Robust Path Planning Research Articles

A Comparative Study of AStar, LPA* and DStarLite Path Planning Algorithms Based on Matlab

In the realm of robotics and autonomous systems, path planning is a pivotal component that determines the efficacy and safety of navigational tasks. With the proliferation of autonomous vehicles, drones, and mobile robots, the need for efficient and adaptive path planning algorithms has become increasingly acute. This paper studies AStar, LPA and DStarLite path planning algorithms based on Matlab platform, and compares their performance through simulation experiments. AStar algorithm is simple and widely applicable, but it has some shortcomings in path smoothness and computational efficiency. LPA improves path smoothness by introducing dynamic cost updating, but it may sacrifice some computational efficiency. The DStarLite algorithm performs well in dynamic environments with an efficient incremental update strategy that maintains high path smoothness and low computational costs. The experimental results show that DStarLite is the fastest in most cases, LPA* and DStarLite are superior to AStar in path smoothness. Future research may explore combining the advantages of each algorithm to develop more efficient, flexible and robust path planning algorithms to cope with complex and changeable actual scenarios.

Autonomous Robot Navigation Using Adaptive Potential Field

This work presents a novel approach to mobile robot autonomous navigation, utilizing enhanced artificial potential fields in conjunction with path planning,obstacle avoidance and SLAM . In traditional adaptive potential fields, which integrate sensor data to enable robots to navigate safely and efficiently through various environments. The robot's perception of its surroundings, obtained through sensors like cameras, ultrasonic devices, is processed to create a dynamic potential field that reflects the spatial distribution of obstacles, goals, and other relevant features.We propose the design and implementation of an autonomous robot-based real-time vision-based system for detecting and tracking features in a structured environment ensuring robust obstacle avoidance and path planning. Keywords- Adaptive Potential Fields, Autonomous Navigation, Autonomous Systems, Machine Learning in Robotics, Motor driver, Obstacle Avoidance, Path Planning, Raspberry Pi, Real-time Adaptation, ROS (Robot Operating System), Sensor-based Navigation, Servo Motor, SLAM (Simultaneous Localization and Mapping),Ultrasonic Sensors

Energy-Aware 3D Path Planning by Autonomous Ground Vehicle in Wireless Sensor Networks

Wireless sensor networks are used to monitor the environment, to detect anomalies or any other problems and risks in the system. If used in the transportation network, they can monitor traffic and detect traffic risks. In wireless sensor networks, energy constraints must be handled to enable continuous environmental monitoring and surveillance data gathering and communication. Energy-aware path planning of autonomous ground vehicle charging for sensor nodes can solve energy and battery replacement problems. This paper uses the Nearest Neighbour algorithm for the energy-aware path planning problem with an autonomous ground vehicle. Path planning simulations show that the Nearest Neighbour algorithm converges faster and produces a better solution than the genetic algorithm. We offer robust and energy-efficient path planning algorithms to swiftly collect sensor data with less energy, allowing the monitoring system to respond faster to anomalies. Positioning communicating sensors closer minimizes their energy usage and improves the network lifetime. This study also considers the scenario in which it is recommended to avoid taking direct travelling pathways between particular node pairs for a variety of different reasons. To address this more challenging scenario, we provide an Obstacle-Avoided Nearest Neighbour-based approach that has been adapted from the Nearest Neighbour approach. Within the context of this technique, the direct paths that connect the nodes are restricted. Even in this case, the Obstacle-Avoided Nearest Neighbour-based approach achieves almost the same performance as the the Neighbour-based approach.

