No clear criteria have yet been established to guide decision-making for patient selection and the optimal timing of adaptive radiotherapy (ART) based on image-guided radiotherapy (IGRT). We have developed a novel protocol—the Best for Adaptive...
In this paper, an Adaptive Improved Cuckoo-Catfish Optimizer (AICCO) is proposed for smooth and collision-free mobile robot path planning in static and dynamic environments. The proposed AICCO enhances the original Cuckoo-Catfish Optimizer (CCO) by i...
Path planning is one of the key parts of unmanned aerial vehicle (UAV) fast autonomous flight in cluttered environments. However, it remains a challenge to efficiently generate a high-quality trajectory for flight tasks with a high success rate. In t...
Event-triggered communication reduces unnecessary transmissions in unmanned surface vehicle (USV) formations, but leaves two connected trajectory-level problems. Between leader state packets, the follower side must maintain a leader trajectory predic...
Nighttime ground-based astronomical observations are often hindered by unpredictable cloud cover, which significantly reduces observing efficiency and complicates manual schedule adjustments. We present a deep learning-based method that automatically...
Background: Proton therapy requires caution when treating patients with targets near neural structures. Intuitive and quantitative guidelines are needed to support decision-making concerning the treatment modality. This study compared dosimetric prof...
Radiotherapy, a crucial technique in cancer therapy, has traditionally relied on the premise of largely unchanging patient anatomy during the treatment course and encompassing uncertainties by target margins. This review introduces adaptive radiother...
Background/Objectives: Non-ablative stereotactic body radiation therapy (SBRT) is commonly employed for locally advanced pancreatic cancer (LAPC) using computed tomography-guided radiotherapy (CTgRT) without online adaptive radiation therapy (oART)....
Autonomous Unmanned Aerial Vehicle (UAV) navigation in unknown indoor environments is challenged by incremental map revelation and non-uniform geometric changes, which frequently invalidate preplanned trajectories. Existing time-triggered replanning...
A dosimetric study was performed to show the importance of adaptive radiotherapy (ART) for head and neck cancer (HNC) patients using volumetric modulated arc therapy (VMAT). A total of 13 patients with HNC who required replanning during radiotherapy...
Unmanned Aerial Vehicle (UAVs) have become very popular in the last decade due to some advantages such as strong terrain adaptation, low cost, zero casualties, and so on. One of the most interesting advances in this field is the automation of mission...
Dynamic events, such as new task arrivals, UAV failures, and changes in task time windows, require timely updates to task allocation plans in multi-UAV systems. Global replanning provides broad flexibility for feasibility recovery, but it may also mo...
To address trajectory tracking of cable-driven continuum robots (CDCRs) in the presence of obstacles, this paper proposes an integrated control framework that combines online trajectory replanning, obstacle avoidance, and tracking control. The contro...
In order to solve problems of poor stability and large trajectory-tracking errors when intelligent vehicles are travelling at different speeds, when working conditions that require obstacle avoidance are not taken into account in the trajectory-track...
Underwater quadrotor vehicles operating in complex nearshore environments face challenges in three-dimensional path planning, obstacle-triggered replanning, and trajectory tracking under external disturbances. To address these issues, this paper eval...
Background: Radiotherapy is essential in the management of head–neck cancer. During the course of radiotherapy, patients may develop significant anatomical changes. Re-planning with adaptive radiotherapy may ensure adequate dose coverage and sp...
Collaborative operation of heterogeneous UAV swarms in dense urban environments remains challenging because right-of-way allocation is often rigid, frequent replanning consumes considerable onboard computation, and paths obtained by purely mathematic...
Background: Cervical cancer treatment with advanced radiotherapy techniques benefits from image guidance, particularly when anatomical changes occur during therapy. This case emphasizes the need for adaptive radiotherapy when target volume shifts sig...
This paper is motivated by, but not limited to, the task of scheduling jobs organized in workflows to a computational grid. Due to the dynamic nature of grid computing, more or less permanent replanning is required so that only very limited time is a...
Efficient path planning and trajectory tracking are central to the safe and autonomous navigation of autonomous underwater vehicles (AUVs) in complex and unknown environments. In this paper, we propose an integrated framework that couples enhanced pa...
Objectives: This study aimed to assess the precision of dose delivery to the target in adaptive carbon ion radiotherapy (CIRT) for locally advanced non-small cell lung cancer (LA-NSCLC) in cumulative dosimetry. Methods: Forty-six patients who receive...
This paper proposes a task planning method that integrates deep Q-learning network (DQN) with matrix sorting for Earth-oriented static multi-target cooperative observation tasks. The approach addresses emergent satellite failures in imaging constella...
This paper presents a physiological signal-guided uncertainty-management framework that integrates real-time indicators of the operator’s supervisory state into UAV autonomy to improve obstacle avoidance and risk-aware decision-making under unc...
Dynamic drone delivery requires rules for retaining and revising released plans. We specify a closed-loop architecture separating ordinary replanning access, assignment-change penalties, and shared safety intervention, while explicitly identifying cl...
