Search Results (37)

Controlled Traffic Regions (CTRs) around major airports pose an important challenge to Unmanned Aerial System (UAS) traffic management. Current regulations highly restrict UAS missions in these areas by confining them to segregated areas. This paper makes a proposal to allow more ambitious UAS missions inside CTRs, such as paths across the CTR or between heliports inside the CTR, based on self-separation. This proposal faces two important problems: on the one hand, the adaptive response to the dynamic airspace reconfiguration of a CTR without necessarily terminating the flight, and on the other, a self-managed conflict resolution that allows maintaining traffic separations without the intervention of air traffic controllers. This paper proposes a solution named Reinforcement Learning Multi-Agent Separation Management (RL-MASM). It employs a multi-agent reinforcement learning system with a fully decentralized decision-making scheme, although it uses a common information source of the environment. The proposed system is evaluated against classical control algorithms for obstacle avoidance to determine the potential benefits of AI-based methods. Results show that AI-based methods can benefit from knowing the intent of a UAS. This leads to increased performance in intrusions into no-fly zones or collisions, and also solves some challenging scenarios for classical control algorithms. From the aeronautical point of view, the proposed solution also introduces important advantages in terms of efficiency, scalability, and decentralization. Full article

In dynamic environments with continuous variability, such as those affected by typhoons, ship path planning must account for both navigational safety and the maneuvering characteristics of the vessel. However, current methods often struggle to accurately capture the continuous evolution of dynamic obstacles and generally lack adaptive exploration mechanisms. Consequently, the planned routes tend to be suboptimal or incompatible with the ship’s maneuvering constraints. To address this challenge, this study proposes a Space–Time Integrated Q-Learning (STIQ-Learning) algorithm for dynamic path planning under typhoon conditions. The algorithm is built upon the following key innovations: (1) Spatio-Temporal Environment Modeling: The hazardous area affected by the typhoon is decomposed into temporally and spatially dynamic obstacles. A grid-based spatio-temporal environment model is constructed by integrating forecast data on typhoon wind radii and wave heights. This enables a precise representation of the typhoon’s dynamic evolution process and the surrounding maritime risk environment. (2) Optimization of State Space and Reward Mechanism: A time dimension is incorporated to expand the state space, while a composite reward function is designed by combining three sub-reward terms: target proximity, trajectory smoothness, and heading correction. These components jointly guide the learning agent to generate navigation paths that are both safe and consistent with the maneuverability characteristics of the vessel. (3) Priority-Based Adaptive Exploration Strategy: A prioritized action selection mechanism is introduced based on collision feedback, and the exploration factor $ϵ$ is dynamically adjusted throughout the learning process. This strategy enhances the efficiency of early exploration and effectively balances the trade-off between exploration and exploitation. Simulation experiments were conducted using real-world scenarios derived from Typhoons Pulasan and Gamei in 2024. The results demonstrate that in open-sea environments, the proposed STIQ-Learning algorithm achieves reductions in path length of 14.4% and 22.3% compared to the D* and Rapidly exploring Random Trees (RRT) algorithms, respectively. In more complex maritime environments featuring geographic constraints such as islands, STIQ-Learning reductions of 2.1%, 20.7%, and 10.6% relative to the DFQL, D*, and RRT algorithms, respectively. Furthermore, the proposed method consistently avoids the hazardous wind zones associated with typhoons throughout the entire planning process, while maintaining wave heights along the generated routes within the vessel’s safety limits. Full article

