Collision avoidance using potential fields

As the industrial manufacturing is going towards flexible and intelligent manufacturing, human-machine collaboration plays important role in achieving higher productivity and efficiency. In human-robot collaborative environments, the space separation between humans and robots is cancelled and it is very important to prevent human-machine collisions. This paper proposes an active collision avoidance control method based on event-based planning and control. Firstly, the autonomous motion of the robot driven by non-time reference is achieved. Then, real-time modification of the autonomous motion based on artificial potential field to avoid the obstacles is presented. Finally, experiment of event based autonomous motion and simulation of collision avoidance control are conducted.

This paper introduces a novel control strategy for robotic obstacle avoidance by integrating Gradient Tracking Integral Sliding Mode Control (GT-ISMC) with Artificial Potential Fields (APFs). This approach aims to enhance autonomous navigation's robustness and accuracy in dynamic and uncertain environments. Traditional obstacle avoidance methods struggle with local minima and lack sufficient robustness against external disturbances and model uncertainties. The proposed method employs APFs to create smooth repulsive forces to avoid obstacles and attract the robot to the target. The novelty lies in designing an integral sliding mode controller to track the APF gradients, improving robustness against disturbances and uncertainties in the robot's dynamics, reducing chattering and improving convergence performance. Stability analysis guarantees the convergence of the robot to the desired reference while evading the obstacle. Simulations and experimental results demonstrate that the GT-ISMC with APFs effectively navigates complex environments, offering superior robustness, faster convergence, and smoother trajectories compared to conventional PID, sliding mode, and Potential Field methods. This work contributes to the advancement of autonomous robotic control by providing a reliable and efficient method for real-time obstacle avoidance, with potential applications in mobile robotics and industrial automation. Highlights A novel control strategy for robotic obstacle avoidance by integrating Gradient Tracking Integral Sliding Mode Control (GT-ISMC) with Artificial Potential Fields (APFs) is proposed. The controller enhances the robustness and accuracy of autonomous navigation in dynamic and uncertain environments, where some motion constraints are considered. The proposed method employs APFs to generate smooth, continuous repulsive forces that guide the robot away from obstacles while attracting it toward the target. By incorporating gradient tracking into the sliding mode framework, the system benefits from reduced chattering and improved convergence performance.

A simulation study for path planning of mobile robots in a 2D space using the potential field method is presented. The mathematical analysis represents the attractive force between the robot and the target, as well as the repulsive forces between robot and obstacles. The forces are decomposed to their resultant x-y components in 2D for simulation implementation purposes. These two forces control the motion of the mobile robot to safely avoid any obstacle within the robot environment and track the dynamic target. The simulations for many cases were carried out using the resultant forces. The integration between the robot’s equations of motion for constant acceleration, Newton’s second law, and the potential field forces are illustrated for simulation implementation purposes.

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.

This paper presents a viable approach for incorporating collision avoidance strategies into existing guidance and control systems on marine vessels. We propose a method that facilitates the use of simulation-based Model Predictive Control (MPC) for collision avoidance (COLAV) on marine vessels. Any COLAV strategy to be applied in real traffic must adhere to the international regulations for preventing collisions at sea (COLREGS). The proposed MPC COLAV method does not rely on an accurate model of the guidance system to achieve vessel behaviors that are compliant with the COLREGS. Rather, it depends on transitional costs in the MPC objective for collision avoidance maneuvers that are being executed by the marine vessel. Hence, it is straightforward to implement the MPC COLAV on different vessels without specific knowledge of the vessel's guidance strategy. Moreover, it offers the possibility to switch between different (possibly application specific) guidance strategies on the same vessel while running the same MPC COLAV algorithm. We present results from full scale experiments that show the viability of our method in different collision avoidance scenarios.

This paper presents a distributed solution for the problem of collaborative collision avoidance for autonomous inland waterway ships. A two-layer collision avoidance framework that considers inland waterway traffic regulations is proposed to increase navigational safety for autonomous ships. Our approach allows for modifying traffic rules without changing the collision avoidance algorithm, and is based on a novel formulation of model predictive control (MPC) for collision avoidance of ships. This MPC formulation is designed for inland waterway traffic and can handle complex scenarios. The alternating direction method of multipliers is used as a scheme for exchanging and negotiating intentions among ships. Simulation results show that the proposed algorithm can comply with traffic rules. Furthermore, the proposed algorithm can safely deviate from traffic rules when necessary to increase efficiency in complex scenarios.

