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Real-time obstacle avoidance for manipulators and mobile robots
... Potential fields are essential for robot obstacle avoidance in dynamic environments, guiding the robot's movement with varying potential primitives. Using a local representation of the environment based on previous data reduces noise, especially with sonar data (Khatib, 1986). This approach is also practical for AUVs, where the goal generates attractive potential, and obstacles create repulsive potentials, allowing the AUV to navigate safely. ...
... This approach is also practical for AUVs, where the goal generates attractive potential, and obstacles create repulsive potentials, allowing the AUV to navigate safely. Gradients represent forces attracting the AUV towards the goal, akin to negatively charged particles (Khatib, 1986) (Belker and Schulz, 2002). Combining attractive forces towards the goal with repulsive forces from obstacles, the AUV can safely navigate to its destination in a 2D plane with position Q = (x, y) in R 2 space. ...
... They investigated control of two agents avoiding collisions with each other. In 1986 Khatib proposed new control algorithm [4] in which he combined attracting (to the goal) and repelling (from the obstacles) interactions. The novelty was the use of artificial potential functions (APF), similar to models of intermolecular interactions. ...
... Collision avoidance behaviour is based on the artificial potential functions (APF). This concept originally was proposed in [4]. All robots are surrounded by APFs that raise to infinity near objects border r j ( j -number of the robot/obstacle) and decreases to zero at some distance R j , R j > r j . ...
... Recently, obstacle avoidance has become one of the critical and practical research topics when we consider realistic scenarios where obstacles are present. Many quick and efficient obsta-cle avoidance methods have been researched [36][37][38][39]. In [36], the APF method was first proposed and implemented for obstacle avoidance, and its flexibility, simplicity, and efficiency make it develop well. ...
... Many quick and efficient obsta-cle avoidance methods have been researched [36][37][38][39]. In [36], the APF method was first proposed and implemented for obstacle avoidance, and its flexibility, simplicity, and efficiency make it develop well. In [37], the Model Predictive Control (MPC) method was used to avoid collisions with dynamic obstacles. ...
This paper utilizes the distributed event-triggered impulsive control (ETIC) scheme to solve the consensus formation problem of leader-follower multi-agent systems (MASs) subject to obstacles. In order to solve the problem of the high cost of continuous control, an impulsive control strategy combined with the event triggering function is applied. By Lyapunov theory, the sufficient conditions for the strategy to achieve global exponential consensus are obtained, which implies that the formation of the system can be completed successfully. This research proposes a novel event-triggered condition which reduces the system’s energy loss when performing formation tasks. The proposed scheme not only realizes the system formation but also avoids the occurrence of Zeno behavior. Furthermore, the influence of the external environment with obstacles to consensus formation is considered. An improved artificial potential field (APF) function is proposed, which enables the MASs to avoid obstacles during the formation process. Finally, the effectiveness of the proposed method is verified by simulations.
... -Artificial potential fields, which represent a combination of attractive and repulsive potential fields. In particular, one can take [19]: ...
... where K is a positive constant gain, ξ belongs to a neighborhood of obstacles (see [19] for more details). Another function of such type was proposed in [42]: ...
Nonlinear control-affine systems described by ordinary differential equations with time-varying vector fields are considered in the paper. We propose a unified control design scheme with oscillating inputs for solving the trajectory tracking and stabilization problems under the bracket-generating condition. This methodology is based on the approximation of a gradient-like dynamics by trajectories of the designed closed-loop system. As an intermediate outcome, we characterize the asymptotic behavior of solutions of the considered class of nonlinear control systems with oscillating inputs under rather general assumptions on the generating potential function. These results are applied to examples of nonholonomic trajectory tracking and obstacle avoidance.
... The grid method, proposed by Hachour in 2008, is a popular algorithm for cell decomposition [10]. The potential field method models the robot as a particle moving under the influence of a potential field [11] [12]. The local variations in the potential field reflect the structure of the free space, determined by the set of obstacles and the goal positions. ...