The paper considers the problem of interaction between robots with parallel and serial structures that are part of a robotic system for aliquoting biomaterials. An approach to selecting the relative position and limiting the ranges of movement of manipulators working nearby to avoid collisions is presented. The elimination of collisions is ensured by the absence of intersections between work safety zones (a 3D space within which all manipulator links can be located for a given range of robot positions). Universal algorithms for determining work safety zones were developed, including for an individual manipulator and taking into account the work safety zone of the manipulator installed nearby and other obstacles. An analysis of the workspace and safety zones was performed, taking into account both individual limitations and limitations associated with collaboration within the system. The issue of adapting control algorithms of the robotic system to external disturbances in order to minimize the time spent on executing a given trajectory was addressed. In particular, the meeting point (interaction) of robots solving the problem of biomaterial aliquotation was optimized depending on the workload level of each robot. Experiments were carried out to verify the developed approaches. Full article

Obstacle avoidance is crucial for the successful completion of UAV missions. Static and dynamic obstacles, such as trees, buildings, flying birds, or other UAVs, can threaten these missions. As a result, safe path planning is essential, particularly for missions involving multiple UAVs. Collision-free paths can be designed in either 2D or 3D environments, depending on the scenario. This study provides an overview of recent advancements in obstacle avoidance and path planning for UAVs. These methods are compared based on various criteria, including avoidance techniques, obstacle types, the environment explored, sensor equipment, map types, and path statuses. Additionally, this paper includes a process addressing obstacle detection and avoidance and reviews the evolution of obstacle detection and avoidance (ODA) techniques in UAVs over the past decade. Full article

Before a fixed-wing UAV executes target tracking missions, it is essential to identify targets through reconnaissance mission areas using onboard payloads. This paper presents an autonomous mission planning method designed for such reconnaissance operations, enabling effective target identification prior to tracking. Existing planning methods primarily focus on flight performance, energy consumption, and obstacle avoidance, with less attention to integrating payload. Our proposed method emphasizes the combination of two key functions: flight path planning and payload mission planning. In terms of path planning, we introduce a method based on the Hierarchical Traveling Salesman Problem (HTSP), which utilizes the nearest neighbor algorithm to find the optimal visit sequence and entry points for area targets. When dealing with area targets containing no-fly zones, HTSP quickly calculates a set of waypoints required for coverage path planning (CPP) based on the Generalized Traveling Salesman Problem (GTSP), ensuring thorough and effective reconnaissance coverage. In terms of payload mission planning, our proposed method fully considers payload characteristics such as scan resolution, imaging width, and operating modes to generate predefined mission instruction sets. By meticulously analyzing payload constraints, we further optimized the path planning results, ensuring that each instruction meets the payload performance requirements. Finally, simulations validated the effectiveness and superiority of the proposed autonomous mission planning method in reconnaissance tasks. Full article

Improving decision-making in the autonomous maneuvering of unmanned aerial vehicles (UAVs) is of great significance to improving flight safety, the mission execution rate, and environmental adaptability. The method of deep reinforcement learning makes the autonomous maneuvering decision of UAVs possible. However, the current algorithm is prone to low training efficiency and poor performance when dealing with complex continuous maneuvering problems. In order to further improve the autonomous maneuvering level of UAVs and explore safe and efficient maneuvering methods in complex environments, a maneuvering decision-making method based on hierarchical reinforcement learning and Proximal Policy Optimization (PPO) is proposed in this paper. By introducing the idea of hierarchical reinforcement learning into the PPO algorithm, the complex problem of UAV maneuvering and obstacle avoidance is separated into high-level macro-maneuver guidance and low-level micro-action execution, greatly simplifying the task of addressing complex maneuvering decisions using a single-layer PPO. In addition, by designing static/dynamic threat zones and varying their quantity, size, and location, the complexity of the environment is enhanced, thereby improving the algorithm’s adaptability and robustness to different conditions. The experimental results indicate that when the number of threat targets is five, the success rate of the H-PPO algorithm for maneuvering to the designated target point is 80%, which is significantly higher than the 58% rate achieved by the original PPO algorithm. Additionally, both the average maneuvering distance and time are lower than those of the PPO, and the network computation time is only 1.64 s, which is shorter than the 2.46 s computation time of the PPO. Additionally, as the complexity of the environment increases, the H-PPO algorithm outperforms other compared networks, demonstrating the effectiveness of the algorithm constructed in this paper for guiding intelligent agents to autonomously maneuver and avoid obstacles in complex and time-varying environments. This provides a feasible technical approach and theoretical support for realizing autonomous maneuvering decisions in UAVs. Full article