Modeling and performance evaluation of mecanum wheel platform with reduced vibration and a robust path planning

Modeling and performance evaluation of mecanum wheel platform with reduced vibration and a robust path planning

Evaluation of popular path planning algorithms

The navigation of mobile robots is a key element of autonomous systems, which allows robots to move effectively and securely in changing environments with greater autonomy and precision. This study aims to provide researchers with a comprehensive guide for selecting the best path-planning methods for their particular projects. We evaluate some popular algorithms that are regularly used in mobile robot navigation, in order to demonstrate their specifications and determine where they are most effective. For example, one algorithm is used to model the problem as a standard graph, and another algorithm is found to be the most suitable for highly dynamic and highly dimensional environments, due to its robust path-planning capabilities and efficient route construction. We also filter high-performance algorithms in terms of computational complexity, accuracy, and robustness. In conclusion, this study provides valuable information on its individual strengths and weaknesses, helping robotics and engineers make informed decisions when selecting the most appropriate algorithm for their specific applications.

Heterogeneous Multi-Robot Collaboration for Coverage Path Planning in Partially Known Dynamic Environments

This research presents a cooperation strategy for a heterogeneous group of robots that comprises two Unmanned Aerial Vehicles (UAVs) and one Unmanned Ground Vehicles (UGVs) to perform tasks in dynamic scenarios. This paper defines specific roles for the UAVs and UGV within the framework to address challenges like partially known terrains and dynamic obstacles. The UAVs are focused on aerial inspections and mapping, while UGV conducts ground-level inspections. In addition, the UAVs can return and land at the UGV base, in case of a low battery level, to perform hot swapping so as not to interrupt the inspection process. This research mainly emphasizes developing a robust Coverage Path Planning (CPP) algorithm that dynamically adapts paths to avoid collisions and ensure efficient coverage. The Wavefront algorithm was selected for the two-dimensional offline CPP. All robots must follow a predefined path generated by the offline CPP. The study also integrates advanced technologies like Neural Networks (NN) and Deep Reinforcement Learning (DRL) for adaptive path planning for both robots to enable real-time responses to dynamic obstacles. Extensive simulations using a Robot Operating System (ROS) and Gazebo platforms were conducted to validate the approach considering specific real-world situations, that is, an electrical substation, in order to demonstrate its functionality in addressing challenges in dynamic environments and advancing the field of autonomous robots.

Autonomous navigation and adaptive path planning in dynamic greenhouse environments utilizing improved LeGO‐LOAM and OpenPlanner algorithms

AbstractThe autonomous navigation of greenhouse robots depends on precise mapping, accurate localization information and a robust path planning strategy. However, the complex agricultural environment introduces significant challenges to robot perception and path planning. In this study, a hardware system designed exclusively for greenhouse agricultural environments is presented, employing multi‐sensor fusion to diminish the interference of complex environmental conditions. Furthermore, a robust autonomous navigation framework based on the improved lightweight and ground optimized lidar odometry and mapping (LeGO‐LOAM) and OpenPlanner has been proposed. In the perception phase, a relocalization module is integrated into the LeGO‐LOAM framework. Comprising two key steps—map matching and filtering optimization, it ensures a more precise pose relocalization. During the path planning process, ground structure and plant density are considered in our Enhanced OpenPlanner. Additionally, a hysteresis strategy is introduced to enhance the stability of system state transitions. The performance of the navigation system in this paper was evaluated in several complex greenhouse environments. The integration of the relocalization module significantly decreases the absolute pose error (APE) in the perception process, resulting in more accurate pose estimation and relocalization information. In our experiments, the APE was reduced by at least 24.42%. Moreover, our enhanced OpenPlanner exhibits the capability to plan safer trajectories and achieve more stable state transitions in the experiments. The results underscore the safety and robustness of our proposed approach, highlighting its promising application prospects in autonomous navigation for agricultural robots.

Unified Robust Path Planning and Optimal Trajectory Generation for Efficient 3D Area Coverage of Quadrotor UAVs

Unified Robust Path Planning and Optimal Trajectory Generation for Efficient 3D Area Coverage of Quadrotor UAVs

Robust Online Multi-robot Simultaneous Exploration and Coverage Path Planning.

Robust Online Multi-robot Simultaneous Exploration and Coverage Path Planning.

