Analysis of the Effectiveness Path Planning Methods and Algorithm for the Anthropomorphic Robot Manipulator

An energy efficient IoD static and dynamic collision avoidance approach based on gradient optimization

The ability of automated vehicles to avoid obstacles on the road is one of the most critical functionalities, as it directly impacts passenger safety. The trajectory planning functionality plays a key role in this task, as it uses data from sensors to identify obstacles and plan a free obstacle path. Thus, this paper proposes a trajectory planning method based on a model predictive control (MPC) strategy that takes into account both static and dynamic obstacles. Additionally, a trajectory tracking controller based on a linear quadratic regulator (LQR) algorithm is designed to ensure the vehicle follows the planned path. To evaluate the proposed control solution for the lateral dynamics of the vehicle, a traffic scenario involving multiple obstacles (both static and dynamic) is used. The results show that the proposed solution effectively controls the vehicle's lateral dynamics and successfully avoids collisions with obstacles.

We present a reactive base control method that enables high performance mobile manipulation on-the-move in real-world environments with static and dynamic obstacles. Performing manipulation tasks while the mobile base remains in motion can significantly decrease the time required to perform multi-step tasks, as well as improve the gracefulness of the robot's motion. Existing approaches to manipulation on-the-move either ignore the obstacle avoidance problem or rely on the execution of planned trajectories, which is not suitable in environments with dynamic objects and obstacles. The presented controller addresses both of these deficiencies and demonstrates robust performance of pick-and-place tasks in dynamic environments. The performance is evaluated on several simulated and real-world tasks. On a mobile manipulation task with static obstacles, we outperform an existing method by 41% in terms of total task time. Further, we present real-world examples of our robot performing manipulation tasks on-the-move while avoiding a second autonomous robot in the workspace.

During the mission of the USV (Unmanned Surface Vehicle), there may be some static or dynamic obstacles on the pre-planned track that are not displayed on the ground station. To avoid these obstacles in time, a model of DCPA (distance to the closest point of approach) between the USV and the obstacle is proposed, which is used to plan the speed of the USV, and adaptively accelerate and decelerate can safely avoid dynamic obstacles. We solved the problem of target unreachable and heading change optimization by planning the heading at IAPF (improved artificial potential field) in the process of sailing, which can effectively avoid static obstacles and respond to collision avoidance in emergencies. The collision avoidance planning outputs a set of behavior vectors (speed, heading), and hands it over to the USV controller to control the navigation of the USV. In this study, an adaptive speed and heading planning method is used to improve the collision avoidance ability of the USV in the mixed scene of dynamic and static obstacles. Through multiple launching experiments, the actual collision avoidance effect of the method is verified, and the safety and reliability of the USV application requirements are met.

COLREGs-abiding hybrid collision avoidance algorithm based on deep reinforcement learning for USVs

Background: With the development of robotics, more and more robots are being used in industrial production. However, as the production environment surrounding the robots becomes increasingly complex, there is a need for more intelligent industrial robots. For improving the intelligence of industrial robots, the most important thing is to ensure that they can carry out safe industrial production activities in a complex production environment. Therefore, studying the autonomous dynamic obstacle avoidance path planning of industrial robots in complex environments is of great significance for improving the intelligence of industrial robots and the application of human-machine collaboration. Objective: The main purpose of this paper is to improve the traditional artificial potential field method. It aims to improve the disadvantages of the traditional artificial potential field method, such as falling into the local minimum and failing to reach the target. Secondly, the background difference method, which is based on binocular vision and Kalman filtering algorithm, is used, and the environmental map containing the static and dynamic obstacles is obtained. After obtaining the information on the position of static and dynamic obstacles, the robot arm can make good use of the improved artificial potential field method to plan its own trajectory, thus realizing the dynamic obstacle avoidance of the robot arm in a complex environment. Methods: This paper proposes an improved artificial potential field method. First of all, in order to solve the problem of not being able to achieve the goal, the method of modifying the repulsion field function, as proposed by Wang Huili and others, has been cited. In the traditional repulsion function, the relative distance between the robot and the target is introduced. Due to this, the target point is always the minimum point of the potential field. It is necessary to ensure that the robot can reach the target position smoothly. For the local minimum problem, by adding a gravitational increase factor β, the gravitational force received is greater than the repulsive force, thereby breaking the balance of the resultant force. Finally, for the traditional artificial potential field method that does not consider the path planning problem in the presence of dynamic obstacles, the velocity vector of the dynamic obstacle is brought into the potential field function to improve the traditional artificial potential field method. Results: The robot easily falls into the local optimum during path planning, and the improved artificial potential field method overcomes this shortcoming. Therefore, the manipulator can perform autonomous dynamic obstacle avoidance path planning in an environment with dynamic obstacles, finally reaching the target point safely. Conclusions: The industrial production field faces an increasing demand for intelligent industrial robots. This paper improves on the traditional artificial potential field method based on binocular vision so that the mechanical arm can avoid dynamic obstacles autonomously in the presence of dynamic obstacles, thereby improving the degree of intelligence of the robotic arm, enabling the technology to meet the needs of future industrial development and contribute to the development of future industrial robot technology.