p>In this paper, we explore the practical implications of our research, offering significant advantages to the autonomous and robotics sector. Our focus revolves around enhancing geometric path planning for mobile robots, a pivotal aspect of automation. Notably, we not only delve into how to formulate optimal navigation problems while considering practical constraints and applications, but we also introduce a novel Smoothed PSO-IPF algorithm. This algorithm serves as an illustrative example of an innovative and context-specific approach to addressing navigation challenges. It furnishes engineers and practitioners in the field with a comprehensive framework for designing navigational solutions. By presenting the PSO-IPF method as a hybrid approach, we effectively bridge the gap between classical and reactive methods. Consequently, it leads to enhanced navigation efficiency, reduced collisions, and heightened mobile robot reliability. This innovation not only optimizes navigation issues but also underscores its potential for diverse applications across various industries.</p
... Global motion algorithms, including the Ant Colony algorithm , the A-STAR algorithm , and the RRT algorithm , usually, these algorithms necessitate prior knowledge of the environment to determine the most efficient routine. Conversely, local motion planning algorithms such as the Dynamic Window Approach (DWA) and the Artificial Potential Field Approach (APF) are utilized in environments that are either unknown or partially known, where the system receives real-time data from sensors regarding obstacles in the environment and plans its path accordingly [1][2][3][4][5]. These local algorithms allow for more flexibility, adaptability, and responsiveness when navigating complex environments. ...
Path planning is a crucial aspect of agent (robot) automation, as its efficacy directly affects the quality of tasks performed. This paper proposes an innovative and efficient path planning method by merging the advantages of the A-STAR and the artificial potential field (APF) technique. The proposed method of calculation aims to enhance the traditional A-STAR approach by incorporating an artificial potential field system. Specifically, the estimated cost in the conventional A-STAR approach is ameliorated through the integration of a precisely designed estimated cost gain. The gain is determined by the direction of the force in the artificial potential field., thus enabling the algorithm to focus more accurately on the direction of the target location while avoiding obstacles during path exploration. Through simulation results, the improved algorithm proves its feasibility by maintaining the same path length while reducing the running time by decreasing multiple exploration directions. The improved algorithm, which is more efficient, surpasses the conventional A-STAR and can be utilized for agent trajectory planning in static and complex environments, showcasing its superior performance.
... Khatib [75] was the first to apply the artificial potential field (APF) approach to path planning for mobile robots. This approach is suitable for real-time control of robots because of its clear physical meaning and simple mathematical description. ...
The use of Unmanned Aerial Vehicles (UAVs) has become popular in recent years, especially for their potential in various practical applications, but for their use to become a reality this context, it is necessary to study about it. One of the main problems involving UAVs, regardless of the application to which it will be used, is path planning, which is crucial to ensure safety, economy, and effectiveness. In this study we present a literature review on the optimization problem path planning and the methods used to solve it. To this end, we seek to explore the existing papers in literature on this topic, identifying mathematical models, analyzing characteristics of the objective function, types of obstacles, number of UAVs considered, the nature of the solution adopted and deployments and integration in the Internet of Drones (IoD). A comparative analysis of the works analyzed was presented in the form of tables for each path planning technique considered. In addition, some advantages and safety of the methods were also listed. We furthermore present a set of open research challenges, high-level insights, and future research directions related to the UAV path planning problem in the context of IoD. This study contributes deeply with the advancement of state of art regarding the path planning strategies on the Internet of Drones since we provide a thorough analysis of characteristics of the mathematical models used in the UAV path planning problem reviewing papers published in relevant journals and conferences in the last 4 years (2018 to 2022), highlighting the advantages and disadvantages of each method as well as the possibilities of implementation and integration with IoD.
... Incorporating risk criteria and field computation into control algorithms can prevent robot collisions. For instance, potential field methods define repulsive vector fields that guide robot motion, adjusting the trajectory based on changing dynamic environmental factors to ensure safety during complex behaviors (Khatib, 1986;Kovács et al., 2016). This method allows for more intricate collision avoidance beyond adjusting the robot's speed. ...
Faced with the increasingly severe global aging population with fewer children, the research, development, and application of elderly-care robots are expected to provide some technical means to solve the problems of elderly care, disability and semi-disability nursing, and rehabilitation. Elderly-care robots involve biomechanics, computer science, automatic control, ethics, and other fields of knowledge, which is one of the most challenging and most concerned research fields of robotics. Unlike other robots, elderly-care robots work for the frail elderly. There is information exchange and energy exchange between people and robots, and the safe human-robot interaction methods are the research core and key technology. The states of the art of elderly-care robots and their various nursing modes and safe interaction methods are introduced and discussed in this paper. To conclude, considering the disparity between current elderly care robots and their anticipated objectives, we offer a comprehensive overview of the critical technologies and research trends that impact and enhance the feasibility and acceptance of elderly care robots. These areas encompass the collaborative assistance of diverse assistive robots, the establishment of a novel smart home care model for elderly individuals using sensor networks, the optimization of robot design for improved flexibility, and the enhancement of robot acceptability.