Intelligent Systems in Motion

Simultaneous localization and mapping (SLAM) serves as a cornerstone in autonomous systems and has seen exponential growth in its roles, particularly in facilitating advanced path planning solutions. One emerging avenue of research that is rapidly evolving is the incorporation of multi-sensor fusion techniques to enhance SLAM-based path planning. The paper initiates with a thorough review of various sensor types and their attributes before covering a broad spectrum of both traditional and contemporary algorithms for multi-sensor fusion within SLAM. Performance evaluation metrics pertinent to SLAM and sensor fusion are explored. A special focus is laid on the interconnected roles and applications of multi-sensor fusion in SLAM-based path planning, discussing its significance in navigation scenarios as well as addressing challenges such as computational burden and real-time implementation. This paper sets the stage for future developments in creating more robust, resilient, and efficient SLAM-based path planning systems enabled by multi-sensor fusion.

A robust multiple Unmanned Aerial Vehicles 3D path planning strategy via improved particle swarm optimization

Path planning algorithms for Unmanned Aerial Vehicles (UAVs) are essential in various domains, such as search and rescue operations, agriculture, and delivery services. Nonetheless, finding optimal flight paths in complex environments with obstacles remains challenging. This paper proposes a novel path planning algorithm that uses a Nash equilibrium approach based on game theory to strike a balance between the exploitation and exploration capabilities of UAVs. The algorithm is designed in a simulated flight environment with obstacles, utilizing the self-awareness and group awareness coefficients from particle swarm optimization(PSO) as participants in the game theory model. The proposed path planning algorithm significantly improves the navigation of multiple UAVs in complex scenarios with obstacles by achieving a balance between their exploitation and exploration capabilities. Simulation experiments demonstrate that the proposed method surpasses traditional approaches, exhibiting an average improvement of 32.57%, 32.17%, and 29.33% in the algorithm convergence time, average flight distance, and average flight time, respectively. The significance of this research lies in its contribution to advancing UAV path planning algorithms. By integrating game theory and PSO, the proposed method optimizes the exploration and exploitation capabilities of UAVs, leading to improved navigation and resource utilization.

A Robust and Efficient UAV Path Planning Approach for Tracking Agile Targets in Complex Environments

The research into the tracking methods of unmanned aerial vehicles (UAVs) for agile targets is multi-disciplinary, with important application scenarios. Using a quadrotor as an example, in this paper, we mainly researched the tracking-related modeling and application verification of agile targets. We propose a robust and efficient UAV path planning approach for tracking agile targets aggressively and safely. This approach comprehensively takes into account the historical observations of the tracking target and the surrounding environment of the location. It reliably predicts a short time horizon position of the moving target with respect to the dynamic constraints. Firstly, via leveraging the Bernstein basis polynomial and combining obstacle distribution information around the target, the prediction module evaluated the future movement of the target, presuming that it endeavored to stay away from the obstacles. Then, a target-informed dynamic searching method was embraced as the front end, which heuristically searched for a safe tracking trajectory. Secondly, the back-end optimizer ameliorated it into a spatial–temporal optimal and collision-free trajectory. Finally, the tracking trajectory planner generated smooth, dynamically feasible, and collision-free polynomial trajectories in milliseconds, which is consequently reasonable for online target tracking with a restricted detecting range. Statistical analysis, simulation, and benchmark comparisons show that the proposed method has at least 40% superior accuracy compared to the leading methods in the field and advanced capabilities for tracking agile targets.

