Towards Comparison and Real Time Implementation of Path Planning Methods for 2R Planar Manipulator with Obstacles Avoidance

Aiming at the problem of multi-objective path planning (MOPP) and dynamic obstacle avoidance of manipulator in dynamic environment, this paper proposes a solution based on hierarchical reinforcement learning (HRL) framework. Traditional path planning methods have some problems in dynamic scenes, such as poor real-time performance, difficult to balance multi-objective conflicts and insufficient adaptability to environmental changes. Therefore, this paper designs a two-tier architecture including global path planning layer and local obstacle avoidance layer, which is trained by Proximal policy optimization (PPO) and Soft Actor-Critic (SAC) algorithms respectively, and achieves collaborative optimization through an efficient information exchange mechanism between layers. At the same time, a multi-objective reward function based on dynamic weight adjustment strategy is introduced. Fuzzy logic is used to adaptively balance the relationship among path length, obstacle avoidance safety and energy consumption according to environmental complexity. Combined with Long Short-Term Memory (LSTM), the trajectory of obstacles is predicted, and the potential field method is further introduced to modify the obstacle avoidance reward, which improves the real-time response ability and robustness of the algorithm in dynamic environment. The experimental results show that the HRL-SAC-PPO method proposed in this paper shows superior performance in both static and dynamic scenarios. In the static scene, the success rate of this method reaches 100%, the average path length is shortened to 2.13m, no collision occurs, and the energy consumption is reduced to 1.12 kJ, which shows a good multi-objective optimization effect. In the dynamic scene, the trajectory error of obstacles predicted by LSTM is only 4.2%, and the safe distance between the robot arm and obstacles is improved by 35%, which significantly enhances the reliability of obstacle avoidance. In addition, the average decision delay of this method is only 11.3ms, and the peak delay is 23ms, which is much lower than that of the contrast algorithm, showing stronger real-time response ability. The ablation experiment further verified the key role of LSTM trajectory prediction, dynamic weight adjustment and layered structure on the overall performance. In the welding task verification of the real UR5 manipulator, the success rate of the system in dynamic environment is 95.3%, the average path length is 3.41m, and the maximum joint acceleration is only 0.87 radian/s², which is far below the safety threshold, indicating that the algorithm has good stability and obstacle avoidance ability in practical application. The comprehensive performance comparison shows that this method performs well in different industrial scenarios, and has stronger environmental adaptability and comprehensive path planning ability.

The article deals with the high-energy consumption of excessive anthropomorphic robots manipulators problem. Among the existing ways of solving this problem, solving the problems of energy-efficient traffic control methods of are chosen. Earlier on this topic, the authors developed mathematical methods for energy-efficient planning of the path of the anthropomorphic robot manipulator in the working area with a stationary obstacle. Earlier on this topic, the authors developed mathematical methods for energy-efficient motion path planning of the anthropomorphic robot manipulator in the working area with a stationary obstacle. The proposed methods have reduced computational complexity for real-time operation mode. The principle of the presented mathematical apparatus operation is to construct a virtual rectilinear energy-efficient path for moving the manipulator effector to a given position and then iteratively pushing the manipulator links out of the obstacle area to build the necessary movement route. The developed methods can be modified according to the necessary criteria of motion manipulator quality. This article presents the results of the new algorithm energy efficiency analysis that combines the previously described methods. A comparison with the selected analogue, the principle of which is based on the representation of objects in the form of oriented connected containers, is accomplished. The results of an experiments series on the simulation model implemented in MatLab show a reduction in the energy consumption of manipulators when performing targeted operations in the working area with or without an obstacle. A promising area of further research is the modification of the proposed mathematical apparatus for working in a zone with multiple stationary and dynamic obstacles, as well as the elimination of collisions when working with a human.

According to the requirements of real-time obstacle avoidance of unmanned vehicles, an improved artificial potential field local obstacle avoidance path planning algorithm is proposed. Considering the influence factors of vehicle kinematics and dynamics, an improved distance adjustment factor is added to the static obstacle potential field function to construct the water drop repulsion potential field, which improves the efficiency of path planning; The relative velocity function and relative acceleration function are added to the potential field function of dynamic obstacles, which solves the problem that the traditional potential field method has no solution under the dynamic obstacle avoidance condition. The Bessel curve is used to smooth the path, and finally a fast, efficient and collision-free optimal path is generated. Using the Prescan-CarSim-Matlab/Simulink joint simulation platform, the effectiveness of the proposed path planning algorithm is verified under the conditions of lane changing obstacle avoidance of static obstacles and deceleration obstacle avoidance of lateral dynamic obstacles. The simulation results show that compared with the traditional potential field method, the change of heading angle obtained by the improved potential field method is reduced by 84% and the stability of dynamic obstacle avoidance is improved.