... Local path planning includes dynamic window algorithm, artificial potential field (APF) method, etc., which is suitable for path planning in real-time environments where the environment is unknown or partially unknown. As a popular path planning algorithm proposed by Khatib [1] in 1985, APF has many advantages, such as a mathematical model that is simple and easy to implement, small amount of calculation, good adaptability to unknown or dynamic environments, short search time, good real-time performance and so on. It is widely used in active and realtime avoidance of collision [2], [3] and path planning [4], [5], [6] of AMRs. ...
Aiming at the shortcomings of the path generated by the artificial potential field (APF) method, such as local minimum, target unreachability, and low path smoothness, an improved artificial potential field method is proposed. First, to reduce the collision risk and planning difficulty, based on known environmental information such as the location of obstacles and targets, the area with fewer obstacles is selected as the priority area for path planning. Second, to improve the path smoothness and reduce the computation amount, an adaptive step-size adjustment method based on the distance and angle relationship with obstacles within the prediction range is proposed. Third, in view of the effect on each other between obstacle, local minimum, and unsmooth path, a multi-target model considering the size and influence range of obstacles and an improved potential field function are proposed on the basis of the identified planning priority area. Finally, in order that the path is smooth enough to be tracked by autonomous mobile robots, a safe driving corridor without collision with obstacles is constructed on the planned path, and a trajectory fully constrained to the safe driving corridor is generated using the quadratic programming method. Compared with the traditional APF algorithm by using matlab simulation software, the improved APF algorithm can effectively solve the problems of local minimum and target unreachability, generate collision-free trajectories with better smoothness, and have better real-time performance.
... The concept of this method was first introduced in [17]. A robot in this method is considered as a point in a configuration space that is subjected under the action of an artificial potential function (U) whose variations are seen as the structure of the free space. ...
Mobile robots have applications in military (for reconnaissance, search and rescue operations, bomb detection, surveillance), transportation (for cargo and packet delivery), data acquisition, etc. For the mobile robots to be able to execute these tasks with minimum or no human intervention, they need to be autonomous and intelligent. Path planning (PP) is one of the most critical areas of concern in the field of autonomous mobile robots. It is about obtaining a collision-free motion optimal path based on either time, distance, energy or cost in a static or dynamic environment containing obstacles. However, power limitation hinders the mobile robots to accomplish their task of reaching the target location as there are several paths they can follow. Each of these paths has its own path length, cost (i.e., time to reach destination), and energy constraint, thus, the need to plan for an optimal path according to a certain performance criterion. Significant research has been conducted in recent years to address the PP problem. Hence, this chapter is aimed at presenting the different approaches for PP of mobile robots with respect to different optimality criteria (time, distance, energy and cost), challenges and making recommendations on possible areas of future research.
... In the literature, there exist two types of popular techniques in designing the control law for obstacle avoidance. Briefly speaking, one is based on reactive behaviors (online), while the other is derived by re-planning (mostly offline) [14][15][16]. However, it is worth noting that the limitations of communication and computation bandwidth must be considered when involving reactive behaviors in the control design. ...
In this paper, the formation control problem for a multi-agent system is studied. Two new robust control algorithms for serial and parallel formations respectively are proposed, which take the constraints of limited field of view into consideration. Without the need for any global information, the only relative information required is distance and bearing angle, thus is easy to implement with onboard directional sensors. It is then demonstrated how complex formations can be realized by combining the proposed basic controllers. Finally, effectiveness of the proposed algorithms is illustrated by numerical examples.
... An example is the Proportional-integral-derivative (PID) controller (131), which uses current state information to compute the next input to the system. This can be combined with repulsive forces to push the system away from obstacle regions, as in the artificial potential fields approach (21). More global approaches are the Linear-quadratic regulator (LQR) method (132) which optimizes a (locally) optimal path, and the Model predictive control (MPC) method (133) which can find optimal path segments over a receding horizon. ...
Sampling-based motion planning is one of the fundamental paradigms to generate robot motions, and a cornerstone of robotics research. This comparative review provides an up-to-date guide and reference manual for the use of sampling-based motion planning algorithms. It includes a history of motion planning, an overview of the most successful planners, and a discussion of their properties. It also shows how planners can handle special cases and how extensions of motion planning can be accommodated. To put sampling-based motion planning into a larger context, a discussion of alternative motion generation frameworks highlights their respective differences from sampling-based motion planning. Finally, a set of sampling-based motion planners are compared on 24 challenging planning problems in order to provide insights into which planners perform well in which situations and where future research would be required. This comparative review thereby provides not only a useful reference manual for researchers in the field but also a guide for practitioners to make informed algorithmic decisions.
Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 7 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
... One of the first and widely used local planning approaches for collision-free trajectory generation are artificial potential field methods proposed in [26]. These methods are mainly used for single-arm manipulation [27] and consist of generating a potential field with repulsive terms in the vicinity of obstacles and attracting terms at the target point. ...
We present a model predictive control (MPC) algorithm for online time-optimal trajectory planning of cooperative robotic manipulators. Robotic arms sharing a common confined operational space are exposed to high interrobot collision risks. For collision avoidance, a smooth robot geometry approximation by Bézier curves is applied, utilizing velocity constraints and tangent separating planes, enabling an efficient generation of robot trajectories in real-time. The proposed optimization algorithm is validated on an experimental setup consisting of two collaborative robotic arms performing synchronous pick-and-place tasks.
... Обычно для управления роем БПЛА с уклонением от препятствий используется метод искусственного потенциального поля, представленный в работе [19] (1986), где задача обхода препятствий была перенесена из высокоуровневого построения траектории на низкий оперативный уровень. Искус-ственное потенциальное поле, описываемое в данной работе, состоит из точки притяжения на месте цели и точек отталкивания на месте препятствий. ...
развитие и внедрение автономных транспортных средств требует создания модели смешанного транспортного потока. Разрабатываемая модель может использоваться для оценки влияния автопилотов на транспортный поток, а в дальнейшем – для управления транспортной сетью. Существующих моделей, созданных на основе водителей-людей, недостаточно. Модификации этих моделей сводятся к уменьшению в два раза параметров скорости реакции и дистанции до впереди идущеготранспортного средства для имитации беспилотного транспорта. Большое внимание в современных исследованиях уделяется подключению автомобилей к единой сети для обмена информацией. В статье рассмотрены подходы к моделированию смешанного транспортного потока и прогнозирования объема трафика, решению задач управления транспортной системой в целом и автономными транспортными средствами и летательными аппаратами в частности: избежания столкновений, планирования маршрута, ориентирования. Условно выделены и охарактеризованы классы этих подходов и сделан вывод о направлении дальнейших разработок.
as autonomous vehicles become common, we need a simulation model for a mixed traffic flow. The proposed model can be used to assess the impact of autonomous vehicles on the traffic flow, and to manage the future traffic. The existing models for human-driven vehicles are insufficient. Such models are usually adapted by halving the driver reaction speed and distance to the vehicle ahead to simulate an autonomous vehicle. Much of the current research is focused on connected vehicles. This study considers some approaches to the simulation of mixed traffic flows and traffic prediction, the management of the entire transportation network, autonomous surface vehicles and aircraft (collision avoidance, route planning, and navigation). The existing strategies are categorized and suggestions for further developments are made.
... In 1986, Khatib proposed the artificial potential field algorithm and introduced it into the field of robot path planning [21]. However, this method has some problems, such as unreachable target position, local extreme value, failure to consider obstacle motion state and vehicle motion constraint, etc., which greatly reduces the safety of intelligent vehicles using the artificial potential field method in the actual obstacle avoidance process and the success rate of path planning for obstacle avoidance [22]. ...
Establishing an accurate and computationally efficient model for driving risk assessment, considering the influence of vehicle motion state and kinematic characteristics on path planning, is crucial for generating safe, comfortable, and easily trackable obstacle avoidance paths. To address this topic, this paper proposes a novel dual-layered dynamic path-planning method for obstacle avoidance based on the driving safety field (DSF). The contributions of the proposed approach lie in its ability to address the challenges of accurately modeling driving risk, efficient path smoothing and adaptability to vehicle kinematic characteristics, and providing collision-free, curvature-continuous, and adaptable obstacle avoidance paths. In the upper layer, a comprehensive driving safety field is constructed, composed of a potential field generated by static obstacles, a kinetic field generated by dynamic obstacles, a potential field generated by lane boundaries, and a driving field generated by the target position. By analyzing the virtual field forces exerted on the ego vehicle within the comprehensive driving safety field, the resultant force direction is utilized as guidance for the vehicle’s forward motion. This generates an initial obstacle avoidance path that satisfies the vehicle’s kinematic and dynamic constraints. In the lower layer, the problem of path smoothing is transformed into a standard quadratic programming (QP) form. By optimizing discrete waypoints and fitting polynomial curves, a curvature-continuous and smooth path is obtained. Simulation results demonstrate that our proposed path-planning algorithm outperforms the method based on the improved artificial potential field (APF). It not only generates collision-free and curvature-continuous paths but also significantly reduces parameters such as path curvature (reduced by 62.29% to 87.32%), curvature variation rate, and heading angle (reduced by 34.11% to 72.06%). Furthermore, our algorithm dynamically adjusts the starting position of the obstacle avoidance maneuver based on the vehicle’s motion state. As the relative velocity between the ego vehicle and the obstacle vehicle increases, the starting position of the obstacle avoidance path is adjusted accordingly, enabling the proactive avoidance of stationary or moving single and multiple obstacles. The proposed method satisfies the requirements of obstacle avoidance safety, comfort, and stability for intelligent vehicles in complex environments.