Unmanned aerial vehicle (UAV) swarms have shown substantial potential to enhance operational efficiency and reduce strike costs, presenting extensive applications in modern urban warfare. However, achieving effective strike performance in complex urban environments remains challenging, particularly when considering three-dimensional obstacles and threat zones simultaneously, which can significantly degrade strike effectiveness. To address this challenge, this paper proposes a target strike strategy using the Electric Eel Foraging Optimization (EEFO) algorithm, a heuristic optimization method designed to ensure precise strikes in complex environments. The problem is formulated with specific constraints, modeling each UAV as an electric eel with random initial positions and velocities. This algorithm simulates the interaction, resting, hunting, and migrating behaviors of electric eels during their foraging process. During the interaction phase, UAVs engage in global exploration through communication and environmental sensing. The resting phase allows UAVs to temporarily hold their positions, preventing premature convergence to local optima. In the hunting phase, the swarm identifies and pursues optimal paths, while in the migration phase the UAVs transition to target areas, avoiding threats and obstacles while seeking safer routes. The algorithm enhances overall optimization capabilities by sharing information among surrounding individuals and promoting group cooperation, effectively planning flight paths and avoiding obstacles for precise strikes. The MATLAB(R2024b) simulation platform is used to compare the performance of five optimization algorithms—SO, SCA, WOA, MFO, and HHO—against the proposed Electric Eel Foraging Optimization (EEFO) algorithm for UAV swarm target strike missions. The experimental results demonstrate that in a sparse undefended environment, EEFO outperforms the other algorithms in terms of trajectory planning efficiency, stability, and minimal trajectory costs while also exhibiting faster convergence rates. In densely defended environments, EEFO not only achieves the optimal target strike trajectory but also shows superior performance in terms of convergence trends and trajectory cost reduction, along with the highest mission completion rate. These results highlight the effectiveness of EEFO in both sparse and complex defended scenarios, making it a promising approach for UAV swarm operations in dynamic urban environments. Full article

This paper details the development of a collision avoidance algorithm for unmanned surface vehicles (USVs) and its validation using free-running tests. The USV, designed as a catamaran, incorporates a variety of sensors for its guidance, navigation, and control system. It performs turning maneuvers using thrusters positioned on the port and starboard sides. The robot operating system is used to streamline communication, transmitting data such as position, orientation, and situational information from diverse sensors. Using the collision risk index (CRI) method, the algorithm calculates risk based on the distance to obstacles and the angle to the desired waypoint, directing the USV on a path with minimized risk. Noise within the data captured by the two-dimensional light detection and ranging system is filtered out using the k-dimensional tree and Euclidean distance methods, ensuring single obstacles are distinctly identified. To assess the efficacy of the CRI-based collision avoidance algorithm, it was benchmarked against other algorithms rooted in the artificial potential field and safety zone methods within an artificial tank setting. The results highlight the CRI method’s superior time efficiency and optimality in comparison to its counterparts. Full article

Unmanned aerial vehicles (UAVs) have become essential tools with diverse applications across multiple sectors, including remote sensing. This paper presents a trajectory planning method for a team of UAVs aimed at enhancing covert video sensing in uneven terrains and urban environments. The approach establishes a feasible flight zone, which dynamically adjusts to accommodate line of sight (LoS) occlusions caused by elevated terrains and structures between the UAVs’ sensors and the target. By avoiding ‘shadows’—projections of realistic shapes on the UAVs’ operational plane that represent buildings and other obstacles—the method ensures continuous target visibility. This strategy optimizes UAV trajectories, maintaining covertness while adapting to the changing environment, thereby improving overall video sensing performance. The method’s effectiveness is validated through comprehensive MATLAB simulations at both single and multiple UAV levels, demonstrating its ability to prevent LoS occlusions while preserving a high level of camouflage. Full article