An optimization-based selection approach of landing sites for swarm unmanned aerial vehicles in unknown environments

Autonomous selection of landing sites is an important capability of a mission when unmanned aerial vehicles (UAVs) face technical difficulties or adverse weather, especially in unknown environments. In order to build a better decision-making system for swarm UAVs landing, besides terrain safety, the global flight cost among individuals should be also considered during descent. However, the existing methods cannot solve these problems elegantly due to terrain uncertainty and optimization complexity. In this paper, we present an optimization method to tackle this issue based on point cloud integrating the coarse-to-fine manner and swarm intelligence model. Specifically, our method starts with a point cloud preprocessing module for sparse elevation estimation and robust path planning. In the coarse stage, a novel cost map is constructed by extracting only low-level features from the elevation map. Based on the optimum results of the coarse selection, the final landing sites are automatically generated and finely evaluated in terms of multiple 3D terrain factors. Finally, the landing model of swarm-UAVs is treated as an unbalanced assignment problem, which minimizes flight costs by incorporating terrain safety, fuel consumption, and path planning. Experimental evaluations on three different real-world scenario datasets demonstrate the effectiveness of our method in both normal operations and emergency situations.

Emission-aware adjustable robust flight path planning with respect to fuel and contrail cost

Aviation emission has always been regarded as the major contributor to climate and atmosphere changes. The recent research suggested that flights can be re-routed from sufficiently cold and humid atmospheres to mitigate the level of contrail production in particular regions during the flight planning stage. To address the needs, a weather data-driven flight path planning is proposed in this work. The spatial meteorological condition is generated using historical data and formulated as uncertain factors to develop a robust solution. Airlines can determine their allowance of flight level changes, pre-determined constrained flight levels set and robustness towards the maximum tolerance level of uncertain contrail length of a flight path. The overall cost can be further minimised, with a lowered total contrail length with the least increases in fuel consumption cost. The suggested methods validate the possibility of emission-aware adjustable robust flight path planning and ensure sustainable air transport operations.

Uncertainty Estimation of Pedestrian Future Trajectory Using Bayesian Approximation

Past research on pedestrian trajectory forecasting mainly focused on deterministic predictions which provide only point estimates of future states. These future estimates can help an autonomous vehicle plan its trajectory and avoid collision. However, under dynamic traffic scenarios, planning based on deterministic predictions is not trustworthy. Rather, estimating the uncertainty associated with the predicted states with a certain level of confidence can lead to robust path planning. Hence, the authors propose to quantify this uncertainty during forecasting using stochastic approximation which deterministic approaches fail to capture. The current method is simple and applies Bayesian approximation during inference to standard neural network architectures for estimating uncertainty. The authors compared the predictions between the probabilistic neural network (NN) models with the standard deterministic models. The results indicate that the mean predicted path of probabilistic models was closer to the ground truth when compared with the deterministic prediction. Further, the effect of stochastic dropout of weights and long-term prediction on future state uncertainty has been studied. It was found that the probabilistic models produced better performance metrics like average displacement error (ADE) and final displacement error (FDE). Finally, the study has been extended to multiple datasets providing a comprehensive comparison for each model.

Hierarchical Freespace Planning for Navigation in Unfamiliar Worlds

Autonomous navigation in a large, complex space requires a spatial model, but the construction of a detailed map is costly. This paper demonstrates how two kinds of exploration support an alternative to metric mapping, one that facilitates robust hierarchical path planning. High-level exploration builds a global spatial model whose connectivity supports an effective, efficient, freespace planner, while low-level, target-driven exploration addresses areas where the global model lacks knowledge. Empirical results demonstrate successful and efficient travel in three challenging worlds.

Multi-Modal Neural Feature Fusion for Automatic Driving Through Perception-Aware Path Planning

Path planning is a significant and challenging task in the domain of automatic driving. Many applications, such as autonomous driving, robotic navigation and aircraft object tracking in complex and changing urban road scenes, need accurate and robust path planning by detecting obstacles in the forward direction. The traditional methods only rely on the path search method without considering the environmental factors, the vehicle path planning method cannot deal with the complex and changeable environment. To deal with above problems, we propose a perception-aware based multi-modal feature fusion approach that combines visual-inertial odometer (VIO) poses and semantic obstacles in the forward scene of vehicles to plan driving paths. The proposed method takes environment awareness as the guide and combines path search algorithm to realize path optimization task in complex environment. The proposed approach first uses a long short memory network (LSTM) to build a VIO that fuses visual and inertial data for pose estimation. To detect obstacles, the proposed method uses a segmentation model with a lightweight structure to extract semantic 3D landmarks. Finally, a path search strategy combining an A* algorithm and visual information is proposed to plan driving paths for intelligent vehicles. We estimate the proposed path planning method on assimilated scenes and public datasets (KITTI and Cityscapes) by using a micro controller (Jetson Xavier NX) installed on a small vehicle. We also show comparable results with path planning that only uses the greedy algorithm or heuristic algorithm without using visual information and show that our method is adequate in coping with different complex scenes.