This work solves the obstacle avoidance problem extending the Potential Field (PF) method for a mobile robot. The usual definition of the PF has been modified to have a field which is continuous everywhere. It is shown that the system has an attracting equilibrium at the target point, repelling equilibriums in the centers of the obstacles and saddle points on the borders. Those unstable equilibriums are avoided capitalizing on the established Input-to-State Stability (ISS) property of this multi-stable system. To escape a local minima this work makes the most of ISS property that is not lost for perturbations. And for small properly designed disturbances the global attractivity of the target point is proved. The proposed modification of the PF method is shown to be effective by simulation and then applied for unicycle-like mobile robots with additive input disturbances.

It is a hot topic in the field of road landscape planning technology that a mobile robot can quickly and safely find an optimal path in a multi-obstacle environment. In path planning, in light of the problems of poor cooperation and slow convergence of ant colony algorithm in a known environment, the existing potential field method in the local path environment focuses on avoiding dynamic obstacles but cannot guarantee an optimal path. This study provides a new fusion algorithm for path planning optimization in both static and dynamic environments. Firstly, to prevent slipping into a local optimum, create a pheromone diffusion model and adaptively tweak the population information entropy factor to speed up the convergence speed of the Adaptive Ant Colony Optimization (AACO) algorithm. Secondly, on the basis of the globally planned path, by designing the local stability detection and escape functions, the Improved Artificial Potential Field (IAPF) method is utilized to solve the problem of unreachable destination. Finally, we conduct simulation experiments through MATLAB to compare the indicators for evaluating paths, it verifies that the fusion algorithm proposed in this research has obvious advantages in path planning in both static and dynamic environments.KeywordsPath planningAdaptive ant colony optimizationPopulation information entropyImproved artificial potential field

In this paper, we propose the infinity-norm based worst-case collision avoidance control system for quadrotors with the collision detector. The associated worst-case collision avoidance is captured via the infinity-norm in <inline-formula> <tex-math notation="LaTeX">$\mathbb {R}^{3}$ </tex-math></inline-formula> (equivalently the cube in <inline-formula> <tex-math notation="LaTeX">$\mathbb {R}^{3}$ </tex-math></inline-formula>). With the proposed infinity-norm based collision avoidance approach, the quadrotors are able to avoid any obstacles that cannot be detected by the standard two-norm in <inline-formula> <tex-math notation="LaTeX">$\mathbb {R}^{3}$ </tex-math></inline-formula> (the cylinder in <inline-formula> <tex-math notation="LaTeX">$\mathbb {R}^{3}$ </tex-math></inline-formula>). In our proposed collision avoidance method, the obstacle approximation algorithm approximates the true coordinates of obstacles by simple integer values to reduce computational complexity. Then the approximated obstacle is surrounded by infinity-norm based to cover the actual obstacle. Note that the actual obstacle may not be covered with the standard two-norm, which can cause the collision. We then design the collision detector and the Model Predictive Control (MPC) tracker, where the latter generates the optimal trajectory for the quadrotor by cooperating with the proposed collision detector. The optimal trajectory generated by the MPC tracker is regarded as the reference input for the quadrotor without collision, where the quadrotor is controlled by the modified backstepping controller. The proposed approach is validated by performing the hardware-in-the-loop simulations (HILS) and experiments. Specifically, we show the superiority of the proposed infinity-norm based collision avoidance performance, by comparing it with the standard two-norm based approach in various maneuver situations of the quadrotor.

In order to solve the problem that intelligent vehicle active collision avoidance systems have different decision-making results under different road conditions, the square-root cubature Kalman filtering algorithm is used to estimate the road adhesion coefficients, which are introduced into the safety distance model and combined with the fireworks algorithm for braking and steering weight coefficient allocation to ensure that the vehicle can safely avoid collision. The simulation results show that the square-root cubature Kalman filter has higher estimation accuracy and robustness compared with the cubature Kalman filter, and a more reasonable collision avoidance control can be adopted in the subsequent collision avoidance control. Therefore, the proposed new estimation method of road adhesion coefficients proves effective in mitigating vehicle collision risks.