Robotic walking devices can be used for intensive exercises to enhance gait rehabilitation therapies. Mixed Reality (MR) techniques may improve engagement through immersive and interactive environments. This article introduces an MR-based multimodal human–robot interaction strategy designed to enable shared control with a Smart Walker. The MR system integrates virtual and physical sensors to (i) enhance safe navigation and (ii) facilitate intuitive mobility training in personalized virtual scenarios by using an interface with three elements: an arrow to indicate where to go, laser lines to indicate nearby obstacles, and an ellipse to show the activation zone. The multimodal interaction is context-based; the presence of nearby individuals and obstacles modulates the robot’s behavior during navigation to simplify collision avoidance while allowing for proper social navigation. An experiment was conducted to evaluate the proposed strategy and the self-explanatory nature of the interface. The volunteers were divided into four groups, with each navigating under different conditions. Three evaluation methods were employed: task performance, self-assessment, and observational measurement. Analysis revealed that participants enjoyed the MR system and understood most of the interface elements without prior explanation. Regarding the interface, volunteers who did not receive any introductory explanation about the interface elements were mostly able to guess their purpose. Volunteers that interacted with the interface in the first session provided more correct answers. In future research, virtual elements will be integrated with the physical environment to enhance user safety during navigation, and the control strategy will be improved to consider both physical and virtual obstacles. Full article

Trajectory optimization is a method of finding the optimal route connecting a start and end point. The suitability of a trajectory depends on not intersecting any obstacles, as well as predefined performance metrics. In the context of unmanned aerial vehicles (UAVs), the goal is to minimize the route cost, in terms of energy or time, while avoiding restricted flight zones. Artificial intelligence techniques, including evolutionary computation, have been applied to trajectory optimization with varying degrees of success. This work explores the use of genetic programming (GP) for 3D trajectory optimization by developing a novel GP algorithm to optimize trajectories in a 3D space by encoding 3D geographic trajectories as function trees. The effects of parameterization are also explored and discussed, demonstrating the advantages and drawbacks of custom parameter settings along with additional evolutionary computational techniques. The results demonstrate the effectiveness of the proposed algorithm, which outperforms existing methods in terms of speed, automaticity, and robustness, highlighting the potential for GP-based algorithms to be applied to other complex optimization problems in science and engineering. Full article

This article presents a combined route and path planning strategy to guide Skid–Steer Mobile Robots (SSMRs) in scheduled harvest tasks within expansive crop rows with complex terrain conditions. The proposed strategy integrates: (i) a global planning algorithm based on the Traveling Salesman Problem under the Capacitated Vehicle Routing approach and Optimization Routing (OR-tools from Google) to prioritize harvesting positions by minimum path length, unexplored harvest points, and vehicle payload capacity; and (ii) a local planning strategy using Informed Rapidly-exploring Random Tree (${\mathrm{IRRT}}^{*}$) to coordinate scheduled harvesting points while avoiding low-traction terrain obstacles. The global approach generates an ordered queue of harvesting locations, maximizing the crop yield in a workspace map. In the second stage, the ${\mathrm{IRRT}}^{*}$ planner avoids potential obstacles, including farm layout and slippery terrain. The path planning scheme incorporates a traversability model and a motion model of SSMRs to meet kinematic constraints. Experimental results in a generic fruit orchard demonstrate the effectiveness of the proposed strategy. In particular, the ${\mathrm{IRRT}}^{*}$ algorithm outperformed RRT and ${\mathrm{RRT}}^{*}$ with 96.1% and 97.6% smoother paths, respectively. The ${\mathrm{IRRT}}^{*}$ also showed improved navigation efficiency, avoiding obstacles and slippage zones, making it suitable for precision agriculture. Full article

