optimization. This paper presents a hierarchical optimization framework that addresses these challeng...
By coupling a robot to a smart environment, the robot can sense state beyond the perception range of its onboard sensors and gain greater actuation capabilities. Nevertheless, incorporating the states and actions of Internet of Things (IoT) devices i...
Unmanned aerial vehicle (UAV) path planning is widely applied in fields such as Internet of Things (IoT) environmental monitoring, intelligent transportation, low-altitude logistics, and autonomous inspection. With the rapid development of UAV intell...
In human–robot collaborative environments, the inherent complexity of shared operational spaces imposes dual requirements on process safety and task execution efficiency. To address the limitations of conventional approaches that decouple plann...
Long-horizon mobile manipulation requires a robot to execute a sequence of heterogeneous subtasks such as navigation, picking, and articulated-object manipulation in indoor environments. Standard reinforcement learning suffers from reward sparsity an...
The rapid development of marine robotic technology in recent decades has resulted in significant improvements in the self-sufficiency of autonomous underwater vehicles (AUVs). However, simple scenario-based approaches are no longer sufficient when it...
Mass customization makes it necessary to upgrade production planning systems to improve the flexibility and resilience of production planning in response to volatile demand. The ongoing development of digital twin technologies supports the upgrade of...
This paper presents a hierarchical lane-changing trajectory planner based on the least action principle for autonomous driving. Our approach aims to achieve reliable real-time avoidance of static and moving obstacles in multi-vehicle interaction scen...
Natural-language-driven robot navigation in complex indoor environments requires the joint capability of high-fidelity scene representation, structured semantic reasoning, and executable path planning. To address this challenge, this paper proposes H...
Automatic parking in constrained environments, such as dead-end roads and narrow parallel spaces, remains a challenge due to the low success rate and poor real-time performance of conventional planning algorithms. The paper proposes an entry-point gu...
Collision-free trajectory planning in narrow spaces has become one of the most challenging tasks in automated parking scenarios. Previous optimization-based approaches can generate accurate parking trajectories, but these methods cannot compute feasi...
In this paper, a hierarchical optimization framework combining Bayesian and pseudospectral approaches is developed to solve the challenging problem of hybrid trajectory planning for energy-augmented hypersonic skip–glide vehicles that have plan...
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