Vehicle Trajectory Planning Using Model Predictive Control in Environments With Dynamic and Static 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

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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 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.

Multi-power sources are included in hybrid electrical vehicles, which leads to multi-driving modes co-existing when driving the vehicle. However, the frequent driving mode switching (DMS) will probably need the engine to be started frequently, which can result in extra fuel consumption. So, avoiding unnecessary DMS should be fully considered when designing the control strategy. For solving this problem, a model predictive control (MPC) strategy integrating Markov chain driving intention identification is put forward. First, the component models of the powertrain system are established. Second, according to the real driving cycle data, a driving intention model based on the Markov chain is designed according to the real driving cycle data. Then the MPC-based control strategy aiming at reducing DMS times is proposed by integrating the cost of DMS. Finally, the proposed control strategy is contrasted with three other control strategies to verify its validity in reducing the mode switching times and improving the fuel economy.

<div>The advancement in vision sensors and embedded technology created the opportunity in autonomous vehicles to look ahead in the future to avoid potential obstacles and steep regions to reach the target location as soon as possible and yet maintain vehicle safety from rollover. The present work focuses on developing a nonlinear model predictive controller (NMPC) for a high-speed off-road autonomous vehicle, which avoids undesirable conditions including stationary obstacles, moving obstacles, and steep regions while maintaining the vehicle safety from rollover. The NMPC controller is developed using CasADi tools in the MATLAB environment. The CasADi tool provides a platform to formulate the NMPC problem using symbolic expressions, which is an easy and efficient way of solving the optimization problem. In the present work, the vehicle lateral dynamics are modeled using the Pacejka nonlinear tire model. Further, a new algorithm is developed based on the box slope and box detection methods to process the stationary and moving obstacles. These methods use the vehicle’s current heading and generate a light detection and ranging (LIDAR) view through rectangular box regions. These box regions mimic the actual vision sensor regions, and logic can easily be applied to real vehicle conditions. From the results, it is observed that the vehicle avoids both moving and stationary lengthy obstacles and can safely navigate through a pool of obstacles that can mimic real-world off-road scenarios. Further, the simulations with Gaussian noise in vehicle state estimations and obstacle states confirmed that the developed algorithm can reach the target without collision by meeting all the vehicle safety constraints for the considered uncertainty limits.</div>

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Wave-propelled unmanned surface vehicles (USVs) rely on the forces exerted by the environment to navigate as intended. The unpredictability of the environment and the low speed and limited maneuverability that characterizes such platforms motivates the need of a continuous monitoring of both static and dynamic obstacles over a long time horizon. In this manuscript we present an automatic anti-collision and anti-grounding system that integrates digital charts and automatic identification system (AIS) messages received onboard to enhance the situational awareness perceived by a wave-propelled USV, and enable evasive maneuvers to avoid grounding and collisions with static and dynamic obstacles. The acquired information is used in a scenario-based model predictive control (SB-MPC) algorithm that computes optimal behaviors that minimize the risk of collisions, grounding and damage. Additionally, the proposed system integrates environmental factors (wind, waves and sea currents) in the SB-MPC optimization process, in order to command the safest behavior when high sea states increase the risk of drifting and grounding. The information contained in Electronic Navigational Charts (ENCs) is represented with point clouds, enabling fast software manipulation of large areas and therefore increasing the USV responsiveness when scenarios with static obstacles turn into dynamic collision avoidance scenarios. The proposed anti-collision and anti-grounding system is tested in simulations and field experiments.

Energy-efficient operation of the thermal management system in electric vehicles via integrated model predictive control

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.

A new consensus theory-based method for formation control and obstacle avoidance of UAVs

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