Robotic Navigation is a crucial issue for any robotic based automation. In order to implement a seamless robotic navigation, path planning is a key challenge in robotic navigation. Several algorithms exist in literature on robotic path planning of static and dynamic obstacles. In this work, an approach called Modified Temporal Difference Learning for path planning and obstacle avoidance is proposed for static obstacles. The algorithm is developed in MATLAB software and path planning is implemented in a 4 × 4 grid environment. A GUI for the same is created, which access the user inputs like obstacle number, positions and type. The developed algorithm is compared with the conventional path planning Dijkstra's algorithm in the same environment. It is observed that computational complexity is less in the proposed approach compared to the conventional method.

The proliferation of unmanned aerial vehicles (UAVs) has driven the emergence of urban air mobility (UAM) as a solution to alleviate the increasing demand for UAVs applications in various domains. The concept of UAM envisions a future where UAVs play a pivotal role in short-distance transportation, delivery services, surveillance, emergency rescue, and more. However, the rapid growth of UAVs poses significant challenges arising from the need to ensure the safe coexistence of UAVs with other manned/unmanned aerial vehicles. To ensure the safe integration of UAVs into low-altitude urban airspace and support high-density on-demand mobility, this research focuses on handling the safety-centric challenges related to UAV collision risk assessment, obstacle avoidance, and conflict resolution in complex urban environments.
\nOne of the main challenges is the intrusion of UAVs into airport runways. Airports serve as critical hubs for civil aircraft takeoff and landing operations. However, the increasing presence of UAVs has raised concerns about intruding into airports, leading to flight delays and temporary runway closures. To quantify the potential interference of UAVs on civil aircraft, a collision-course based trajectory planning model is proposed to evaluate the collision risk between manned civil aircraft and intruding UAVs. In this risk assessment model, the trajectory of the intruding UAV is formulated using a collision-course concept, taking into account the limited trajectory information available about intruding UAVs. The planned trajectory of the UAVs is then used to predict its relative position concerning civil aircraft. To account for the possible disturbances of the relative distance, a stochastic position prediction model based on Brownian motion is developed. The collision risk is assessed based on this stochastic kinematic model, providing a quantitative measure of the potential collisions. The proposed model is validated through extensive simulations involving different UAV initial positions, position updates, and different sizes of collision zone tests, as well as comparative tests with the Monte-Carlo algorithm, demonstrating its robustness in assessing the collision risk of the airport restricted area. To facilitate the application, the model is finally used to develop a platform to generate runway heatmaps, accounting for airport runway risk distributions. 
\nAnother safety-centric challenge is ensuring autonomous obstacle avoidance and conflict resolution of UAVs. To enable UAV autonomous safe operations in uncontrolled urban low-altitude airspace, a comprehensive collision-free path planning framework is developed to address the avoidance of both static obstacles and dynamic threats. Firstly, to handle static obstacles, a novel algorithm of 3D voxel jump point research is introduced to generate a global reference path for UAV navigation to avoid static obstacles, particularly for a free flight at different altitudes. Additionally, to optimize the reference path for specific mission requirements, techniques such as trajectory de-diagonalization, reconstruction, and smoothness are proposed to reduce the risk of static collisions, shorten the fly range, and enable UAVs to negotiate turns more efficiently. Secondly, real-time conflict resolution actions based on the Markov decision process are developed to avoid local dynamic threats encountered along the optimized desired path. This allows the host UAV to dynamically adjust its course to a resolution trajectory while maintaining its planned speed without hovering to avoid the potential collision with the approaching intruders. By avoiding near mid-air collisions (NMAC) and preventing the UAV from hovering, the proposed method ensures the timely completion of UAV missions. Simulation results demonstrate that the developed method successfully achieves UAV autonomous global path planning and local real-time conflict resolution in complex urban low-altitude environments, thereby avoiding both static obstacles and dynamic threats. 
\nMoreover, to bridge the research gap of UAV hard conflict resolution in mitigating NMAC risks arising from local dynamic threats, a novel situational NMAC risk assessment approach is developed by integrating the conditional random field (CRF) algorithm with safety metrics and conflict resolution performance to facilitate UAV tiered aerial passage operations. To improve UAV operating environments in urban airspace, a concept of tiered aerial passages is first designed to provide a practical urban airspace planning scheme that addresses UAV heterogeneity and optimizes the utilization of urban airspace. For UAV tiered aerial passage operations, a situational NMAC risk assessment module coupled with a conflict resolution decision-making module is formulated, enabling the host UAV to evaluate and respond to real-time conflicts, ensuring avoidance of potential collisions. To assess NMAC risks, a general risk assessment model is proposed based on the CRF algorithm, considering UAV safety metrics and resolution performance. Then the overtaking, approaching head-on, and converging UAV encounters are analyzed, and situational risk potential scores are created using the Gaussian kernel function in the CRF algorithm, considering advisory, caution, and warning risk alert levels. To resolve conflict, acceptable, tolerable, and unacceptable scenarios are proposed to enhance the flexibility and personalization of the resolution decision-making. The proposed scheme is validated by simulating geometric encounter situations and comparative tests with the previous hard resolution technique. The results indicate that the proposed method outperforms UAV NMAC risk assessment and conflict resolution. Even in the most dangerous unacceptable resolution decision-making scenarios across three conflict encounters, the proposed algorithm achieves NMAC rates of 0.036, 0.070, and 0.043, which are lower than the corresponding NMAC rates associated with the hard resolution approach. Together with the acceptable and tolerable scenarios, the proposed negotiation resolution effectively tailors different risk requirements to accommodate diverse UAV missions. 
\nWith a primary focus on ensuring the safety of the UAM architecture, this research endeavors to assess UAV NMAC risks and efficiently manage potential conflicts. The proposed work are thoroughly validated to demonstrate their effectiveness in real-world scenarios. As a result, this study significantly contributes to the establishment of a robust UAM architecture, facilitating the realization of safe and reliable UAM.

