Local Path Planning Research Articles

Vision-Based Virtual Simulation Platform for Planetary Rovers

In order to evaluate the feasibility of planetary exploration missions, it is imperative to construct planetary terrain environments on the ground. Nevertheless, the implementation of this method is characterized by time-consuming and challenging factors. By contrast, resorting to simulation approaches represents a cost-effective and high-efficiency alternative, which can facilitate the simulation validation of planetary rover exploration missions in an effective manner. In this paper, a modular planet rover simulation platform is proposed; by building and overlaying multiple feature layers corresponding to the surface of the planet, we realize the simulation of high-resolution fine terrain and through adjusting the terrain parameters to meet the needs of different simulated terrain. The experimental results show that we have built a scene to satisfy the requirements of visual effects and physically realistic characteristics of simulation. Using an improved PatchMatch stereomethod to recover images captured real time by a planetary rover navigation camera in a virtual environment provided more complete three-dimensional terrain data for subsequent simulation validation of local path planning. Finally, a simulation environment that combines high-fidelity visual effects and kinematic characteristics supports visualizing the simulation platform: we propose a path planning method using global planning combined with local obstacle avoidance, and we obtain the optimal path that satisfies the kinematic constraints.

Heuristic dynamic path planning algorithm based on SALSTM-DDPG

In the field of path planning, there are many excellent traditional algorithms proposed by scholars, but usually these traditional methods based on static environment, these methods lack the ability to deal with dynamic variable environment. A heuristic reinforcement learning dynamic environment path planning algorithm (SALSTM-DDPG) combining self-attention and long short-term memory network(LSTM) is proposed in this paper. Firstly, an automatic encoder is constructed to reduce the feature dimension of the local cost map, which reduces the complexity of the overall model. Then the local cost map and global path after dimensionality reduction are taken as input, the original source of information is guaranteed to the maximum extent possible by this method, at the same time, the global optimal path is used to guide the local path planning. Finally, the deep reinforcement learning algorithm DDPG is used for specific motion control, the Actor part is built using a network based on the SALSTM algorithm. This approach allows previous sequence to be used as a reference when an Actor makes decisions, this enables dynamic obstacles to be predictably avoided by the robot. The feasibility and efficiency of the proposed algorithm are proved by experiments.

A Review of Path Planning for Unmanned Surface Vehicles

With the continued development of artificial intelligence technology, unmanned surface vehicles (USVs) have attracted the attention of countless domestic and international specialists and academics. In particular, path planning is a core technique for the autonomy and intelligence process of USVs. The current literature reviews on USV path planning focus on the latest global and local path optimization algorithms. Almost all algorithms are optimized by concerning metrics such as path length, smoothness, and convergence speed. However, they also simulate environmental conditions at sea and do not consider the effects of sea factors, such as wind, waves, and currents. Therefore, this paper reviews the current algorithms and latest research results of USV path planning in terms of global path planning, local path planning, hazard avoidance with an approximate response, and path planning under clustering. Then, by classifying USV path planning, the advantages and disadvantages of different research methods and the entry points for improving various algorithms are summarized. Among them, the papers which use kinematic and dynamical equations to consider the ship’s trajectory motion planning for actual sea environments are reviewed. Faced with multiple moving obstacles, the literature related to multi-objective task assignment methods for path planning of USV swarms is reviewed. Therefore, the main contribution of this work is that it broadens the horizon of USV path planning and proposes future directions and research priorities for USV path planning based on existing technologies and trends.

Prioritising paths: An improved cost function for local path planning for UAV in medical applications

AbstractEven the shortest flight through unknown, cluttered environments requires reliable local path planning algorithms to avoid unforeseen obstacles. The algorithm must evaluate alternative flight paths and identify the best path if an obstacle blocks its way. Commonly, weighted sums are used here. This work shows that weighted Chebyshev distances and factorial achievement scalarising functions are suitable alternatives to weighted sums if combined with the 3DVFH* local path planning algorithm. Both methods considerably reduce the failure probability of simulated flights in various environments. The standard 3DVFH* uses a weighted sum and has a failure probability of 50% in the test environments. A factorial achievement scalarising function, which minimises the worst combination of two out of four objective functions, reaches a failure probability of 26%; A weighted Chebyshev distance, which optimises the worst objective, has a failure probability of 30%. These results show promise for further enhancements and to support broader applicability.

