Path Planning Based on Artificial Potential Field with an Enhanced Virtual Hill Algorithm

Path planning in unknown environments is one of the most challenging research areas in robotics. In this class of path planning, the robot acquires the information from its sensory system. Sampling-based path planning is one of the famous approaches with low memory and computational requirements that has been studied by many researchers during the past few decades. We propose a sampling-based algorithm for path planning in unknown environments using Tabu search. The Tabu search component of our method guides the sampling to find the samples in the most promising areas and makes the sampling procedure more intelligent. The simulation results show the efficient performance of the proposed approach in different types of environments. We also compare the performance of the algorithm with some of the well-known path planning approaches, including Bug1, Bug2, SRT, potential fields and the visibility graph. Furthermore, two different sampling strategies were used in the sampling procedure, including uniform and Gaussian distributions.

Robot path planning in complex and unknown environment is a challenging task. To deal with this problem, an improved PSO method based on bio-inspired neural network is proposed, where a bio-inspired neural network is used to optimize the fitness function of the PSO based method. A dynamic environment modeling method is introduced to realize the path planning in unknown environment. Finally, some simulation experiments on various environments are conducted. The results show the efficiency of the proposed method.

Dynamic path planning for unmanned surface vehicle in complex offshore areas based on hybrid algorithm

13 - Experimental Studies

The ability of a robot to find a goal in an unknown environment is significantly improved from previous path planners with the synthesis of intelligence and reflexive algorithms as a path planning approach. A hybrid expert system vector field (HESV) path planner is introduced and tested in 100 map scenarios using a custom robotic simulation shell. The method combines a trap detection expert system, a low-level reflexive obstacle avoidance algorithm, and a trap evasion expert system, to achieve improved performance without sacrificing computational efficiency. Simulation results and evaluations are presented for the potential field, vector field, and HESV techniques. HESV's expert systems have been simplified to illustrate the performance improvements that result from a small amount of intelligence. Although recommendations for the structure of the path planning system are given, the purpose of the paper is to show the effectiveness of intelligent rules in path planning, rather than to define the actual rules to be used.

This paper proposes a new method, minimum risk approach, to address the local path planning to escape from local minimum during goal-oriented robot navigation in unknown environments. This approach is theoretically proved to guarantee global convergence even in the long-wall, unstructured, cluttered, maze-like, and modified environments. The approach adopts a strategy of multi-behavior coordination, in which a novel path-searching behavior is developed to recommend the regional direction with minimum risk. The paper provides a fuzzy logic framework to implement the behavior design and coordination. It is verified by the simulated and real world tests.

In recent years, topics related to robotics have become one of the researching fields. In the meantime, intelligent mobile robots have great acceptance, but the control and navigation of these devices are very difficult, and the lack of dealing with fixed obstacles and avoiding them, due to safe and secure routing, is the basic requirement of these systems. In this paper, the modified artificial potential field (APF) method is proposed for that robot avoids collision with fixed obstacles and reaches the target in an optimal path; using this algorithm, the robot can run to the target in optimal environments without any problems by avoiding obstacles, and also using this algorithm, unlike the APF algorithm, the robot does not get stuck in the local minimum. We are looking for an appropriate cost function, with restrictions that we have, and the goal is to avoid obstacles, achieve the target, and do not stop the robot in local minimum. The previous method, APF algorithm, has advantages, such as the use of a simple math model, which is easy to understand and implement. However, this algorithm has many drawbacks; the major drawback of this problem is at the local minimum and the inaccessibility of the target when the obstacles are in the vicinity of the target. Therefore, in order to obtain a better result and to improve the shortcomings of the APF algorithm, this algorithm needs to be improved. Here, the obstacle avoidance planning algorithm is proposed based on the improvement of the artificial potential field algorithm to solve this local minimum problem. In the end, simulation results are evaluated using MATLAB software. The simulation results show that the proposed method is superior to the existing solution.

An important challenge for air–ground unmanned systems achieving autonomy is navigation, which is essential for them to accomplish various tasks in unknown environments. This paper proposes an end-to-end framework for solving air–ground unmanned system navigation using deep reinforcement learning (DRL) while optimizing by using a priori information from search-based path planning methods, which we call search-based optimizing DRL (SO-DRL) for the air–ground unmanned system. SO-DRL enables agents, i.e., an unmanned aerial vehicle (UAV) or an unmanned ground vehicle (UGV) to move to a given target in a completely unknown environment using only Lidar, without additional mapping or global planning. Our framework is equipped with Deep Deterministic Policy Gradient (DDPG), an actor–critic-based reinforcement learning algorithm, to input the agents’ state and laser scan measurements into the network and map them to continuous motion control. SO-DRL draws on current excellent search-based algorithms to demonstrate path planning and calculate rewards for its behavior. The demonstrated strategies are replayed in an experienced pool along with the autonomously trained strategies according to their priority. We use a multi-stage training approach based on course learning to train SO-DRL on the 3D simulator Gazebo and verify the robustness and success of the algorithm using new test environments for path planning in unknown environments. The experimental results show that SO-DRL can achieve faster algorithm convergence and a higher success rate. We piggybacked SO-DRL directly onto a real air–ground unmanned system, and SO-DRL can guide a UAV or UGV for navigation without adjusting any networks.

