Autonomous Path Research Articles

The BCRobo dataset for Robotic Vision and Autonomous Path Planning in Outdoor Beach Environment

The BCRobo dataset for Robotic Vision and Autonomous Path Planning in Outdoor Beach Environment

Path Planning for Unmanned Delivery Robots Based on EWB-GWO Algorithm.

With the rise of robotics within various fields, there has been a significant development in the use of mobile robots. For mobile robots performing unmanned delivery tasks, autonomous robot navigation based on complex environments is particularly important. In this paper, an improved Gray Wolf Optimization (GWO)-based algorithm is proposed to realize the autonomous path planning of mobile robots in complex scenarios. First, the strategy for generating the initial wolf pack of the GWO algorithm is modified by introducing a two-dimensional Tent-Sine coupled chaotic mapping in this paper. This guarantees that the GWO algorithm generates the initial population diversity while improving the randomness between the two-dimensional state variables of the path nodes. Second, by introducing the opposition-based learning method based on the elite strategy, the adaptive nonlinear inertia weight strategy and random wandering law of the Butterfly Optimization Algorithm (BOA), this paper improves the defects of slow convergence speed, low accuracy, and imbalance between global exploration and local mining functions of the GWO algorithm in dealing with high-dimensional complex problems. In this paper, the improved algorithm is named as an EWB-GWO algorithm, where EWB is the abbreviation of three strategies. Finally, this paper enhances the rationalization of the initial population generation of the EWB-GWO algorithm based on the visual-field line detection technique of Bresenham's line algorithm, reduces the number of iterations of the EWB-GWO algorithm, and decreases the time complexity of the algorithm in dealing with the path planning problem. The simulation results show that the EWB-GWO algorithm is very competitive among metaheuristics of the same type. It also achieves optimal path length measures and smoothness metrics in the path planning experiments.

Autonomous Path Planning for Industrial Omnidirectional AGV Based on Mechatronic Engineering Intelligent Optical Sensors

Autonomous Path Planning for Industrial Omnidirectional AGV Based on Mechatronic Engineering Intelligent Optical Sensors

Multisensor Fusion SLAM Research Based on Improved RBPF‐SLAM Algorithm

Simultaneous localization and map construction (SLAM) technology provides the foundation for indoor robots to realize autonomous path planning. The Rao‐Blackwellized particle filtering (RBPF) algorithm is widely used to obtain information and perform map construction in unknown environments. This paper proposes a multisensor fusion algorithm that improves the RBPF‐SLAM algorithm by addressing the issues of particle distribution errors and degradation. To achieve this, the extended Kalman filter (EKF) is first adopted to effectively fuse odometry and inertial navigation (inertial measurement unit (IMU)) data as the initial positional information. Then, a high‐precision light detection and ranging (LIDAR) observation model is integrated to calculate the proposed distribution, and a threshold is introduced to determine the number of valid particles to simplify the resampling step. Finally, raster map construction experiments were carried out on both the Gazebo simulation environment and Robot Operating System (ROS)‐based mobile robot Keepbot platform in different scenarios. The experimental results demonstrate that the optimized RBPF‐SLAM algorithm generates a clearer and more complete map outline, which can effectively improve the accuracy of robot pose estimation, acquire a reliable 2D raster map with a reduced number of particles, and greatly reduces the computational effort.

Searchable Encryption With Autonomous Path Delegation Function and Its Application in Healthcare Cloud

Outsourcing medical data to healthcare cloud has become a popular trend. Since medical data of patients contain sensitive personal information, they should be encrypted before outsourcing. However, information retrieval methods based on plaintext cannot be directly applied to encrypted data. In this paper, we present a new cryptographic primitive named conjunctive keyword search with secure channel free and autonomous path delegation function (AP-SCF-PECKS), which can be applied in scenarios where patients want to search for and autonomous delegate their private medical information without revealing their private key. Particularly, the proposed solution allows patients to set up multi-hop delegation path with their preferences, and the delegated doctors in the path can search for and access the patient's private medical information with priority from high to low. Patients can ensure that authorized doctors are always trustworthy, and unauthorized users cannot obtain the private medical information of patients. Moreover, the scheme supports the conjunctive keyword search, secure channel free, and is secure against chosen keyword attack, chosen ciphertext attack, and keyword guessing attack. The security of proposed scheme has been formally proved in the standard model. Finally, the performance evaluations demonstrate that the overhead of proposed scheme are modest for healthcare cloud scenarios.