Multi-UAV Cooperative Obstacle Avoidance of 3D Vector Field Histogram Plus and Dynamic Window Approach

In this paper, we propose a fusion algorithm that integrates the 3D vector field histogram plus (VFH+) algorithm and the improved dynamic window approach (DWA) algorithm. The aim is to address the challenge of cooperative obstacle avoidance faced by multi-UAV formation flying in unknown environments. First, according to the navigation evaluation function of the standard DWA algorithm, the target distance is introduced to correct the azimuth. Then, aiming at the problem that the fixed weight mechanism in standard DWA algorithm is unreasonable, we combine the A* algorithm and introduce variable weight factors related to azimuth to improve the target orientation ability in local path planning. A new rotation cost evaluation function is added to improve the obstacle avoidance ability of two-dimensional UAV. Then, 3D VFH+ algorithm is introduced and integrated with improved DWA algorithm to design a distributed cooperative formation obstacle avoidance control algorithm. Simulation validation suggests that compared with the traditional DWA algorithm, the improved collaborative obstacle avoidance control algorithm can greatly optimize the obstacle avoidance effect of UAVs’ formation flight.

Research on the local path planning of an orchard mowing robot based on an elliptic repulsion scope boundary constraint potential field method.

In orchard scenes, the complex terrain environment will affect the operational safety of mowing robots. For this reason, this paper proposes an improved local path planning algorithm for an artificial potential field, which introduces the scope of an elliptic repulsion potential field as the boundary potential field. The potential field function adopts an improved variable polynomial and adds a distance factor, which effectively solves the problems of unreachable targets and local minima. In addition, the scope of the repulsion potential field is changed to an ellipse, and a fruit tree boundary potential field is added, which effectively reduces the environmental potential field complexity, enables the robot to avoid obstacles in advance without crossing the fruit tree boundary, and improves the safety of the robot when working independently. The path length planned by the improved algorithm is 6.78% shorter than that of the traditional artificial potential method, The experimental results show that the path planned using the improved algorithm is shorter, smoother and has good obstacle avoidance ability.

비정형 환경의 모바일 로봇을 위한 보조 도착점 기반 계층적 경로계획 프레임워크

Navigating unstructured environments poses challenges for mobile robots. Because such environments are partially observable, ground mobile robots must consider both global traversability and local obstacles. In this paper, we proposed a subgoal-based hierarchical motion planning framework for mobile robots to navigate unstructured environments. This framework comprises a global path planner, a subgoal planner, and a local path planner. This framework enables the mobile robot to effectively avoid local obstacles that cannot be represented on the global map. The hierarchical framework plans subgoals in local coordinates to avoid obstacles when following the planned global path. The local planner utilizes subgoals to generate local paths. To evaluate the proposed framework, we constructed a simulated unstructured terrain environment with diverse obstacles. The hierarchical framework exhibited better and safer quantitative performance than a conventional global path-based method, and the mobile robot successfully navigated unstructured environments by using the hierarchical framework while avoiding obstacles in local coordinates in real time.

Dynamic path-speed planning algorithm for autonomous driving on structured roads

Aiming at the problem of local path planning for structured roads, this paper proposes a framework of local path-speed planning for autonomous driving, which takes safety as the premise and improves driving efficiency. The framework simulates human driving thinking and divides the local path planning of autonomous driving into two parts: lane decision and path-speed planning. In the part of the lane decision, a lane decision algorithm based on driving risk field and safe distance is proposed, which can ensure driving efficient and ensure that the planning vehicle is always in a low-risk driving environment. In the part of the lane change path-speed planning, a candidate path generation algorithm based on uniform sampling of lane change time and a cost function considering lane change timeliness, driving safety, speed smoothness, and path continuity are proposed to achieve optimal path selection and speed planning. In the experiment part, there are six different driving tasks. In six scenes, the local path-speed planning framework proposed in this paper can plan a safe, efficient, and smooth driving path and a safe planning speed. Taking the scenario of detouring low-speed obstacles as an example, the path-speed planning algorithm proposed is compared with the path-speed planning algorithm based on discrete optimization in Hu et al. It has been verified that the algorithm proposed can ensure that planner is always at low environmental risks and drive with high driving efficiency.

