NI-MOD-P-RRT*: A threshold-free sampling-based path planning algorithm for unmanned surface vehicles in dynamic environments

In the real-time decision-making and local planning process of autonomous vehicles in dynamic environments, the autonomous driving system may fail to find a reasonable policy or even gets trapped in some situation due to the complexity of global tasks and the incompatibility between upper-level maneuver decisions with the low-level lower level trajectory planning. To solve this problem, this paper presents a synchronous maneuver searching and trajectory planning (SMSTP) algorithm based on the topological concept of homotopy. Firstly, a set of alternative maneuvers with boundary limits are enumerated on a multi-lane road. Instead of sampling numerous paths in the whole spatio-temporal space, we, for the first time, propose using Trajectory Profiles (TPs) to quickly construct the topological maneuvers represented by different routes, and put forward a corridor generation algorithm based on graph-search. The bounded corridor further constrains the maneuver's space in the spatial space. A step-wise heuristic optimization algorithm is then proposed to synchronously generate a feasible trajectory for each maneuver. To achieve real-time performance, we initialize the states to be optimized with the boundary constraints of maneuvers, and we set some heuristic states as terminal targets in the quadratic cost function. The solution of a feasible trajectory is always guaranteed only if a specific maneuver is given. The simulation and realistic driving-test experiments verified that the proposed SMSTP algorithm has a short computation time which is less than 37ms, and the experimental results showed the validity and effectiveness of the SMSTP algorithm.

This paper addresses the problem of real time flexible operation of a mobile vehicle in dynamic environment which is still a very challenging problem because the surrounding situations are not qualified in static, knowledge is only partial and the execution is often associated with uncertainty. It is difficulty to select appropriate sub-target in real-time to obtain a collision-free and low cost path. A conventional control method usually sets only a best control target beforehand based on the control purpose and object’s characteristics. For many real systems, the situations always change with constraints or disturbances, which result in the necessary to set sub-targets between current state and final target. Thus recalculation of new target is necessary the best target becomes unavailable because of the constraints or disturbances. And the recalculation is costly and involves time lags. Human decisions to act are based on broad targets and respond flexibly in different situations. Responding flexibly to the dynamic environment change like human is considered in this paper. We propose a fuzzy target based intelligent soft decision-making predictive fuzzy controller for differential drive mobile vehicle in dynamic environment to realize autonomous navigation. Simulation in Webots demonstrated the validity and feasibility of our fuzzy target based soft decision controller.

Automated docking technologies for marine vessels have advanced significantly, yet trailer loading, a critical and routine task for autonomous surface vehicles (ASVs), remains largely underexplored. This paper presents a novel, vision-based framework for autonomous trailer loading that operates without GPS, making it adaptable to dynamic and unstructured environments. The proposed method integrates real-time computer vision with a finite state machine (FSM) control strategy to detect, approach, and align the ASV with the trailer using visual cues such as LED panels and bunk boards. A realistic simulation environment, modeled after real-world conditions and incorporating wave disturbances, was developed to validate the approach and is available1. Experimental results using the WAM-V 16 ASV in Gazebo demonstrated a 100% success rate under calm to medium wave disturbances and a 90% success rate under high wave conditions. These findings highlight the robustness and adaptability of the vision-driven system, offering a promising solution for fully autonomous trailer loading in GPS-denied scenarios.

This paper presents the autonomous tracking and following of a marine vessel by an Unmanned Surface Vehicle in the presence of dynamic obstacles while following the International Regulations for Preventing Collisions at Sea (COLREGS) rules. The motion prediction for the target vessel is based on Monte-Carlo sampling of dynamically feasible and collision-free paths with fuzzy weights, leading to a predicted path resembling anthropomorphic driving behavior. This prediction is continuously optimized for a particular target by learning the necessary parameters for a 3-degree-of-freedom model of the vessel and its maneuvering behavior from its path history without any prior knowledge. The path planning for the USV with COLREGS is achieved on a grid-based map in a single stage by incorporating A* path planning with Artificial Terrain Costs for dynamically changing obstacles. Various scenarios for interaction, including multiple civilian and adversarial vessels, are handled by the planner with ease. The effectiveness of the algorithms has been demonstrated both in representative simulations and on-water experiments.