Generalized Autonomous Path Proxy Re-Encryption Scheme to Support Branch Functionality

Generalized Autonomous Path Proxy Re-Encryption Scheme to Support Branch Functionality

EFFECTIVE SOLUTIONS FOR COMMON PROBLEMS OF ARTIFICIAL POTENTIAL FIELD BASED PATH PLANNING ALGORITHMS FOR MOBILE ROBOTS

Abstract
 Autonomous Path Planning (APP) capability is one of the main factors determining the autonomous level of a mobile robot. Although different methods are used for APP in the literature, the path planning approach based on Artificial Potential Fields (APF) has a very common usage area with its modeling ease and computational performance. APF-based APP, which is a grid-based path planning approach, is usually performed by combining a repulsive and attractive component that models many basic motions with a certain equation and calculating the gradient of this potential field to obtain the vector field. In this study, the basic models used for APF-based APP are examined, and how they are realized and how the resultant potential field is produced are mentioned. Although APF-based APP approaches have advantages, they also have problems such as local minimum, obstacles positioned too close, oscillation, and targets positioned too close to obstacles. Within the scope of the study, these problems were defined one by one and the approaches suggested in the literature for the solution of these problems were mentioned in detail. As a result, it has been seen that to obtain an effective APF-based APP solution, it is necessary to generate a convolutional vector field, limit the fundamental potential fields with exponential functions, use virtual potential fields and perform models with harmonic functions.

Design and Experiments of Autonomous Path Tracking Based on Dead Reckoning

Path tracking is an important component of autonomous driving and most current path tracking research is based on different positioning sensors, such as GPS, cameras, and LIDAR. However, in certain extreme cases (e.g., in tunnels or indoor parking lots), if these sensors are unavailable, achieving accurate path tracking remains a problem that is worthy of study. This paper addresses this problem by designing a dead reckoning method that is solely reliant on wheel speed for localization. Specifically, a differential drive model is first used for estimating the current relative vehicle position in real time by rear wheel speed, and the deviation between the current path and the reference path is then calculated using the pure pursuit algorithm as a means of obtaining the target steering wheel angle and vehicle speed. The steering wheel and vehicle speed signals are then output by two PID controllers in order to control the vehicle, and the automatic driving path tracking is ultimately realized. Through exhaustive tests and experiments, the stop position error and tracking process error are compared under different conditions, and the effects of vehicle speed, look-ahead distance, starting position angle, and driving mode on tracking accuracy are analyzed. The experimental results show the average error of the end position to be 0.26 m, 0.383 m, and 0.505 m when using BMW-i3 to drive one lap automatically at speeds of 5 km/h, 10 km/h, and 15 km/h in a test area with a perimeter of approximately 200 m.

Multi-UAV Autonomous Path Planning in Reconnaissance Missions Considering Incomplete Information: A Reinforcement Learning Method

Unmanned aerial vehicles (UAVs) are important in reconnaissance missions because of their flexibility and convenience. Vitally, UAVs are capable of autonomous navigation, which means they can be used to plan safe paths to target positions in dangerous surroundings. Traditional path-planning algorithms do not perform well when the environmental state is dynamic and partially observable. It is difficult for a UAV to make the correct decision with incomplete information. In this study, we proposed a multi-UAV path planning algorithm based on multi-agent reinforcement learning which entails the adoption of centralized training–decentralized execution architecture to coordinate all the UAVs. Additionally, we introduced a hidden state of the recurrent neural network to utilize the historical observation information. To solve the multi-objective optimization problem, We designed a joint reward function to guide UAVs to learn optimal policies under the multiple constraints. The results demonstrate that by using our method, we were able to solve the problem of incomplete information and low efficiency caused by partial observations and sparse rewards in reinforcement learning, and we realized kdiff multi-UAV cooperative autonomous path planning in unknown environment.

Uncertainty-Based Autonomous Path Planning for Laser Line Scanners

This study proposes an algorithm to autonomously generate the scan path for a laser line scanner mounted on a coordinate measuring machine. The scan path is determined based on task-specific measurement uncertainty in order to prove conformance to specified tolerances. The novelty of the algorithm is the integration of measurement uncertainty. This development is made possible by recent developments for digital twins of optical measurement systems. Furthermore, the algorithm takes all the constraints of this optical measurement system into account. The proposed algorithm is validated on different objects with different surface characteristics. The validation is performed experimentally by a physical measurement system and virtually by an in-house developed digital twin. The validation proves that theoretical coverable areas are measured properly, and the method applied to the equipment used leads to adequate measurement paths that give measurements results with sufficient measurement uncertainty to prove conformance to specifications.