A real-time local path planning algorithm for the wave glider based on time-stamped collision detection and improved artificial potential field

The local path planning technology of wave gliders (WGs) is faced with two inherent difficulties: the impact of the complex marine environment on the path and the weak maneuverability. In this paper, a fusion algorithm based on the time-stamped collision detection (TCD) and the environmental improved artificial potential field (EAPF) is proposed to solve the above problems, which combines the dynamic model of the WG to guide it by generating a desired heading output. The improved APF (IAPF) is developed to solve the problems of local minimum and target unreachability. The influence of the complex marine environment on the WG is considered during dynamic obstacle avoidance, and the proposed EAPF improves the maneuverability of the WG with the help of environmental field, while reducing the impact of the environment on the path. For dynamic obstacle prediction, different dynamic obstacle models are used, and the TCD is proposed to predict the collision risk between WG and dynamic obstacles to adapt to the weak maneuverability of the WG. The simulation results show that the proposed TCD-EAPF can realize the early avoidance of dynamic obstacles and improve the maneuverability of the WG in obstacle avoidance.

A new hybrid path planning method for the sailboat architecture wave glider in the wind field environment

To ensure efficient sailing of the SWG in complex marine environments and maintain its controllability in upwind or downwind conditions, a hybrid path planning method is proposed aiming at path time consumption and safety in this paper. The method includes a weight heuristic A* algorithm and a WAPF algorithm. The kinematic and dynamic models of the SWG are established, and the rotary diameter, velocity polar diagram, rotation constraint and dangerous wind angle constraint are obtained. To make better use of the fixed wind field environment and improve the efficiency of the algorithm, a weight heuristic A* algorithm is proposed for global path planning. The algorithm uses the form of weight-heuristic and reduces useless search directions. To ensure navigation safety and overcome the shortcomings of traditional APF algorithm, a WAPF algorithm for local path planning is proposed by introducing kinematic constraints and wind potential field into the algorithm model. Simulations are carried out and the results show that the proposed weight heuristic A* algorithm and WAPF algorithm are more suitable for the SWG navigation in the wind field environment than traditional path planning algorithms. The effectiveness of the WAPF algorithm is verified by sea trial and simulation comparison.

Improved reinforcement learning for collision-free local path planning of dynamic obstacle

The rapid development of artificial intelligence has driven the transformation of traditional industry. The proposal for an intelligent ship reflects the development of the shipping industry in the direction of intelligence. Safe maritime transportation is an essential branch of the intelligent ship industry. In this context, applying various intelligent algorithms in ship dynamic obstacle avoidance has attracted the attention and discussion of scholars. Due to the complexity and diversity of ship navigation, environments cannot be described by a definite mathematical model. Reinforcement learning has certain advantages in solving path planning in a complex environment. Traditional mathematical algorithms and swarm intelligence algorithms need mathematical models to constrain boundary conditions when solving complex problems. However, some complex dynamic environments in the real world cannot define mathematical models. This paper optimizes the parameter update and exploration strategies of the actor–critic algorithm in reinforcement learning to improve the algorithm’s performance. Under complex meteorological conditions, improved reinforcement learning is applied to the dynamic ship offshore channel simulation environment to verify the algorithm’s performance.

Feature selection-based decision model for UAV path planning on rough terrains

Path planning and obstacle avoidance in 3D terrain have been identified as a monumental challenge for a UAV in a variety of autonomous missions, such as disaster management, and search and rescue operations. In large terrain areas, it is a key problem for traditional approaches to search within the point-cloud maps to find a global path for a UAV considering the flight safety, maneuverability, weather constraints, and fuel cost. Hence, this paper proposes a trajectory planning technique for global and local path planning of a fixed-wing UAV above 3D terrain under static and dynamic constraints. For global path generation, a novel feature selection-based decision model has been proposed to select the features of a point-cloud map and transform them into the feature set. The feature set is utilized by an A* multi-directional planner with an extensive search area to deliver an optimal global path. The global path is assumed as the UAV’s reference waypoints. The motion of the UAV on reference waypoints is simplified with two coordinates (R,d), where R is the cumulative distance covered by the UAV along the reference waypoints and d is its offset distance from the reference line segment in time t. For the local path planning, offset trajectories are generated along with reference waypoints to avoid collisions. Cost functions have been added so that the best global and local path can be chosen, taking into account altitude, weather, and fuel constraints. The simulation results and comparison show that the proposed approach outperforms various other 3D UAV path planning techniques in complex terrain.