Group animation technology is widely used in the fields of virtual reality, intelligent simulation and game development, the core of which lies in simulating the autonomous movement and interaction behaviour of large-scale intelligent bodies in complex environments. However, there is still room for optimising the motion coordination and environment adaptability of intelligent bodies. In order to improve the performance of group animation in complex environments, a group animation algorithm combining the firefly algorithm and the leader-follower mechanism is designed. By combining global optimisation and local cooperative control, the path planning and motion control of the intelligent body are optimised. The experimental results show that in a small-scale scene with a dataset size of 50, the path smoothness of the proposed algorithm under the study is 0.94, the average deviation of the intelligent body is reduced to 0.92m, and the growth rate of the path length is 8.30%. In a 50 × 25 smart body and obstacle environment, the algorithm’s path smoothness is improved to 0.91, the average deviation of the smart body is reduced to 1.36m, and the path growth rate is reduced to 10.7%. And the success rate of obstacle avoidance of the algorithm is still maintained at 0.92 when the size of the dataset is increased to 600.The study shows that the algorithm has better performance in path planning and obstacle avoidance in complex environments. The proposed algorithm provides an efficient, stable and adaptable solution for group animation control in virtual reality.

Efficiency of manipulator path planning and obstacle avoidance are crucial technologies in the field of manipulator research. Conventional manipulator path planning algorithms have deficiencies such as low efficiency and unstable planned paths. To address this, this paper proposes an improved joint space three-dimensional Astar algorithm with pre-planning strategy. Firstly, we present a pre-planning strategy to generate new start point and target point that can reduce computational load. Next, we conduct joint space three-dimensional Astar path planning, which enables us to circumvent a multitude of forward and inverse kinematics computations as well as singularities, thereby diminishing the amounts of high-dimensional calculations. In addition to enabling the manipulator end-effector to avoid obstacles, we also achieved the obstacle avoidance for manipulator link. In simulation and experiments, the effectiveness of proposed path planning algorithm is validated and in contrast to the traditional Astar, RRT, and Dijkstra algorithms, the path generated exhibits a stronger tendency towards optimality.