Research on Transformer Oil Level Monitoring System Based on Inspection Robot

Because the traditional oil level gauge transformer relies on manual regular patrol detection, it is not easy to know the state change of the oil level in real time. In recent years, the rapid development of AR/VR technology, the positioning, and the construction of maps of robots have also received widespread attention, and the oil level inspection task has gradually evolved in the direction of intelligent machine inspection. Based on this background, this paper studies the Cartographer algorithm, according to the mapping results for path planning, to achieve robot autonomous navigation of the designated location. After arriving at the destination, the user obtains the oil level by the depth camera, and uses the YOLOv4 to design an intelligent detection algorithm for oil level detection, using neural networks to learn the oil level state in various weather, and train a dedicated model. Through experimental verification, the inspection robot can complete the construction of the map and realize autonomous path navigation according to the target point, and the oil level detection system based on the YOLOv4 algorithm can achieve an accuracy of more than 90% for the recognition of oil level information, which has good applicability.

Robust Gain-Scheduling Path Following Control of Autonomous Vehicles Considering Stochastic Network-Induced Delay

This paper concerns the robust gain-scheduling control issue for autonomous path following systems with stochastic network-induced delay. Firstly, to effectively approximate the highly nonlinear tire dynamics, the linear fractional transformation formulations are employed to describe the tire cornering stiffness with a norm-bounded uncertainty. Secondly, by taking the data dropout and delay encountered in signal computation and transmission into account, a more generalized lumped delay form is proposed to unify the time-varying data dropout and network-induced delay. Moreover, a Markovian process is presented to describe the lumped delay as a stochastic distribution. Thirdly, to address the issue of varying vehicle velocity, a linear parameter varying model is established to capture vehicle lateral behaviors. Based on the stochastic stability theory, a new robust gain-scheduling path following control method is proposed for the autonomous vehicles. Finally, the experimental study is presented to bridge the gap between the theoretical and practical investigations on path following control of autonomous vehicles, and results validate the superior performance of the proposed method compared with existing works.

Path planning algorithm based on improved Bidirectional RRT

This paper studies the autonomous path planning of unmanned vehicles in a known driving environment. The path planning algorithm used is a hybrid algorithm combining bidirectional fast random search tree and artificial potential field method. On the basis of introducing the principle of the two algorithms, an improved algorithm combining Bi-RRT and artificial potential field is proposed. In order to reflect the advantages of the improved algorithm, the traditional RRT and Bi-RRT algorithms are simulated and compared in Matlab.

Autonomous path planning for robot-assisted pelvic fracture closed reduction with collision avoidance.

Robot-assisted pelvic fracture closed reduction (RPFCR) positively contributes to patient treatment. However, the current path planning suffers from incomplete obstacle avoidance and long paths. A collision detection method is proposed for applications in the pelvic environment to improve the safety of RPFCR surgery. Meanwhile, a defined orientation planning strategy (OPS) and linear sampling search (LSS) are coupled into the A* algorithm to optimise the reduction path. Subsequently, pelvic in vitro experimental platform is built to verify the augmented A*algorithm's feasibility. The augmented A* algorithm planned the shortest path for the same fracture model, and the paths planned by the A* algorithm and experience-based increased by 56.12% and 89.02%, respectively. The augmented A* algorithm effectively improves surgical safety and shortens the path length, which can be adopted as an effective model for developing RPFCR path planning.

The Construction of Robot Experiment Teaching Course for the Cultivation of Outstanding Talents in Intelligent Manufacturing

With the continuous progress of science and technology, intelligent manufacturing has become one of the main directions of industrial development, and training outstanding talents for it has become a historical mission that relevant universities must complete. As an important basis for realizing intelligent manufacturing, robot learning and application naturally become a key link in the cultivation of outstanding talents in intelligent manufacturing. On the basis of theoretical learning, the construction of robot experimental teaching courses can truly cultivate outstanding talents with practical application ability. This paper takes the construction of "Autonomous Navigation and Obstacle Avoiding Mobile Robot System Design Experiment" as an example to analyze how to build a student centered, interest oriented, practice based, and education oriented robot experiment teaching course that combines reality with fiction. This experimental course is based on scientific research projects, built around the student center, and oriented to teaching needs. It includes robot autonomous positioning and navigation, autonomous path planning, full coverage path planning, etc. Its experimental content, teaching methods, and concept design can be applied to the construction of similar experimental courses, with great promotion and application value.

An Optimized Probabilistic Roadmap Algorithm for Path Planning of Mobile Robots in Complex Environments with Narrow Channels.