Robots can be used for various purposes in environments humans cannot reach. Dynamic environments with static and dynamic obstacles are challenging areas in robotic implementations. In this proposed study, robotic exploration is presented in an unknown dynamic environment using Random Network Distillation(RND) algorithm, which was introduced for Atari games. RND algorithm is used here for robotic navigation depending on obstacle location and environmental features. The proposed route planning robot is implemented using the Robot Operating System(ROS) and the Gazebo simulator. In previous works, Deep Reinforcement Learning based agents have shown promising outcomes. However, the reward system needs to be more robust for this path-planning task. In order to unravel the sparse reward issue, a revised RND approach is suggested. In order to dynamically alter intrinsic and extrinsic rewards, this algorithm determines the variation of the training steps count in each training episode. Laser inputs from Gazebo are fed to the RND algorithm to get the actions. Actions will define by avoiding walls or obstacles. The robotic path and map are visualized on the Gazebo. The results show the robot's actions and combine intrinsic and extrinsic rewards from RND on the application. Further, employing the RND-trained agent learns to cover environments presented as a novel part of the research. Four simulations are conducted based on the environment to validate the algorithm, including without obstacles, static obstacles, dynamic obstacles, and both. The experimental results indicate that RND agents trained in different environments can reach the goal accurately without crashes.

The main requirement of parametric path planning techniques in robot manipulators is to create continuous, smooth, and easy-to-modify path such as to move the end-effector from start point to destination point. To meet these requirements, the rational Bezier and NURBS algorithms have been proposed for path planning of 2R manipulator in environment with known and static obstacles. In this study, a comparison in terms of path length and time consumption of algorithm has been conducted to show the superior of one method to another. Based on numerical simulation, it has been shown that the rational Bezier algorithm generates shorter path and takes less time to complete the path planning task as compared to NURBS method. In addition, this study presented the design of real-time set-up based on Arduino UNO microcontroller and micro-stepping actuators. It has been shown that the experimental results could successfully verify the numerical results for both proposed path planning methods for different configuration of obstacles.

A cuspidal robot can move from one inverse kinematics (IK) solution to another without crossing a singularity. Multiple industrial robots are cuspidal. They tend to have a beautiful mechanical design, but they pose path-planning challenges. A task-space path may have a valid IK solution for each point along the path, but a continuous joint-space path may depend on the choice of the IK solution or even be infeasible. This article presents new analysis, path planning, and optimization methods to enhance the utility of cuspidal robots. We first demonstrate an efficient method to identify cuspidal robots and show, for the first time, that the ABB GoFa and certain robots with three parallel joint axes are cuspidal. We then propose a new path-planning method for cuspidal robots by finding all IK solutions for each point along a task-space path and constructing a graph to connect each vertex corresponding to an IK solution. Graph edges have a weight based on the optimization metric, such as minimizing joint velocity. The optimal feasible path is the shortest path in the graph. This method can find nonsingular paths as well as smooth paths that pass through singularities. Finally, we incorporate this path-planning method into a path optimization algorithm. Given a fixed workspace toolpath, we optimize the offset of the toolpath in the robot base frame while ensuring continuous joint motion. Code examples are available in a publicly accessible repository.

Two potential fields fused adaptive path planning system for autonomous vehicle under different velocities

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.

This paper presents a method for real-time path planning for fully actuated autonomous surface vehicles in confined waters. The goal is to continuously generate a collision-free path from a given initial pose to a given final pose. Both the own vehicle and static obstacles are represented as convex polygons. As soon as the environment changes, or other initial or final poses are specified, a warm start is performed, in which the results of previous solutions are reused. An optimal sampling-based approach is used to explore the search space. In a cost function, the length of the path is weighted together with the distance to all obstacles. Some parts of the cost function are calculated in advance and stored in look-up tables to reduce the computation time. The result is an optimal path from an initial pose to a final pose that avoids collisions of the vehicle with static obstacles. The proposed warm start procedure is tested by real-time experiments using different scenarios.

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.

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.