To tackle the challenges of poor stability during real-time random gait switching and precise trajectory control for hexapod robots under limited stride and steering conditions, a novel real-time replanning gait switching control strategy based on an...
Low-altitude urban environments pose significant challenges to multi-UAV trajectory planning because of dense buildings, constrained airspace, dynamic obstacles, inter-UAV conflicts, and terminal-area congestion. This study proposes a hierarchical th...
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...
Efficient task allocation and coordination are critical for heterogeneous multi-agent systems operating in dynamic field environments. This paper presents a closed-loop framework that integrates Large Language Models (LLMs) with graph-based optimisat...
In this work we focus on the problem of truck fleet management of the company GESUGA. This company is responsible of the collection and proper treatment of animals not intended for human consumption. On a daily basis, with the uncollected requests, t...
This study develops a safety-aware supervisory framework for industrial fed-batch bioreactors by integrating an adaptive digital twin, fuzzy multi-objective optimization, Agentic LLM reasoning, and an independent Safety Governor within a closed-loop...
Autonomous water quality monitoring using unmanned surface vehicles (USVs) requires effective path planning under time-varying hydrodynamic conditions and spatially evolving pollutant distributions. Existing approaches often rely on static environmen...
To address the problems of closed-loop task organization, strong corridor constraints, and path failure after local disturbances in unmanned aerial vehicle (UAV) inspection of gas-insulated switchgear (GIS) rooms, this paper proposes a topology-and-c...
Despite the large amount of successful existing methods and frameworks for planning sets of multiple unmanned aerial systems (UASs), there is still a lack of coordination frameworks that are capable of coping with real-world operational conditions. T...
Hybrid aerial underwater vehicles (HAUV) are a new frontier for vehicles. They can operate both underwater and aerially, providing enormous potential for a wide range of scientific explorations. Informative path planning is essential to vehicle auton...
Background/Objectives: Personalized ultrafractionated stereotactic adaptive radiotherapy (PULSAR) delivers high-dose stereotactic radiation as temporally separated pulses, permitting interval response assessment, adaptive replanning, and integration...
Space Station Operation Mission Planning (SSOMP) requires coordinated decisions across long-term activity allocation, mid-term logistics optimization, and short-term execution scheduling and is a key component of autonomous mission operations for hig...
Autonomous exploration is a fundamental challenge for various applications of unmanned aerial vehicles (UAVs). To enhance exploration efficiency in large-scale unknown environments, we propose a Fast Autonomous Exploration Framework (FAEM) designed t...
With the rapid development of industrial automation, robotic arms are increasingly required to operate in dynamic and unstructured environments. Path planning for such systems has become a critical challenge, particularly in scenarios involving dynam...
Efficient path planning for dual crane cooperative systems is a complex challenge due to high-dimensional configuration spaces and the volatile nature of dynamic construction environments. Conventional sampling-based algorithms often suffer from comp...
This study evaluates and compares the computational performance and practical applicability of advanced path planning algorithms for Unmanned Aerial Vehicles (UAVs) in dynamic and obstacle-rich environments. The Adaptive Multi-Objective Path Planning...
Accurate positioning remains a central challenge in head and neck radiotherapy because highly conformal dose distributions are delivered near multiple critical organs, and clinically relevant discrepancies may not be resolved by rigid image-guided co...
The problem of path planning is one of the most crucial and challenging issues in the fields of intelligent systems and autonomous robotics. A robot’s ability to move quickly and easily from a starting position to a goal position without hittin...
Energy optimization is crucial for unmanned aerial vehicle (UAV) path planning, particularly in complex wind-field environments. Most existing path-planning algorithms rely on simplified energy consumption models, which often fail to adequately captu...
Autonomous unmanned-aircraft obstacle avoidance is a mature research area, but comparatively less work addresses controller-level behavior motivated by European ground-risk and no-overflight considerations during an inspection mission that is already...
Vision–language–action (VLA) models often suffer from limited robustness in long-horizon manipulation tasks—where robots must execute extended sequences of actions over multiple time steps to achieve complex goals—due to their...
This article addresses the problem of finding the shortest route for Automated Guided Vehicles (AGVs) in a production environment with constrained battery state-of-charge (SoC) and time-dependent operating conditions. The route map is divided into a...
Introduction: Novel MRI-linear accelerator hybrids (MR-Linacs, MRL) promise an optimization of radiotherapy (RT) through daily MRI imaging with enhanced soft tissue contrast and plan adaptation on the anatomy of the day. These features might potentia...
Collaborative 3D path planning for multiple unmanned aerial vehicles (UAVs) in dense urban airspace is difficult, which does not come from one factor alone. Buildings, flight restrictions, moving obstacles, and inter-UAV coupling all act together, an...
The cooperative airdrop of UAV swarms by multiple transport aircraft creates a large-scale multi-agent planning problem. The mission involves heterogeneous aircraft, multi-visit airdrop areas, strict time windows, and threat-aware flight paths. To ad...
This paper proposes a novel algorithm, Terrain Analysis–Rapidly exploring Random Tree* (TA-RRT*), which improves the performance of path planning using adaptive sampling and step size based on terrain analysis. While existing RRT*-based algorit...
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