This paper focuses on a distributed cooperative time-varying formation maneuvering issue of under-actuated unmanned surface vehicles (USVs). A fleet of USVs is guided by a parameterized path with a time-varying formation while avoiding collisions and preserving the connectivity in the environment with multiple obstacles. In some surface missions, due to the obstacles in the external environment, the bandwidth limitations of the communication channel, and the hardware components/performance constraints of the USVs themselves, each vehicle is considered to be subject to model uncertainty, actuator quantization, sensor dead zone, and velocity constraints. During the control design process, the radial basis function (RBF) neural networks (NNs) are utilized to deal with nonlinear terms. Based on a nonlinear decomposition method, the relationship between the control signal and the quantization one is established, which overcomes the difficulty arising from actuator quantization. A Nussbaum function is introduced to handle the unknown output dead zone problem caused by reduced sensor sensitivity. Moreover, a universal-constrained function is employed to satisfy both the constrained and unconstrained requirements during formation keeping and obstacle avoidance. The Lyapunov stability theory confirmed that the error signals are uniformly ultimately bounded (UUB). The simulation results demonstrate the effectiveness of the proposed distributed formation control of multiple USVs. Full article

The quest for efficient and safe trajectory planning in robotic manipulation poses significant challenges, particularly in complex obstacle environments where the risk of encountering singularities and obstacles is high. Addressing this critical issue, our study presents a novel enhancement of the Fast Marching Tree (FMT) algorithm, ingeniously designed to navigate the complex terrain of Cartesian space with an unprecedented level of finesse. At the heart of our approach lies a sophisticated two-stage path point sampling strategy, ingeniously coupled with a singularity avoidance mechanism that leverages geometric perception to assess and mitigate the risk of encountering problematic configurations. This innovative method not only facilitates seamless obstacle navigation but also adeptly circumvents the perilous zones of singularity, ensuring a smooth and uninterrupted path for the robotic arm. To further refine the trajectory, we incorporate a quasi-uniform cubic B-spline curve, optimizing the path for both efficiency and smoothness. Our comprehensive simulation experiments underscore the superiority of our algorithm, showcasing its ability to consistently achieve shorter, more efficient paths while steadfastly avoiding obstacles and singularities. The practical applicability of our method is further corroborated through successful implementation in real-world robotic arm trajectory planning scenarios, highlighting its potential to revolutionize the field with its robustness and adaptability. Full article

This study aimed to solve the problem that is the frequent switching between the acceleration and braking modes of the driverless ferry vehicle, affecting the comfort and stability of speed control. The driverless ferry vehicle encounters unknown obstacles on the road that affect the normal planning and tracking control of the ferry vehicle and finally lead to the problem that the driverless ferry vehicle cannot drive normally. First of all, in the longitudinal control, the fuzzy PID control algorithm was utilized to produce the fuzzy PID acceleration controller by taking into account the difference between the actual and expected speeds and choosing the triangular membership function. According to the relationship between the brake oil pressure and brake torque, the brake controller was designed. The acceleration/braking switching module with acceleration tolerance zone was added to the longitudinal controller, and the acceleration/braking mode-switching controller was designed. Secondly, in the lateral control, the tire cornering stiffness was analyzed, an MPC controller with a planning module was designed, and a lateral motion controller with an obstacle avoidance replanning function was proposed. Finally, according to the prediction time domain of different planning modules corresponding to different speeds, a coordinated control strategy of horizontal and longitudinal motion was proposed by using a real-time speed adjustment planning module to predict the time domain. Through the joint simulation analysis of MATLAB and CarSim, the results show that the driving stability of the ferry vehicle was significantly improved, and the longitudinal speed error of the ferry vehicle was reduced by 43.59%. The ferry’s avoidance of obstacles and tracking of reference trajectories were significantly improved, so that the tracking error can be reduced by 61.11%. Full article