Highly smooth and parameter independent obstacle avoidance method for autonomous vehicle with velocity-varying obstacle

One of the primary challenges for autonomous vehicle (AV) is planning a collision-free path in dynamic environment. It is a tricky task for achieving high-performance obstacle avoidance with velocity-varying obstacle. To solve this problem, a highly smooth and parameter independent obstacle avoidance method for autonomous vehicle with velocity-varying obstacle (HSPI-OAM) is presented in this work. The proposed method uses the virtual collision point model to accurately design the desired acceleration, which makes the obtained path highly smooth. At the same time, the method gets rid of the dependence on parameter adjustment and has strong adaptability to different environments. The simulation is implemented on the Matlab-Carsim co-simulation platform, and the simulation results show that the path planned by HSPI-OAM has good performance for obstacle with acceleration.

Virtual Obstacles for Sensors Incapacitation in Robot Navigation: A Systematic Review of 2D Path Planning

The field of mobile robot (MR) navigation with obstacle avoidance has largely focused on real, physical obstacles as the sole external causative agent for navigation impediment. This paper has explored the possible option of virtual obstacles (VOs) dominance in robot navigation impediment in certain navigation environments as a MR move from one point in the workspace to a desired target point. The systematically explored literature presented reviews mostly between the years 2000 and 2021; however, some outlier reviews from earlier years were also covered. An exploratory review approach was deployed to itemise and discuss different navigation environments and how VOs can impact the efficacy of both algorithms and sensors on a robotic vehicle. The associated limitations and the specific problem types addressed in the different literature sources were highlighted including whether or not a VO was considered in the path planning simulation or experiment. The discussion and conclusive sections further recommended some solutions as a measure towards addressing sensor performance incapacitation in a robot vehicle navigation problem.

Reconfigurable fully constrained cable-driven parallel mechanism for avoiding collision between cables with human

Productivity can be increased by manipulators tracking the desired trajectory with some constraints. Humans as moving obstacles in a shared workspace are one of the most challenging problems for cable-driven parallel mechanisms (CDPMs) that are considered in this research. One of the essential primary issues in CDPM is collision avoidance among cables and humans in the shared workspace. This paper presents a model and simulation of a reconfigurable, fully constrained CDPM enabling detection and avoidance of cable–human collision. In this method, unlike conventional CDPMs where the attachment points are fixed, the attachment points on the rails can be moved (up and down on their rails), and then the geometric configuration is adapted. Karush–Kuhn–Tucker method is proposed, which focuses on estimating the shortest distance among moving obstacles (human limbs) and all cables. When cable and limbs are close to colliding, the new idea of reconfiguration is presented by moving the cable’s attachment point on the rail to increase the distance between the cables and human limbs while they are both moving. Also, the trajectory of the end effector remains unchanged. Some simulation results of reconfiguration theory as a new approach are shown for the eight-cable-driven parallel manipulator, including the workspace boundary variation. The proposed method could find a collision-free predefined path, according to the simulation results.

Reachable Set Estimation for Markovian Jump Neutral-Type Neural Networks With Time-Varying Delays

The reachable set estimation problem for a class of Markovian jump neutral-type neural networks (MJNTNNs) with bounded disturbances and time-varying delays is tackled in this article. With the aid of the delay partitioning method, a novel stochastic Lyapunov-Krasovskii functional containing triple integral terms is constructed in mode-dependent augmented form. To begin with, transition probabilities of the concerned Markovian jump neural networks (NNs) are considered to be completely known. By employing the integral inequality approach and reciprocally convex combination method, it is proved that all state trajectories which start from the origin by bounded inputs can be constrained by an ellipsoid-like set if a group of linear matrix inequalities (LMIs) is feasible. Then, the free-connection weighting matrix technique is utilized to handle the case of partially known transition probabilities. As byproducts, some sufficient conditions are also obtained to guarantee the stochastic stability of the concerned NNs. The validity of the theoretical analysis is confirmed by numerical simulations.