In this paper, we propose a new path planning algorithm based on the probabilistic roadmaps method (PRM), in order to effectively solve the autonomous path planning of mobile robots in complex environments with multiple narrow channels. The improved PRM algorithm mainly improves the density and distribution of sampling points in the narrow channel, through a combination of the learning process of the PRM algorithm and the APF algorithm. We also shortened the required time and path length by optimizing the query process. The first key technology to improve the PRM algorithm involves optimizing the number and distribution of free points and collision-free lines in the free workspace. To ensure full visibility of the narrow channel, we extend the obstacles through the diagonal distance of the mobile robot while ignoring the safety distance. Considering the safety distance during movement, we re-classify the all sampling points obtained by the quasi-random sampling principle into three categories: free points, obstacle points, and adjacent points. Next, we transform obstacle points into the free points of the narrow channel by combining the APF algorithm and the characteristics of the narrow channel, increasing the density of sampling points in the narrow space. Then, we include potential energy judgment into the construction process of collision-free lines shortening the required time and reduce collisions with obstacles. Optimizing the query process of the PRM algorithm is the second key technology. To reduce the required time in the query process, we adapt the bidirectional A* algorithm to query these local paths and obtain an effective path to the target point. We also combine the path pruning technology with the potential energy function to obtain a short path without collisions. Finally, the experimental results demonstrate that the new PRM path planning technology can improve the density of free points in narrow spaces and achieve an optimized, collision-free path in complex environments with multiple narrow channels.

Rmap+: Autonomous Path Planning for Exploration of Mobile Robot Based on Inner Pair of Outer Frontiers

Rmap+: Autonomous Path Planning for Exploration of Mobile Robot Based on Inner Pair of Outer Frontiers

Autonomous path planning with obstacle avoidance for smart assistive systems

Given the increased interest in smart assistive technologies and autonomous robot vehicles, path planning has emerged as one of the most researched and challenging topics in navigation. Moving to partially known or unknown environment, an assistive navigation system should be able to extract spatiotemporal information and dynamically identify objects and adjust the route. Current approaches typically rely on external services to perform high demanding computations and employ a plethora of overlapping sensors to accurately scan the surrounding environment. This increases their energy demands, size and weight, while incommodes their use in real time applications making their application to wearable assistive systems, such as smart glasses, a challenge. Aiming to provide a comfortable and computationally efficient wearable solution that can be used by human or robotic assistive systems, in this study we propose a novel two-level hierarchical architecture combining global and local path planning. The macroscale navigation involves the construction of the initial global path while the microscale navigation includes the local path planning with obstacle detection and avoidance. The methodology consists of: (i) a novel chaotic ant colony optimization algorithm with fuzzy logic (CACOF) for path construction; (ii) powerful and light weight deep convolutional neural networks for obstacle detection; and (iii) a Bug-like algorithm enhanced with fuzzy rules for obstacle avoidance in case of static objects. A vast experimental evaluation was conducted to test the proposed methodologies in a simulation environment based on the topology of real area. The results proved the computational efficiency and ability of the proposed path planning algorithms to address effectively multi-objective global and path planning problems which make them suitable for real time applications.

Dynamic path planning of a three-dimensional underwater AUV based on an adaptive genetic algorithm

Aiming at the problems of low global path quality and poor dynamic obstacle avoidance performance in underwater three-dimensional AUV autonomous path planning, an AUV global path planning method based on an adaptive genetic algorithm (AGA) is proposed in this paper. First, the traditional genetic algorithm are optimized to reduce the time of global path generation and improve the quality of global path generation. The path optimization strategy based on the collision detection mechanism is used to optimize the global path. Then, a local obstacle avoidance strategy based on the vector artificial potential field method (VAPF) is proposed. The space vector method is used to improve the calculation method of the resultant force direction to improve the calculation efficiency of the algorithm. Finally, the key path point is used as the local target point, and local obstacle avoidance is carried out under the guidance of the vector artificial potential field method. The simulation results show that the adaptive genetic algorithm can generate effective and high-quality global paths in a three-dimensional underwater environment. The new obstacle avoidance strategy can make AUVs effectively avoid all kinds of obstacles and reduce the obstacle avoidance cost of AUVs.

Path reinforcement learning planning of nuclear power plant UAVs based on dynamic target probability distribution

This work develops a path reinforcement learning planning algorithm for unmanned aerial vehicles (UAVs) based on the probability distribution of dynamic targets. The proposed algorithm enables the effective interception of aerial dynamic invasive targets into nuclear power plants. In accordance with the status information of the invasive target, the target probability distribution is calculated by the probability diffusion principle and the target's possible location is deduced based on the obtained distribution. On this basis, the action space based on path point transfer rules is established and the dynamic updating mechanism of the reward function based on the target probability distribution is designed. Using $Q$-learning for continuous path optimization, a UAV path planning framework based on the target probability distribution and reinforcement learning is constructed, so as to realize the UAV path planning. Simulation results indicate that the proposed method can enable the autonomous path planning of UAVs for intercepting aerial dynamic invasive targets into nuclear power plants when the target point changes.