The researches of Simultaneous Localization and Mapping (SLAM) are very important problems for mobile robot in dynamic environments. This paper shows a new approach joining data given by an odometer and a laser extend discoverer sensor to proficiently explain the SLAM problem of Unmanned Ground Vehicles (UGV). Our approach to self-localization in a non-static environment uses an important step in the map management algorithm that is robust to delete the moving feature. The robustness is accomplished by taking a part of the dynamic object instead of all features at once, meaning that partial occlusion will only affect a subset of all features. Our hypothesis is that a feature that is moving with an important velocity is considered that is out of date and it cannot maintain much useful information. The updates of dynamic landmarks are calculated every time step, as well as a measurement update which is the dynamic object positions become more uncertain as they move. A new Adaptive Smooth Variable Structure Filter (ASVSF) SLAM algorithm is implemented to localize the UGV with an original covariance matrix formulation. The proposed algorithm is validated in real-world and the results obtained confirm the efficiency and robustness in a dynamic environment.

The evaluation of collision avoidance strategies for special-purpose unmanned ground vehicles (UGVs) operating in dynamic environments with human presence is presented in this study. The autonomous wheeled vehicle equipped with an armament module for combat and reconnaissance tasks is described considering mission specifications and off-road usage scenarios. The main operational parameters are outlined, and the obstacle detection features integrated into the PERUN vehicle are introduced. The control system architecture is presented, along with installed sensors and their perception capabilities of surrounding object detection. Safety considerations in operational conditions where human presence force appropriate collision avoidance actions are discussed. Real field tests conducted in diverse weather and environmental conditions, such as industrial area and unstructured terrains, are shown to demonstrate the system's performance. Carried out tests show that the collision avoidance system enables obstacle detection and proving the effectiveness of steering strategies for the platform's navigation. The safe operation of unmanned systems in environments with human presence is crucial for the successful deployment and scalability of UGV technologies.

PurposeThe motion control of unmanned ground vehicles (UGV) is a challenge in the industry of automation. The purpose of this paper is to propose a fuzzy inference system (FIS) based on sensory information for solving the navigation challenge of UGV in cluttered and dynamic environments.Design/methodology/approachThe representation of the dynamic environment is a key element for the operational field and for the testing of the robotic navigation system. If dynamic obstacles move randomly in the operation field, the navigation problem becomes more complicated due to the coordination of the elements for accurate navigation and collision-free path within the environmental representations. This paper considers the construction of the FIS, which consists of two controllers. The first controller uses three sensors based on the obstacles distances from the front, right and left. The second controller employs the angle difference between the heading of the vehicle and the targeted angle to obtain the optimal route based on the environment and reach the desired destination with minimal running power and delay. The proposed design shows an efficient navigation strategy that overcomes the current navigation challenges in dynamic environments.FindingsExperimental analyses are conducted for three different scenarios to investigate the validation and effectiveness of the introduced controllers based on the FIS. The reported simulation results are obtained using MATLAB software package. The results show that the controllers of the FIS consistently perform the manoeuvring task and manage the route plan efficiently, even in a complex environment that is populated with dynamic obstacles. The paper demonstrates that the destination was reached optimally using the shortest free route.Research limitations/implicationsThe paper represents efforts toward building a dynamic environment filled with dynamic obstacles that move at various speeds and directions. The methodology of designing the FIS is accomplished to guide the UGV to the desired destination while avoiding collisions with obstacles. However, the methodology is approached using two-dimensional analyses. Hence, the paper suggests several extensions and variations to develop a three-dimensional strategy for further improvement.Originality/valueThis paper presents the design of a FIS and its characterizations in dynamic environments, specifically for obstacles that move at different velocities. This facilitates an improved functionality of the operation of UGV.

The use of underwater robot systems, including Autonomous Underwater Vehicles (AUVs), has been studied as an effective way of monitoring and exploring dynamic aquatic environments. Furthermore, advances in artificial intelligence techniques and computer processing led to a significant effort towards fully autonomous navigation and energy-efficient approaches. In this work, we formulate a reinforcement learning framework for long-term navigation of underwater vehicles in dynamic environments using the techniques of tile coding and eligibility traces. Simulation results used actual oceanic data from the Regional Ocean Modeling System (ROMS) data set collected in Southern California Bight (SCB) region, California, USA.

SLAM based on Adaptive SVSF for Cooperative Unmanned Vehicles in Dynamic environment

Motion planning is an essential component in intelligent vehicle study. Rapidly-exploring Random Tree(RRT) and its variants are popular algorithms that have been successfully applied in solving motion planning problems. However, obtaining an optimal trajectory while concerning driving safety in dynamic environments is a difficult problem. In this study, we present an active safe RRT(AS-RRT) motion planning algorithm that enable the intelligent vehicle to avoid collision risks and find an efficient path in the dynamic environment. The algorithm firstly reconstructs a potential field-based configuration space for static obstacles and moving vehicles, which defines the risk regions. Then, it develops an RRT tree through samples in the space with considerations of nonholonomic constraints of the vehicles. A comprehensive cost function is used for the priority sequence mechanism to get an initial trajectory. After that, the trajectory is asymptotically optimized gradually by decreasing the cost iteratively. Simulation results demonstrated that the proposed algorithm improved the vehicles’ motion planning safety performance in dynamic environments.