... For instance, [20] proposes to encode motion and impedance behaviors in a potential field learnt from human demonstrations. This strategy is inspired by the popular potential field approach [21]. In [17], the authors reformulate a GMM into a closed-loop control policy with a state-varying spring and a damper, while [22] proposes to follow a path encoded in a velocity field, with a virtual fly-wheel used to ensure the passivity of the overall system. ...
Recently, several approaches have attempted to combine motion generation and control in one loop to equip robots with reactive behaviors, that cannot be achieved with traditional time-indexed tracking controllers. These approaches however mainly focused on positions, neglecting the orientation part which can be crucial to many tasks e.g. screwing. In this work, we propose a control algorithm that adapts the robot's rotational motion and impedance in a closed-loop manner. Given a first-order Dynamical System representing an orientation motion plan and a desired rotational stiffness profile, our approach enables the robot to follow the reference motion with an interactive behavior specified by the desired stiffness, while always being aware of the current orientation, represented as a Unit Quaternion (UQ). We rely on the Lie algebra to formulate our algorithm, since unlike positions, UQ feature constraints that should be respected in the devised controller. We validate our proposed approach in multiple robot experiments, showcasing the ability of our controller to follow complex orientation profiles, react safely to perturbations, and fulfill physical interaction tasks.
... Fig. 2 illustrates the concept of an artificial PF algorithm. In the traditional APF algorithm introduced by Khatib in 1986, the avoidance of collisions is achieved through the utilization of attractive and repulsive forces [26]. The attractive force, represented as a force vector, is directed towards the desired target location to guide the robot in reaching its destination. ...
The Adaptive Collision Avoidance Algorithm Based on the Estimated Collision Time (ACACT) is proposed in this paper, representing a novel approach designed for effective and efficient collision avoidance and path planning in highly dynamic environments, notably those with swarm Unmanned Aerial Vehicles (UAVs). The fundamental challenge in swarm UAV operations revolves around dynamic collision avoidance and nimble path planning. Addressing this, the ACACT algorithm exhibits the capability of predicting imminent collisions by estimating their likely occurrence times and then adeptly adjusting the UAVs’ trajectories in real-time. A significant facet of the algorithm is the employment of adaptive target velocity, updated in accordance with the predicted collision timelines. This ensures not only that UAVs can sidestep potential collisions but also that they can pursue more direct and efficient routes in comparison to conventional methodologies. Highlighting its superiority over existing techniques, the ACACT algorithm successfully resolves some long-standing issues linked with the Artificial Potential Field (APF) method, especially concerning unreachability and oscillation. This is accomplished by integrating a strategic contingency plan coupled with enhanced obstacle navigation, particularly in proximity to target locations. For a comprehensive evaluation of the algorithm’s prowess in collision avoidance and path planning, a novel metric named the Path Traveling Time Ratio (PTTR) is introduced. PTTR assesses both the traveling time taken for a vehicle to reach its target position and the duration it spends within collision-prone zones. This metric offers a more advanced evaluation method than merely comparing path lengths, collision counts, or traveling times. Through rigorous experimentation, it is observed that the ACACT algorithm enhances collision avoidance and path planning by an impressive margin of up to 20% compared to its traditional counterparts. Furthermore, a distinct advantage of the ACACT is its ability to uniformly tackle obstacles, irrespective of their speeds and independent of PID gain variations. It not only boosts the safety parameters but also amplifies operational efficiency, setting new benchmarks for UAVs to reach their target points with swiftness and security.
... However, in dynamic environments where obstacles and conditions change rapidly, reliance on such a controller can be limiting. A significant contribution to the field was made by Khatib [5], who introduced artificial potential fields to enable collision avoidance during not only the motion planning stage but also the realtime control of a mobile robot. Later, Rimon and Koditschek [6] developed navigation functions, a particular form of artificial potential functions that guarantees simultaneous collision avoidance and stabilization to a goal configuration. ...