A Path Planning Method for Underground Intelligent Vehicles Based on an Improved RRT* Algorithm

Path planning is one of the key technologies for unmanned driving of underground intelligent vehicles. Due to the complexity of the drift environment and the vehicle structure, some improvements should be made to adapt to underground mining conditions. This paper proposes a path planning method based on an improved RRT* (Rapidly-Exploring Random Tree Star) algorithm for solving the problem of path planning for underground intelligent vehicles based on articulated structure and drift environment conditions. The kinematics of underground intelligent vehicles are realized by vectorized map and dynamic constraints. The RRT* algorithm is selected for improvement, including dynamic step size, steering angle constraints, and optimal tree reconnection. The simulation case study proves the effectiveness of the algorithm, with a lower path length, lower node count, and 100% steering angle efficiency.

Collision Avoidance of Hexacopter UAV Based on LiDAR Data in Dynamic Environment

A reactive three-dimensional maneuver strategy for a multirotor Unmanned Aerial Vehicle (UAV) is proposed based on the collision cone approach to avoid potential collision with a single moving obstacle detected by an onboard sensor. A Light Detection And Ranging (LiDAR) system is assumed to be mounted on a hexacopter to obtain the obstacle information from the collected point clouds. The collision cone approach is enhanced to appropriately deal with the moving obstacle with the help of a Kalman filter. The filter estimates the position, velocity, and acceleration of the obstacle by using the LiDAR data as the associated measurement. The obstacle state estimate is utilized to predict the future trajectories of the moving obstacle. The collision detection and obstacle avoidance maneuver decisions are made considering the predicted trajectory of the obstacle. Numerical simulations, including a Monte Carlo campaign, are conducted to verify the performance of the proposed collision avoidance algorithm.

Mobile Robot Path Planning Using Ant Colony Algorithm and Improved Potential Field Method

For the problem of mobile robot's path planning under the known environment, a path planning method of mixed artificial potential field (APF) and ant colony optimization (ACO) based on grid map is proposed. First, based on the grid model, APF is improved in three ways: the attraction field, the direction of resultant force, and jumping out the infinite loop. Then, the hybrid strategy combined global updating with local updating is developed to design updating method of the ACO pheromone. The process of optimization of ACO is divided into two phases. In the prophase, the direction of the resultant force obtained by the improved APF is used as the inspired factors, which leads ant colony to move in a directional manner. In the anaphase, the inspired factors are canceled, and ant colony transition is completely based on pheromone updating, which can overcome the inertia of the ant colony and force them to explore a new and better path. Finally, some simulation experiments and mobile robot environment experiments are done. The experiment results verify that the method has stronger stability and environmental adaptability.

Mobile robot path planning in dynamic environment based on cuckoo optimization algorithm

Nowadays, the usage of autonomous mobile robots that fulfill various activities in enormous number of applications without human’s interference in a dynamic environment are thriving. A dynamic environment is the robot’s environment which is comprised of some static obstacles as well as several movable obstacles that their quantity and location change randomly through the time. Efficient path planning is one the significant necessities of these kind of robots to do their tasks effectively. Mobile robot path planning in a dynamic environment is finding a shortest possible path from an arbitrary starting point toward a desired goal point which needs to be safe (obstacle avoidance) and smooth as well as possible. To achieve this target, simultaneously satisfying a collection of certain constraints including the shortest, smooth, and collision free path is required. Therefore, this issue can be considered as an optimization problem, consequently solved via optimization algorithms. In this article, a new method based on cuckoo optimization algorithm is proposed for solving the mobile robot path planning problem in a dynamic environment. Furthermore, to diminish the computational complexity, the feature vector is also optimized (i.e. reduced in dimension) via a new proposed technique. The simulation results show the performance of proposed algorithm in finding a short, safe, smooth, and collision free path in different environment conditions.