ABSTRACTIn order to reduce the possibility of collisions during the driving process of intelligent vehicles in the same direction, this paper studies the collision avoidance control of intelligent vehicles in the same direction and designs an active collision avoidance controller. The longitudinal safe distance model, lateral lane change path planning model, and adaptive multi‐point preview model of preview distance are established. The longitudinal speed control is carried out by the expert PID control method based on mode switching, the lateral path tracking control is carried out by the sliding mode control method with exponential convergence law, and the active collision avoidance controller is designed in combination with the multi‐point preview module that is adaptive to the preview distance. The active collision avoidance controller was jointly simulated using Carsim, Prescan, and Simulink software for emergency lane change scenarios and slow vehicle driving in front. In the emergency lane change scenario, the minimum distance between the two vehicles is 1.9 m, and the path tracking deviation is 0.17 m. In the front vehicle slow driving scenario, the minimum distance between the two vehicles is 2.2 m, and the path tracking deviation is 0.13 m. The controller can realize collision avoidance in two scenarios of 80 and 108 km/h respectively, which shows that the controller is robust and considers the tracking accuracy and steering stability at the same time, which is of reference significance for improving the safety of intelligent vehicles driving in the same direction.

There are many unknown obstacles in the sea, so the autonomous navigation of unmanned surface vehicle needs to avoid them as soon as possible even under the condition of low control ability of the controller. To solve the problem, by combining the dynamic collision avoidance algorithm and tracking control, a dynamic collision avoidance control method in the unknown ocean environment is presented. In this article, in consideration of the unknown ocean environment and real-time dynamic obstacle avoidance problem, the collision avoidance controller using a velocity resolution method and backstepping tracking controller based on unmanned surface vehicle maneuvering motion model is designed. Simulation results show that the method is effective and accurate and can provide the reference for the unmanned surface vehicle intelligent collision avoidance control technology.

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.