ROS-based Library Book Precise Positioning Guidance Robot

In order to solve the problem of book search and positioning in the library, this paper introduces a library book precise positioning guidance robot based on robot operating system (ROS). The robot has functions such as synchronous positioning and mapping, autonomous navigation and obstacle avoidance. First of all, the library book precision positioning guidance robot uses sensors such as lidar and cameras to realize the perception of the library environment, and at the same time uses image recognition technology to identify books. Secondly, the two-dimensional raster map of the library is drawn by using the SLAM algorithm, and the global and local optimal path planning and obstacle avoidance function are realized by using the Move-base function package, so as to realize the navigation and positioning tasks in the library.

A path planning algorithm for mobile robot based on water flow potential field method and beetle antennae search algorithm

Aiming at the problems that beetle antennae search algorithm is difficult and easy to avoid obstacles when solving path planning problems, and falls into local optimization, which leads to low efficiency, a path planning algorithm of mobile robot combining the water flow potential field method and the beetle antennae search is proposed. Path planning divides the global path into segments by setting up segmented sites by using the beetle genetic operator. Local path planning between sites is classified according to the characteristics of obstacles, and the artificial potential field method is used to guide the search. The nature of water flow method is used to plan the obstacle avoidance route, optimize the obstacle avoidance process, and effectively avoid falling into local trap obstacles. Finally, the site coordinates are optimized by the beetle antennae search algorithm to improve the path quality and prevent the path from falling into local optimum. The simulation results show that the algorithm can effectively avoid obstacles in various path planning environments, has the characteristics of short time consumption, good optimization effect, and is not easy to fall into local optimization, and is an efficient algorithm for solving the path planning problem of mobile robots.

Mobile Robot Path Planning Based on Improved Particle Swarm Optimization and Improved Dynamic Window Approach

To enable mobile robots to effectively complete path planning in dynamic environments, a hybrid path planning method based on particle swarm optimization (PSO) and dynamic window approach (DWA) is proposed in this paper. First, an improved particle swarm optimization (IPSO) is proposed to enhance the exploration capability and search accuracy of the algorithm by improving the velocity update method and inertia weight. Secondly, a particle initialization strategy is used to increase population diversity, and an addressing local optimum strategy is used to make the algorithm overcome the local optimum. Thirdly, a method of selecting navigation points is proposed to guide local path planning. The robot selects the appropriate navigation points as the target points for local path planning based on the position of the robot and the risk of collision with dynamic obstacles. Finally, an improved dynamic window approach (IDWA) is proposed by combining the velocity obstacle (VO) with the DWA, and the evaluation function of the DWA is improved to enhance trajectory tracking and dynamic obstacle avoidance capabilities. The simulation and experimental results show that IPSO has greater exploration capability and search accuracy; IDWA is more effective in trajectory tracking and dynamic obstacle avoidance; and the hybrid algorithm enables the robot to efficiently complete path planning in dynamic environments.

High-Efficiency Navigation of Nonholonomic Mobile Robots Based on Improved Hybrid A* Algorithm

With the development of automation technologies, autonomous robots are increasingly used in many important applications. However, precise self-navigation and accurate path planning remain a significant challenge, particularly for the robots operating in complex circumstances such as city centers. In this paper, a nonholonomically constrained robot with high-precision navigation and path planning capability was designed based on the Robot Operating System (ROS), and an improved hybrid A* algorithm was developed to increase the processing efficiency and accuracy of the global path planning and navigation of the robot. The performance and effectiveness of the algorithm were evaluated by using randomly constructed maps in MATLAB and validated in a practical circumstance. Local path planning and obstacle avoidance were carried out based on the model predictive control (MPC) theory. Compared with the conventional A* + DWA (dynamic window approach) method, the average searching time was reduced by 12.62~24.5%, and the average search length was reduced by 9.25~9.5%. In practical navigating tests, the average search time was reduced by 18~24%, and the average search length was reduced by 10.3~12%, while the overall path was smoother. The results demonstrate that the improved algorithm can enable precise and efficient navigation and path planning of the robot in complex circumstances.