This paper addresses the challenge of safe navigation for rigid-body mobile robots in dynamic environments. We introduce an analytic approach to compute the distance between a polygon and an ellipse, and employ it to construct a control barrier function (CBF) for safe control synthesis. Existing CBF design methods for mobile robot obstacle avoidance usually assume point or circular robots, preventing their applicability to more realistic robot body geometries. Our work enables CBF designs that capture complex robot and obstacle shapes. We demonstrate the effectiveness of our approach in simulations highlighting real-time obstacle avoidance in constrained and dynamic environments for both mobile robots and multi-joint robot arms.
In this study, to address the issues of sluggish convergence and poor learning efficiency at the initial stages of training, the authors improve and optimise the Deep Deterministic Policy Gradient (DDPG) algorithm. First, inspired by the Artificial Potential Field method, the selection strategy of DDPG has been improved to accelerate the convergence speed during the early stages of training and reduce the time it takes for the mobile robot to reach the target point. Then, optimising the neural network structure of the DDPG algorithm based on the Long Short‐Term Memory accelerates the algorithm's convergence speed in complex dynamic scenes. Static and dynamic scene simulation experiments of mobile robots are carried out in ROS. Test findings demonstrate that the Artificial Potential Field method‐Long Short Term Memory Deep Deterministic Policy Gradient (APF‐LSTM DDPG) algorithm converges significantly faster in complex dynamic scenes. The success rate is improved by 7.3% and 3.6% in contrast to the DDPG and LSTM‐DDPG algorithms. Finally, the usefulness of the method provided in this study is similarly demonstrated in real situations using real mobile robot platforms, laying the foundation for the path planning of mobile robots in complex changing conditions.
Intelligent mobile robotic agents demand optimal motion planners with minimal query time. Most contemporary algorithms lack one of these two required aspects. This paper proposes an efficient path-planning scheme based on cellular automata (CA) that generates optimal paths in the minimum time. A Cellular automaton is evolved over the entire search space and subsequently used for the determination of the shortest path. This approach generates a parent-child relationship for each cell in order to minimize the search time. Performance comparisons with A*, Dijkstra's, D* and MPCNN have proven it to be time-efficient. Analysis, simulation and experimental results have proven it to be a robust and complete path-planning scheme. Also it has demonstrated to be time-efficient in both static and dynamic environments.
Most studies on path planning of robotic arm focus on obstacle avoidance at the end position of robotic arm, while ignoring the obstacle avoidance of robotic arm joint linkage, and the obstacle avoidance method has low flexibility and adaptability. This paper proposes a path obstacle avoidance algorithm for the overall 6-DOF robotic arm that is based on the improved A* algorithm and the artificial potential field method. In the first place, an improved A* algorithm is proposed to address the deficiencies of the conventional A* algorithm, such as a large number of search nodes and low computational efficiency, in robotic arm end path planning. The enhanced A* algorithm proposes a new node search strategy and local path optimization method, which significantly reduces the number of search nodes and enhances search efficiency. To achieve the manipulator joint rod avoiding obstacles, a method of robotic arm posture adjustment based on the artificial potential field method is proposed. The efficiency and environmental adaptability of the robotic arm path planning algorithm proposed in this paper are validated through three types of simulation analysis conducted in different environments. Finally, the AUBO-i10 robotic arm is used to conduct path avoidance tests. Experimental results demonstrate that the proposed method can make the manipulator move smoothly and effectively plan an obstacle-free path, proving the method’s viability.
Autonomous dynamic obstacle avoidance of unmanned aerial vehicles (UAVs) based on collision prediction is critical for stable flight missions. UAVs must deal with high‐speed and nonlinearly moving obstacles that obey nonlinear dynamics, which are one of the numerous objects affecting stable flight. To satisfy the requirements of smooth flight and attitude stability, a novel dynamic smooth obstacle avoidance (QVA) method based on obstacle trajectory prediction is proposed. To predict the dynamic obstacle trajectories, a trajectory prediction (QLTP) method using a quasi‐linear parameter varying representations is proposed. The proposed QVA integrates the QLTP approach, velocity obstacle (VO) approach, and artificial potential field (APF) methods. The QVA detects an imminent collision based on the QLTP method, and then replans the UAV path based on the VO method at the time of the predicted collision. The UAV tracks planned waypoints for collision avoidance. To ensure the flight safety of the UAV, a virtual APF is constructed with waypoints as local targets and obstacles. The simulation results show that the proposed method performs better than the improved APF and VO methods in terms of the smoothness of the obstacle avoidance path and the stability of the UAV attitude.