Efficiency in manipulator path planning and obstacle avoidance represents a crucial technological challenge in manipulator research. Traditional path planning methods have certain limitations when dealing with the obstacle avoidance for links of manipulator, such as large calculational amount or unstable planned paths. To address this, this paper presents an improved joint space Astar algorithm for a 6 degree of freedom (DOF) manipulator with a pre-planning strategy. Firstly, we propose a pre-planning strategy that can refresh start positions and target positions to reduce computational load. Next, we perform a three-dimensional Astar path planning method in joint space, which avoids numerous inverse kinematics computations and singularities, and reduces the amount of high-dimensional calculations. In addition to the obstacle avoidance for manipulator end-effector, we also realize the obstacle avoidance for manipulator link. In simulation and experiments, the effectiveness of the proposed path planning algorithm is validated and the path tends to be optimal compared to the conventional Astar, RRT and Dijkstra algorithms.

Given a map and a target position, a robot must perform path planning in order to determine the best route from its current position to the target position. Three algorithms for path planning are presented: Dijkstra’s shortest path algorithm for a grid of cells, an algorithm for continuous maps and the A\(^{*}\) algorithm, an improvement of Dijkstra’s algorithm that uses heuristic functions. The chapter concludes with a description of the integration of path planning with obstacle avoidance.

Robot path planning is a cornerstone of precision agriculture, enabling safe and efficient operations for agricultural robots. However, complex field environments—characterized by static and dynamic obstacles, dense vegetation, and unstructured terrain—pose significant challenges to effective path planning. Conventional methods, such as A*, Dijkstra, and rapidly exploring random tree (RRT), exhibit limitations in efficiency and adaptability to dynamic conditions. To address these challenges, this study introduces AgriPath, a robust multi-objective path planning framework that integrates an improved convolutional neural network (CNN), an improved A* algorithm, and an improved whale optimization algorithm (IWOA) to optimize pathfinding, convergence efficiency, and obstacle avoidance in complex agricultural settings. Key innovations include an improved CNN leveraging causal convolution and multi-head self-attention mechanisms to improve temporal modeling for short-term trajectory prediction, augmented by Gaussian perturbations to enhance initial solution diversity; an improved A* algorithm incorporating dynamic heuristic functions based on Normalized Difference Vegetation Index (NDVI), combined with Kalman filtering, to bolster global path adaptability; IWOA employing non-linear convergence factors and differential evolution mechanisms to dynamically balance path length, smoothness, and planning time; and an improved Douglas–Peucker algorithm paired with cubic B-spline smoothing and navigation command modules to ensure path simplification and real-time execution. Experiments conducted in the Modern Agricultural Demonstration Zone at Chengdu, Sichuan Province, China, across simple, moderate, and complex scenarios, demonstrate that AgriPath outperforms advanced algorithms—SBREA*, Ant Colony A*, Orchard A*, and Greedy A*—in path length, smoothness, planning time, and dynamic obstacle avoidance success rate, indicative of superior multi-objective optimization balance. This study significantly enhances the efficiency and robustness of agricultural robot path planning, offering a more adaptive solution for autonomous navigation in precision agriculture while providing new theoretical and practical directions for the field of path planning.