Path Planning of an Unmanned Surface Vessel Based on the Improved A-Star and Dynamic Window Method

In order to ensure the safe navigation of USVs (unmanned surface vessels) and real-time collision avoidance, this study conducts global and local path planning for USVs in a variable dynamic environment, while local path planning is proposed under the consideration of USV motion characteristics and COLREGs (International Convention on Regulations for Collision Avoidance at Sea) requirements. First, the basis of collision avoidance decisions based on the dynamic window method is introduced. Second, the knowledge of local collision avoidance theory is used to study the local path planning of USV, and finally, simulation experiments are carried out in different situations and environments containing unknown obstacles. The local path planning experiments with unknown obstacles can prove that the local path planning algorithm proposed in this study has good results and can ensure that the USV makes collision avoidance decisions based on COLREGs when it meets with a ship.

Dynamic path planning of intelligent robot in power equipment maintenance environment

Aiming at the problem that the traditional path planning algorithm does not have real-time planning performance in the dynamic and complex power equipment maintenance environment, or it is challenging to avoid dynamic obstacle obstacles due to poor real-time version, this paper proposes an artificial potential field method for real-time planning. The characteristics of dynamic obstacle in a dynamic complex environment, the dynamic obstacles are simplified as moving particles, and the repulsion function of the dynamic obstacles is added to avoid the collision. Within the influence range of dynamic obstacles in a busy complex environment, the robot avoids the dynamic obstacles through real-time local path planning under the influence of the repulsive force of the dynamic obstacle to reach the target point. At the same time, aiming at the problems of unevenness and fluctuation of the path generated by the traditional artificial potential field method, the non-uniform rational B-spline and moving average filtering algorithm are used to improve the local path planning algorithm of the conventional artificial potential field, and the generated path is smoother and smoother than the traditional algorithm.

Drone Swarm Path Planning for Mobile Edge Computing in Industrial Internet of Things

Drone-swarm-assisted mobile edge computing (MEC) provides extra computation and storage capacity for smart city applications and the industrial Internet of Things. To solve the problems of traditional fixed base stations in complex terrain, including cost of deployment, transmission loss of telecommunication, and limited coverage, this paper brings forward the unmanned aerial vehicles (UAVs) as mobile edge computing nodes in the air. For the purpose of matching the dynamic mobile devices and UAV trajectory, this paper raises a multi-UAVs-assisted mobile edge computing offloading algorithm based on global and local path planning controlled by ground station and onboard computer. Firstly, this paper considers a drone swarm scheduling and allocation strategy based on the priority of monitoring areas, UAVs residual energy and distance to target points, so that to minimize the global flight length and energy consumption. Secondly, based on user mobility, this paper calculates the optimal communication coverage of UAV, and jointly optimizes the local path planning and computing offloading, so that to maximize the number of offloading services and minimize the total latency in completing the computation task. Finally, based on the total latency and energy consumption of path planning and computation offloading, a UAV cluster computation offloading strategy with optimized energy efficiency is realized. Experimental results prove that the proposed algorithm can provide more offloading services while obtaining shorter path length and greater energy efficiency.

Research on the influence of pilot workload on local route path planning of helicopters in low-altitude penetration

Targeting the problem of high pilot workload caused by trajectory planning of low altitude penetration, this paper proposed a trajectory planning method considering the pilot workload. Firstly, by decomposing the three-dimensional trajectory into horizontal and vertical directions, the trajectory tree method based on the dynamic programming technique is used for horizontal trajectory planning, and the comprehensive slope-curvature limitation method is used for altitude trajectory planning. Then, a method was proposed to quantify the control inputs and pilot workload based on trajectory tracking simulation. The pilot workload cost, together with flight altitude cost and route length cost, was added to the local planning of a reference trajectory. To solve the problem of dimension explosion in trajectory tree search, a segmented optimal route tree algorithm is proposed. Finally, the simulation results show that the proposed method can effectively consider the pilot workload in trajectory planning, which is helpful to improve the flight ability of the planning trajectory and reduce the pilot workload at the same time.