Motion planning is a very important part of robot technology, where the quality of planning directly affects the energy consumption and safety of robots. Focusing on the shortcomings of traditional RRT methods such as long, unsmooth paths, and uncoupling with robot control system, an automatic robot motion planning method was proposed based on Rapid Exploring Random Tree called AM-RRT* (automatic motion planning based on RRT*). First, the RRT algorithm was improved by increasing the attractive potential fields of the target points of the environment, making it more directional during the sampling process. Then, a path optimization method based on a dynamic model and cubic B-spline curve was designed to make the planned path coupling with the robot controller. Finally, an RRT speed planning algorithm was added to the planned path to avoid dynamic obstacles in real time. To verify the feasibility of AM-RRT*, a detailed comparison was made between AM-RRT* and the traditional RRT series algorithms. The results showed that AM-RRT* improved the shortcomings of RRT and made it more suitable for robot motion planning in a dynamic environment. The proposal of AM-RRT* can provide a new idea for robots to replace human labor in complex environments such as underwater, nuclear power, and mines.
Secure and reliable active debris removal methods are crucial for maintaining the stability of the space environment. Continuum robots, with their hyper-redundant degrees of freedom, offer the ability to capture targets of varying sizes and shapes through whole-arm grasping, making them well-suited for active debris removal missions. This paper proposes a pre-grasping motion planning method for continuum robots based on an improved artificial potential field to restrict the movement area of the grasping target and prevent its escape during the pre-grasping phase. The analysis of the grasping workspace ensures that the target is within the workspace when starting the pre-grasping motion planning by dividing the continuum robot into delivery and grasping segments. An improved artificial potential field is proposed to guide the continuum robot in surrounding the target and creating a grasping area. Specifically, the improved artificial potential field consists of a spatial rotating potential field, an attractive potential field incorporating position and posture potential fields, and a repulsive potential field. The simulation results demonstrate the effectiveness of the proposed method. A comparison of motion planning results between methods that disregard and consider the posture potential field shows that the inclusion of the posture potential field improves the performance of pre-grasping motion planning for spatial targets, achieving a success rate of up to 97.8%.
The subject of this research is navigation of a compartmentalized robot made up of several structures which are car-like with a global rigid formation. The system must move safely to a predetermined target in an already-known workspace that is filled with obstacles. A target convergence and obstacle avoidance method is put forth that is effective for any quantity of obstacles. The smallest distance between an obstacle and the point closest to it on each line segment that forms the rectangular protected zone of the car-like units can be calculated analytically using the minimum distance technique, as proposed in this paper. The Lyapunov-based control scheme (LbCS) is used to construct continuous acceleration-based controls. The computer simulations are presented to validate the proposed controllers, and the system demonstrates the rigorous upkeep of a rigid formation. The successful presentation of controllers opens further research in developing and applying controllers by considering various compartmentalized robots in swarming models, splitting and rejoining units, and applying rotational leadership concepts.
Robots can traverse sparse obstacles by sensing environmental geometry and avoiding contact with obstacles. However, for search and rescue in rubble, environmental monitoring through dense vegetation, and planetary exploration over Martian and lunar rocks, robots must traverse cluttered obstacles as large as themselves by physically interacting with them. Previous work discovered that the forest floor-dwelling discoid cockroach and a sensor-less minimalistic robot can traverse cluttered grass-like beam obstacles of various stiffness by transitioning across different locomotor modes. Yet the animal was better at traversal than the sensor-less robot, likely by sensing forces during obstacle interaction to control its locomotor transitions. Inspired by this, here we demonstrated in simulation that environmental force sensing helps robots traverse cluttered large obstacles. First, we developed a multi-body dynamics simulation and a physics model of the minimalistic robot interacting with beams to estimate the beam stiffness from sensed contact forces. Then, we developed a force feedback strategy for the robot to use the sensed beam stiffness to choose the locomotor mode with a lower mechanical energy cost. With feedforward pushing, the robot was stuck in front of stiff beams if it has a limited force capacity; without force limit, it traversed but suffered a high energy cost. Using obstacle avoidance, the robot traversed beams by avoiding beam contact regardless of beam stiffness, resulting in a high energy cost for flimsy beams. With force feedback, the robot determined beam stiffness, then traversed flimsy beams by pushing them over and stiff beams by rolling through the gap between them with a low energy cost. Stiffness estimation based on force sensing was accurate across varied body oscillation amplitude and frequency and position sensing uncertainty. Mechanical energy cost of traversal increased with sensorimotor delay. Future work should demonstrate cluttered large obstacle traversal using force feedback in a physical robot.