In this paper, a hierarchical framework for task assignment and path planning of multiple unmanned aerial vehicles (UAVs) in a dynamic environment is presented. For multi-agent scenarios in dynamic environments, a candidate algorithm should be able to replan for a new path to perform the updated tasks without any collision with obstacles or other agents during the mission. In this paper, we propose an intersection-based algorithm for path generation and a negotiation-based algorithm for task assignment since these algorithms are able to generate admissible paths at a smaller computing cost. The path planning algorithm is also augmented with a potential field-based trajectory replanner, which solves for a detouring trajectory around other agents or pop-up obstacles. For validation, test scenarios for multiple UAVs to perform cooperative missions in dynamic environments are considered. The proposed algorithms are implemented on a fixed-wing UAVs testbed in outdoor environment and showed satisfactory performance to accomplish the mission in the presence of static and pop-up obstacles and other agents.

Energy management strategies typically employ reinforcement learning algorithms in a static state. However, during vehicle operation, the environment is dynamic and laden with uncertainties and unforeseen disruptions. This study proposes an adaptive learning strategy in dynamic environments that adapts actions to changing circumstances, drawing on past experience to enhance future real-world learning. We developed a memory library for dynamic environments, employed Dirichlet clustering for driving conditions, and incorporated the expectation maximization algorithm for timely model updating to fully absorb prior knowledge. The agent swiftly adapts to the dynamic environment and converges quickly, improving hybrid electric vehicle fuel economy by 5–10% while maintaining the final state of charge (SOC). Our algorithm’s engine operating point fluctuates less, and the working state is compact compared with Deep Q-Network (DQN) and Deterministic Policy Gradient (DDPG) algorithms. This study provides a solution for vehicle agents in dynamic environmental conditions, enabling them to logically evaluate past experiences and carry out situationally appropriate actions.

A new reactive motion planning method for an autonomous vehicle in dynamic environments is proposed. The new dynamic motion planning method combines a virtual plane based reactive motion planning technique with a sensor fusion based obstacle detection approach, which results in improving robustness and autonomy of vehicle navigation within unpredictable dynamic environments. The key feature of the new reactive motion planning method is based on a local observer in the virtual plane which allows the effective transformation of complex dynamic planning problems into simple stationary in the virtual plane. In addition, a sensor fusion based obstacle detection technique provides the pose estimation of moving obstacles by using a Kinect sensor and a sonar sensor, which helps to improve the accuracy and robustness of the reactive motion planning approach in uncertain dynamic environments. The performance of the proposed method was demonstrated through not only simulation studies but also field experiments using multiple moving obstacles even in hostile environments where conventional method failed.

This paper introduces an accurate nonlinear model predictive control-based algorithm for trajectory following. For accuracy, the algorithm incorporates both the planned state and control trajectories into its cost functional. Current following algorithms do not incorporate control trajectories into their cost functionals. Comparisons are made against two trajectory following algorithms, where the trajectory planning problem is to safely follow a person using an automated ATV with control delays in a dynamic environment while simultaneously optimizing speed and steering, minimizing control effort, and minimizing the time-to-goal. Results indicate that the proposed algorithm reduces collisions, tracking error, orientation error, and time-to-goal. Therefore, tracking the control trajectories with the trajectory following algorithm helps the vehicle follow the planned state trajectories more accurately, which ultimately improves safety, especially in dynamic environments.

In this paper, we present an on-board vision-based approach for avoidance of moving obstacles in dynamic environments. Our approach relies on an efficient obstacle detection and tracking algorithm based on depth image pairs, which provides the estimated position, velocity and size of the obstacles. Robust collision avoidance is achieved by formulating a chance-constrained model predictive controller (CC-MPC) to ensure that the collision probability between the micro aerial vehicle (MAV) and each moving obstacle is below a specified threshold. The method takes into account MAV dynamics, state estimation and obstacle sensing uncertainties. The proposed approach is implemented on a quadrotor equipped with a stereo camera and is tested in a variety of environments, showing effective on-line collision avoidance of moving obstacles.