Path planning in unknown environments has been a major challenge for robots. With the recent development of artificial intelligence, deep reinforcement learning has been gradually used to study the path-planning problem of robots in unknown environments due to its autonomous learning characteristics. However, deep reinforcement learning can produce a large amount of redundant data during model training, which reduces the training efficiency. To address this problem, this study proposes a path-planning algorithm based on deep reinforcement learning with a dual-experience pool playback structure. The proposed method realises the segmentation of data using two experience pools and simultaneously adjusts the ratio of experience playback to improve the speed of model training. The proposed algorithm is validated by experiments on a simulation platform using a Turtlebot3 robot. The experimental results show that the proposed algorithm performs better than the commonly used DQN and Double DQN algorithms in terms of convergence speed and accuracy.

We present an adaptive fast-approximation for sensor configuration which finds near-optimal placements and sensor field of views (FoV). The fast-approximation, either via partition-based or density-based cluster analysis, adapts based on the relation between statistical uncertainty of the path plan and environmental uncertainty. The sensor configurations are performed over regions of interest which most directly influence the path-planning efforts. These regions of interest can include exploratory paths by sampling the probabilistic environment model. The path-planning efforts aim to decide upon a path which minimizes an agent’s exposure to threats in an unknown static environment. The noisy sensor network observations are used to construct a threat field estimate using Gaussian Process Regression each iteration with a stationary kernel and heteroscedastic gaussian likelihood. The optimization of a task-driven information gain determines optimal sensor configurations when maximized. The numerical performance of the direct optimization and the adaptive cluster analysis method is presented. Finally, we show that the cluster centers can be utilized as a dimensionality reduction technique for FoV optimization whereby we only optimize FoV radial coverage.

Unmanned Aerial Vehicles (UAVs), particularly quadrotor UAVs, are widely recognized for their cost-effectiveness, operational flexibility, and vertical takeoff and landing capabilities, making them ideal for specific airspace operations and emergency response scenarios. Efficient path planning is critical for UAV mission execution, requiring optimal flight paths that ensure safe obstacle avoidance while addressing challenges such as dynamic environments, energy optimization, and multi-parameter management. Despite advancements in path planning techniques, including the artificial potential field (APF) method and reinforcement learning (RL), issues like local optima and parameter tuning complexity persist, limiting adaptability in dynamic environments. This study proposes a hybrid approach integrating Markov Decision Process (MDP) theory with an improved artificial potential field (IAPF) method to enhance quadrotor UAV path planning in three-dimensional environments. By generating global waypoints based on known obstacle, the method minimizes flight path deviations and improves navigation performance. The results demonstrate significant advancements in trajectory accuracy and adaptability, offering a robust solution for UAV path optimization in complex scenarios.

Mobile Robot Path Planning problem (MRPPP) is the most prominent research area employed in different real-time environments. In this article, a hybrid strategy is employed and validated with different environments for the path planning of mobile robots. The Artificial Potential Field (APF) algorithm and Multi-objective Genetic Algorithm (MOGA) are hybridized to solve an MRPPP. In the proposed hybrid methodology, the implementation is accomplished in three phases. To generate the initial population, an Artificial Potential Field (APF) algorithm is applied to discover all viable routes in an environment between the start and destination points. The collision-free paths are generated by determining the artificial force produced by obstacles and the target. The population-based evolutionary algorithm is employed to derive an optimal solution path from the initial population consisting of candidate paths. The selection operator is used to select the qualified subpopulation to the next generation to converge into the optimal solution. The genetic operators such as two-point crossover and mid-point mutation are exercised which are specific to the path planning problem.

Based on the artificial potential field algorithm and bacterial foraging algorithm, research method for obstacle avoidance and path planning of the mobile robot. Firstly, analysis method of bacterial foraging three operations: trends, replication, migration, establishment of bacterial foraging algorithm process; secondly establish the bacterial foraging algorithm model; and then analyze the artificial potential field algorithm and bacterial foraging the advantage and disadvantage of the algorithm, proposed a new hybrid algorithm has the global search ability and fast convergence. Using artificial potential field and bacterial foraging method on mobile robot global path planning and then compared with the mixture algorithm, based on the simulation results, the global path planning of mobile robot Simulation results show the feasibility and effectiveness of the hybrid algorithm.

Building a robot that is perform a thorough area scan can be done by applying chaotic motion. To be able to build a robot that can avoid an obstacle, it is necessary to add an obstacle avoidance algorithm. In this study, two algorithms were combined: Double-Scroll Chua Attractor and Artificial Potential Field (APF). Double-Scroll Chua Attractor was used to create the chaotic motion for mobile robot path planning. Meanwhile, the APF algorithm was used for obstacle avoidance. To get the desired result, it is necessary to constrains parameter, i.e.: initial condition, number of iteration, and speed. The obstacle used in this study was a circular obstacle with a radius of 0.001 meter. It was placed in various coordinates and the path planning of the mobile robot was able to avoid it well. The simulation results of this study show that the robot is capable of scanning the area while avoiding obstacles in certain coordinates.

In recent years, most of the research about autonomous mobile robotics is focused on obstacle detection and path planning in unknown environments. Improved accuracy in environment recognition and high precision in robot control can ensure feasibility of intelligent and autonomous mobile robotic systems in unexplored environments. To reach this goal, effective navigation and control approaches for the mobile robots must be proposed. This paper presents a path planning approach based on the navigation map reconstructed by a scout robot equipped with an onboard camera using VSLAM (visual simultaneous localization and mapping) algorithm. In the region where surveillance cameras can see blind robots equipped with no onboard cameras, these blind robots can be driven to follow the desired trajectories and reach their goal positions by the proposed visual servo control law. The proposed navigation and control system has been validated by experimenting with a blind robot using available real-time vision from the surveillance cameras.