We present a haptic guidance system for teleoperating a robot arm controlled by inverse kinematics. Unlike drones or vehicles, a robot arm occupies 3D space dynamically changing with its various configurations. With limited information on the remote environment and its current configuration, the remotely controlled robot arm has a higher chance of colliding with the surroundings. Consequently, users need to maintain a high level of attention, which results in fatigue during operation. In this paper, we propose a system of haptic force guidance that is robust to both the robot arm configuration and its surroundings. Our system first computes the guiding forces at multiple points in the robot arm using ray-based depth sampling. Then, haptic force feedback is generated by aggregating guiding forces using a motion-based approximate Jacobian. Our system requires minimal prior information about the environment and the robot arm. Moreover, the proposed ray-based depth sampling method is more efficient in computation time than the widely used potential field-based approach. User studies show that our system reduces the risk of collisions, as well as the mental workload during teleoperation in a virtual environment.
Teleoperation has emerged as an alternative solution to fully-autonomous systems for achieving human-level capabilities on humanoids. Specifically, teleoperation with whole-body control is a promising hands-free strategy to command humanoids but requires more physical and mental demand. To mitigate this limitation, researchers have proposed shared-control methods incorporating robot decision-making to aid humans on low-level tasks, further reducing operation effort. However, shared-control methods for wheeled humanoid telelocomotion on a whole-body level has yet to be explored. In this work, we explore how whole-body bilateral feedback with haptics affects the performance of different shared-control methods for obstacle avoidance in diverse environments. A time-derivative Sigmoid function (TDSF) is implemented to generate more intuitive haptic feedback from obstacles. Comprehensive human experiments were conducted and the results concluded that bilateral feedback enhances the whole-body telelocomotion performance in unfamiliar environments but could reduce performance in familiar environments. Conveying the robot's intention through haptics showed further improvements since the operator can utilize the feedback for reactive short-distance planning and visual feedback for long-distance planning.
Planning the path of a mobile robot that must transport and deliver small packages inside a multi-story building is a problem that requires a combination of spatial and operational information, such as the location of origin and destination points and how to interact with elevators. This paper presents a solution to this problem, which has been formulated under the following assumptions: (1) the map of the building’s floors is available; (2) the position of all origin and destination points is known; (3) the mobile robot has sensors to self-localize on the floors; (4) the building is equipped with remotely controlled elevators; and (5) all doors expected in a delivery route will be open. We start by defining a static navigation tree describing the weighted paths in a multi-story building. We then proceed to describe how this navigation tree can be used to plan the route of a mobile robot and estimate the total length of any delivery route using Dijkstra’s algorithm. Finally, we show simulated routing results that demonstrate the effectiveness of this proposal when applied to an autonomous delivery robot operating in a multi-story building.
To address an autonomous guided vehicle problem, this article presents extended variants of the established block over-relaxation method known as the Block Modified Two-Parameter Over-relaxation (B-MTOR) method. The main challenge in handling autonomous-driven vehicles is to offer an efficient and reliable path-planning algorithm equipped with collision-free feature. This work intends to solve the path navigation with obstacle avoidance problem explicitly by using a numerical approach, where the mobile robot must project a route to outperform the efficiency of its travel from any initial position to the target location in the designated area. The solution builds on the potential field technique that uses Laplace’s equation to restrict the formation of potential functions across operating mobile robot regions. The existing block over-relaxation method and its variants evaluate the computation by obtaining four Laplacian potentials per computation in groups. These groups can also be viewed as groups of two points and single points if they’re close to the boundary. The proposed B-MTOR technique employs red-black ordering with four different weighted parameters. By carefully choosing the optimal parameter values, the suggested B-MTOR improved the computational execution of the algorithm. In red-black ordering, the computational molecules of red and black nodes are symmetrical. When the computation of red nodes is performed, the updated values of their four neighbouring black nodes are applied, and conversely. The performance of the newly proposed B-MTOR method is compared against the existing methods in terms of computational complexity and execution time. The simulation findings reveal that the red-black variants are superior to their corresponding regular variants, with the B-MTOR approach giving the best performance. The experiment also shows that, by applying a finite difference method, the mobile robot is capable of producing a collision-free path from any start to a given target point. In addition, the findings also verified that numerical techniques could provide an accelerated solution and have generated a smoother path than earlier work on the same issue.
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