With the growing interest of integrating robotics into everyday life and industry, the requirements towards the quality and quantity of applications grows equally hard. This trend is profoundly recognized in applications involving visual perception. Whereas visual sensing in home environments tend to be mainly used for recognition and localization, safety becomes the driving factor for developing intelligent visual control algorithms. More specifically, a robot operating in a human environment should not collide with obstacles and executed motion should be as smooth as possible. Furthermore, as the environment is not known on beforehand, a high demand on the robustness of visual processing is a necessity. On the other hand, in an industrial setting, the environment is known on beforehand and safety is mainly guaranteed by excluding a human operator. Despite these reasons, and the fact that visual servoing has gained much attention from industry to become a standard solution for robotic automation tasks, applications are highly simplified. For example, methods such as visual fault detection are already a mature technique in industrial manufacturing, where a fixed camera observes a product (e.g., on a conveyor belt) and checks whether it meets certain requirements. These operations can be executed at a fairly high rate due to the simplicity of the system (e.g., static camera) and the simplification of the processing task (e.g., binary images). For both areas the identified difficulties are similar. Foremost, this is the slow nature of (robust) visual processing, in respect to the ever growing demand of increasing speed and reducing delay. These two application areas with analogous limitations motivate the design of more direct approaches of vision in visual control systems. Therefore, in order to meet the requirements for next generation visual control systems, this thesis presents approaches which employ visual measurements as a direct feedback to design constrained motion. First, for industrial robotics, in order to obtain the required positioning accuracy, the measurement and fixation system have to be highly rigid and welldesigned, implying high cost and long design time. By measuring the position of objects directly with a camera, instead of indirectly by motor encoders, the requirements of the measurement and fixation system are less demanding. Moreover, this motivates the miniaturization of the complete control system. The approach is validated in experiments on a simplified 2D planar stage (i.e., considerable friction, poor fixation), which attains similar performance compared to encoder-based positioning systems. Secondly, in a human-centred environment, this direct sensing can improve traditional visual control systems, when subject to certain disturbances. More specifically, a method is proposed that uses an image-based feedforward controller on top of traditional position-based visual servo control to overcome disturbances such as friction or poorly designed local motor controllers. This visual feedforward control action is only active when an image-based error is present and vanishes when that error goes to zero. The method is validated on an anthropomorphic robotic manipulator with 7 degrees of freedom, intended for operation in the human care environment. Third, sensing the product directly gives rise to designing motion directly. Whereas in traditional approaches the motion trajectory is designed offline and can not be changed at runtime, direct trajectory generation computes the motion of the next step based on current state and events. This means that at any instance in time, the trajectory of a motion system can be altered with respect to certain desired kinematic or dynamic constraints. For industrial applications this makes manufacturing on near-repetitive or non-rigid structures (e.g. flexible displays) possible. When applied to a robotic manipulator, this enables obstacle avoidance to no longer be on path planning level, but on trajectory planning level, where kinematic or dynamic constraints can be taken into account. This results in a motion that is smoother than when obstacle avoidance with path planning is employed. For both application areas this direct trajectory generation method is implemented and shows high flexibility in constrained motion trajectory design.

Unmanned aerial vehicles (UAVs) are widely used for autonomous sensing tasks such as surveillance, mapping, tracking, and search operations. Multi-UAV path planning is a key research area that focuses on decentralized coordination, optimization, and collaboration. Although the deployment of multiple UAVs increases system complexity, it can significantly improve operational efficiency. Efficient path planning requires reliable obstacle avoidance. Although many existing algorithms effectively handle static obstacles, machine learning–based solutions for dynamic obstacle avoidance remain limited. Dynamic obstacles introduce an additional layer of complexity that requires more advanced planning strategies. To address these challenges, this work proposes a multi-UAV path planning approach that utilizes an enhanced Q-learning algorithm. The method incorporates a Shortest Distance Prioritization (SDP) policy designed to minimize each UAV’s travel distance to its target. Comparative performance evaluations show that the proposed approach outperforms traditional path planning techniques in terms of both training time and path length, demonstrating its effectiveness in handling static and dynamic obstacles.

Taking planar 3R redundant robot as study object, the obstacle avoidance of redundant robot path planning method is researched. The appropriate obstacle avoidance potential function is constructed with the calculation method of obstacles to links improved and the obstacle avoidance constraints simplified. Based on the resolved motion rate control (RMRC), the redundant robot inverse kinematics is resolved and the path planning is realized. The efficiency of this method is proved by the simulation of robot tracking different trajectory and avoiding static and dynamic obstacles in the work space.

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Grasses give beauty to the environment especially when properly managed. There are challenges of and not limited to time consuming, labour intensiveness in carrying out these maintenance thus, the need for the system which can autonomously perform such task. Many researches on autonomous grass cutting robot have been limited to obstacle avoidance and path planning but lawn availability is still a major area ofconcern. This study is aimed at developing an autonomous solar grass cutting robot embedded with obstacle avoidance and path planning mechanisms. The autonomous robot utilizes energy from the sun via photovoltaic panel to charge the battery for continuous performance of the robot. The obstacle avoidance and path planning for area covering of the robot were achieved using infrared sensor and 2D laser scanner respectively. The efficiency evaluation of the machine was based on the primary set objectives of obstacle avoidance and path planning in terms of area coverage, machine lawn availability, energy consumption and machine intervention. The results show that the machine operates at an efficiency of almost 100 % in in term of area coverage. Percentage machine intervention and lawn availability are 5 and 95 %, respectively. Also, 6000 Wh average energy consumption was achieved. The development is very suitable for a semi-structured environment.