Semantic interpretation and complexity reduction of 3D point clouds of vineyards

In precision agriculture, autonomous ground and aerial vehicles can lead to favourable improvements in field operations, extending crop scouting to large fields and performing field tasks in a timely and effective way. However, automated navigation and operations within a complex scenarios require specific and robust path planning and navigation control. Thus, in addition to proper knowledge of their instantaneous position, robotic vehicles and machines require an accurate spatial description of their environment. An innovative modelling framework is presented to semantically interpret 3D point clouds of vineyards and to generate low complexity 3D mesh models of vine rows. The proposed methodology, based on a combination of convex hull filtration and minimum area c-gon design, reduces the amount of instances required to describe the spatial layout and shape of vine canopies allowing the amount of data to be reduced without losing relevant crop shape information. The algorithm is not hindered by complex scenarios, such as non-linear vine rows, as it is able to automatically process non uniform vineyards. Results demonstrated a data reduction of about 98%; from the 500 Mb ha−1 required to store the original dataset to 7.6 Mb ha−1 for the low complexity 3D mesh. Reducing the amount of data is crucial to reducing computational times for large original datasets, thus enabling the exploitation of 3D point cloud information in real-time during field operations. When considering scenarios involving cooperating machines and robots, data reduction will allow rapid communication and data exchange between in field actors.

Robust Hierarchical Model Predictive Control for Trajectory Tracking with Obstacle Avoidance

A robust hierarchical path planning and trajectory tracking framework is proposed to maintain a collision-free path for autonomous vehicles. For the path-planning, a constrained finite-time optimal problem is solved to generate a feasible and collision-free trajectory considering the vehicle kinematics. For the trajectory-tracking, a motion controller is proposed by solving a constrained model predictive control problem, obtaining the front wheel steering angles. Furthermore, to enhance the robustness of the motion controller against the uneven network-induced time delay and unmodelled lateral vehicle dynamics, an additional error feedback mechanism is introduced in the motion controller. Simulations are conducted when both moving obstacles and static obstacles exist. Simulation results show that the proposed hierarchical control framework can effectively guarantee safe and feasible driving maneuvers.

Ultrasound navigation based on minimally designed vehicle inspired by the bio-sonar strategy of bats

ABSTRACTIn this study, we conducted vehicle experiments and a numerical simulation based on a simple algorithm inspired by the bio-sonar system of bats to investigate how the behavioral strategy employed by bats contributes to acoustic navigation for minimal-design sensing. In particular, a double-pulse scanning method inspired from the echolocation behavior of bats was proposed, in which (1) the direction of ultrasound emission by a vehicle equipped with 1 transmitter and 2 receivers was alternately shifted between the movement direction of the vehicle and the direction of the nearest obstacle, and (2) the movement direction of the vehicle was calculated for every double-pulse emission based on integrated information from all echoes detected. As a result of 100 repeated drives in a practical course, the success rate of an obstacle-avoidance drive improved from 13% with the conventional single-pulse scanning method to 73% with the proposed method. Furthermore, the numerical simulation demonstrated that the proposed method achieves robust path planning by suppressing the localization ambiguity due to the interference of multiple echoes. The practical experiments and numerical simulation suggest that bats employ a simple behavioral solution in the operation of acoustic sensing for various problems occurring in the real world.