Rafal Szczepanski

Purpose: This paper addresses the problem of efficient and safe local path planning for mobile robots operating in environments with unknown and randomly distributed obstacles. The purpose of this study is to develop a computationally lightweight local path planner that is designed to support obstacle avoidance, smooth goal-reaching behaviour and reliable waypoint tracking in cluttered environments. Design/methodology/approach: The proposed method is developed on the basis of the artificial potential field algorithm and extends the classical formulation by incorporating future position prediction, vortex potential, a single constrained quadratic optimization problem for control signal selection and a mechanism for maintaining a safe distance from obstacles. The algorithm is evaluated in Matlab using randomly generated obstacle configurations for both the standalone local path planning problem and the waypoint tracking problem. Its performance is compared with that of selected existing local path planning methods. In addition, the proposed approach is validated experimentally using a real mobile robot. Findings: The obtained results indicate that the proposed local path planner achieves low goal-reaching time, short path length, satisfactory path smoothness and accurate waypoint tracking. The method maintained repeatable obstacle-distance behaviour in the evaluated scenarios while requiring relatively low computational effort. The AAPF guidance field is used to generate a predicted trajectory, which is then checked against the proposed safety-distance criterion. Originality/value: The main contribution of this work is the development of an efficient local path planning algorithm that combines safety-distance-aware obstacle avoidance, prediction-based motion planning and constrained optimization-based control signal generation within a unified framework. The proposed method offers a favourable balance between navigation performance, safety and computational complexity, which makes it suitable for real-time implementation in mobile robotic systems.

Purpose: This paper aims to address local path planning for autonomous mobile robots among static and dynamic obstacles, aiming to improve the robustness of the Artificial Potential Field (APF) algorithm against collisions with approaching obstacles. Design/methodology/approach: A modification of the Safe APF (SAPF), called the Velocity-Scaled Safe Artificial Potential Field (VSSAPF), is proposed, in which the repulsive gain is scaled by the closing velocity, estimated online from successive distance measurements and smoothed by an exponential low-pass filter. Simulations sweep the obstacle speed from 0% to 100% of the robot’s maximum linear velocity along straight-line and circular trajectories, against potential-field baselines and the Velocity Obstacle (VO) method, followed by validation on a Husarion ROSbot 2.0 PRO. Findings: Among the potential-field methods, VSSAPF attains the highest simulated success rate, 74.3% against 42.6% and 61.4% for APF with default and retuned gain, and 30.7%, 45.5% and 49.5% for SAPF, Vortex Potential Field and Adaptive SAPF. The VO reaches 100% on the straight-line trajectory, which matches its constant-velocity prediction model exactly, but only 51.7% on the circular one, where VSSAPF reaches 80.3% and is never outperformed. On hardware the mechanism is confirmed up to half the robot’s maximum speed. Beyond that every tested method collides. Originality/value: The method introduces velocity-aware repulsive gain scaling requiring no prediction of the obstacle trajectory, no offline tuning and no sensors beyond distance measurements. Assuming nothing about how the obstacle moves, its performance degrades gradually rather than abruptly once the obstacle leaves a straight-line path, precisely where model-based schemes lose their advantage.

Robotic systems are increasingly expected to operate autonomously, safely, and efficiently in dynamic and uncertain environments

Local path planning algorithms for autonomous mobile robots (AMRs) have gained significant attention in recent years. One of the most commonly used approaches is the Artificial Potential Field (APF) algorithm. Its popularity is based on its simplicity, low computational cost, and efficiency. Nevertheless, the classical APF has some well-known drawbacks. First, the APF generates oscillations near obstacles. Moreover, it is sensitive to parameter tuning. The APF algorithm also exhibits poor performance in narrow passages and stagnates in local minima. Therefore, its applicability in modern AMR systems is limited. This paper presents a review of recent applications and modifications of the APF algorithm. First, the original APF algorithm is presented. Next, recent modifications of repulsive strategies, additional potentials, and adaptation strategies are discussed. The integration of APF with additional mechanisms, such as prediction, optimization, is also presented. Moreover, this review includes shared-control and teleoperation schemes in which APF is used as a collision-avoidance layer. The paper concludes with a discussion of open challenges and promising research directions. This review aims to provide a structured reference for practitioners and a roadmap for future APF-based research.

Permanent Magnet Synchronous Motors (PMSMs) require precise tuning of the state feedback controller to ensure optimal performance. This paper evaluates the robustness of an automatic tuning approach based on nature-inspired optimization algorithms over a wide range of motor power ratings, from 60 W to 8.17 kW. Three objective functions with up to two weighting factors are analyzed, and normalization is introduced to reduce the impact of these factors on the autotuning procedure. The results show that normalization partly improves consistency.

Collision avoidance is a crucial aspect of autonomous ground vehicles (AGVs). One of the most common algorithms is called the dynamic window approach (DWA). The algorithm enables AGVs to operate with high performance in an unknown environment with a particular emphasis on achieving maximum linear and angular acceleration. However, DWA requires high computational effort to examine all possibilities with high resolution, and then select the best possible pair of control signals, i.e., linear and angular velocities. In this paper, the Pattern Search (PS) optimization algorithm is used to reduce the computational requirement of the DWA. Instead of calculating the DWA objective function for each possibility, the PS is used for algorithmically selecting the next examined pair of control signals. The results obtained demonstrate that a similar resolution of control signals can be achieved with almost two times less computational effort. The proposed approach has been examined in the MATLAB environment, while the source code is available on the MathWorks FileExchange.

This paper uses a nature-inspired optimization algorithm to discuss the automatic selection of control structure parameters. The commonly used quality indicators are presented and analyzed for the optimization process of the control system. Moreover, the possibilities of formulating objective functions for nature-inspired optimization algorithms that can be successfully used to solve multi-objective constrained optimization problems are presented. The proposed general methodology was presented and discussed in detail using an example, which is published in the open-source repository Mathwork FileExchange. Theoretical aspects are validated in the case studies for automatic tuning of the hysteresis and PI controller.

This paper proposes a method for solving the path planning problem for a collaborative robot. The time-optimal, smooth, collision-free B-spline path is obtained by the application of a nature-inspired optimization algorithm. The proposed approach can be especially useful when moving items that are delicate or contain a liquid in an open container using a robotic arm. The goal of the optimization is to obtain the shortest execution time of the production cycle, taking into account the velocity, velocity and jerk limits, and the derivative continuity of the final trajectory. For this purpose, the velocity profiling algorithm for B-spline paths is proposed. The methodology has been applied to the production cycle optimization of the pick-and-place process using a collaborative robot. In comparison with point-to-point movement and the solution provided by the RRT* algorithm with the same velocity profiling to ensure the same motion limitations, the proposed path planning algorithm decreased the entire production cycle time by 11.28% and 57.5%, respectively. The obtained results have been examined in a simulation with the entire production cycle visualization. Moreover, the smoothness of the movement of the robotic arm has been validated experimentally using a robotic arm.

The selection of a reference model (RM) for a Model-Reference Adaptive Control is one of the most important aspects of the synthesis process of the adaptive control system. In this paper, the four different implementations of RM are developed and investigated in an adaptive PMSM drive with variable moment of inertia. Adaptation mechanisms are based on the Widrow-Hoff rule (W-H) and the Adaptation Procedure for Optimization Algorithms (APOA). Inadequate order or inaccurate approximation of RM for the W-H rule may provide poor behavior and oscillations. The results prove that APOA is robust against an improper selection of RM and provides high-performance PMSM drive operation.