U NMANNED Aerial Vehicles (UAVs) hold good promise for autonomously carrying out complex civilian and military operations. However, many of these missions require them to fly at low altitudes, making them vulnerable to collision with both stationary as well as moving obstacles. Hence, it is vital that UAVs are equipped with autonomous capability to sense and avoid collisions, especially for the pop-up threats. When such a threat is sensed and a collision is predictedwithin a short time ahead, theUAV should be able to react and maneuver away quickly so that the collision is avoided. An algorithmwhich can assure such amaneuver is called a “reactive collision avoidance algorithm.” Since the available reaction time in such a scenario is usually is small and UAVs are usually limited by computational resources, such an algorithm should also be computationally very efficient (it should preferably be noniterative). It is also required that whilemaneuvering away, it should notmaneuver toomuch away from the obstacle either. This is both to avoid collision from other nearby obstacles and not to compromise on the overall mission objective. There are various attempts in the literature to develop algorithms for collision avoidance purpose, many of which are inspired from global path planning algorithms. The artificial potential field method is such an approach where the motion of the vehicle is guided under the influence of a potential field. The potential field (which is essentially a cost function) is designed in such a way that obstacles have repulsive fields while the destination has an attractive field. The safe path of the UAV is then found by optimizing the carefully selected cost function. To tune this basic philosophy for reactive collision avoidance, a model predictive control-based algorithm has been proposed in the literature. This algorithm essentially assures path following under safe conditions (i.e. if no collision is predicted in the near future) and invokes the potential field function when new collisions are sensed. However, in potential field based techniques the associated optimization process is typically done in an iterative manner. Because of this they are usually computationally intensive and hence are not suitable for reactive collision avoidance of airborne UAVs in general. A promising algorithm in collision avoidance and global path planning is the philosophy of rapidly-exploring random tree (RRT) [1], which has also been used for reactive collision avoidance. However, there are many concerns about the RRT approach, which can largely be attributed to the random nature of the algorithm. For example, the path predicted by RRT is usually a sting of connected straight lines that does not reflect the path followed by a vehicle with nonholonomic constraints. More important, it is a probabilistic approach and hence there is no guarantee of finding a feasible path within a limited finite time. Other graph search algorithms such as best-first search are also implemented for reactive collision avoidance [2]. However, this is not systematic approach and could result in the algorithm searching far too many nodes under some conditions. Moreover, precomputing motion primitives and saving them in a lookup table is infeasible for UAVs, which are usually resource-limited. An interesting perspective to collision avoidance problem is the minimum effort guidance [3], where an optimal control-based approach has been proposed after applying the collision cone philosophy to detect collisions. This method is computationally nonintensive as a closed form solution has been proposed. Even though this is an interesting idea, by minimizing the lateral acceleration, perhaps it imposes unwanted extra constraint on the problem formulation as reactive collision avoidance problems do not necessarily have to be carried out with minimum lateral acceleration. More important, one can observe that this formulation only assures position guarantee and no constraint is imposed on the velocity vector. Hence, even though it guides the vehicle to a carefully selected target point on the safety boundary (we call it the “aiming point”), it causes the vehicle to maneuver until this point. This can be risky as the vehicle may enter the safety ball before reaching the aiming point. Even though the collision cone based aiming point philosophy is a very good idea, the authors of this Note strongly believe that instead of only position guarantee, rather the velocity vector should be aligned towards the aiming point as soon as a collision is detected (which will automatically lead to position guarantee as well). Towards this objective, two new nonlinear guidance laws are proposed in this Note, which are named as nonlinear geometric guidance (NGG) and differential geometric guidance (DGG). These guidance laws are inspired by the philosophy of “aiming point guidance” (APG) [4], which has been proposed in missile guidance literature. It turns out that the APG is a simplified case of the NGG where the associated since function is replaced by its linear approximation (hence, for a systematic discussion, it is renamed as linear geometric guidance (LGG) in this Note). Both of the guidance algorithms proposed in this Note quickly align the velocity vector of the UAValong the aiming point within a part of the available time-togo, which ensures quick reaction and hence safety of the vehicle. The main feature of this philosophy is that they effect high maneuvering at the beginning, causing the velocity vector of the UAV to align with the aiming point direction quickly and then settling along it. Therefore there is no need to maneuver all the way until the aiming point is reached and hence the chance of the UAV entering into the safety ball is minimized. Using the point of closest approach (PCA) [5], the proposed NGG andDGGalgorithms have also been extended for collision avoidance with moving obstacles in both cooperative as well as ignorant scenarios. Mathematical correlations between the guidance laws have also been established, which show that the NGG and DGG are exactly correlated to each other with appropriate gain selections, while the LGG is an approximation of DGG. A “sphere-tracking algorithm” is also proposed in this Note where the UAV is guided to track the surface of the safety spherewhenever a brief violation of the safety boundary occurs after reaching the aiming point because of the location of the next aiming point (which may include the target in Presented as Paper 2010-8315 at the AIAA Guidance, Navigation and Control, Toronto, 2–5August 2010; received 26May 2010; revision received 4 October 2010; accepted for publication 6 October 2010. Copyright © 2010 by Radhakant Padhi. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission. Copies of this paper may be made for personal or internal use, on condition that the copier pay the $10.00 per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923; include the code 0731-5090/11 and $10.00 in correspondence with the CCC. ∗Former Project Assistant, Department of Aerospace Engineering; anushamujumdar87@gmail.com. Associate Professor, Department of Aerospace Engineering; padhi@ aero.iisc.ernet.in. JOURNAL OF GUIDANCE, CONTROL, AND DYNAMICS Vol. 34, No. 1, January–February 2011

In a social environment, there is a natural mutual force between people. This paper proposes a method for detecting group abnormal behavior based on the potential field method. The movement of a pedestrian is described as the movement towards the target under the combined action of several forces. Forces on pedestrians include the attractive force for themselves and the repulsive force between pedestrians. By calculating the resultant force between them, the behavior of pedestrians is judged according to a set threshold. It is verified through experiments on public data sets that our method has high accuracy and robustness.