Collision-Free Autonomous Robot Navigation in Unknown Environments Utilizing PSO for Path Planning

Several algorithms have been developed to help guide mobile robots in unknown environments. Various kinds of Bug algorithms are available and each one these algorithms has an advantage over the others under different circumstances. This paper introduces a new approach, the Diligent-Bug (D-Bug) algorithm, which is developed to enable a collision free navigation of robots in an unknown 2-dimensional environment. Static obstacles of arbitrary shapes have been considered to evaluate the developed algorithm. This algorithm also enables robots to avoid getting stuck in both local and global loops.

When autonomous mobile robot is navigating in an unknown environment, the difficulty is avoiding unexpected objects on its way to the goal. This paper presents a new parallel double-layered control system of an autonomous mobile to avoid static or dynamic objects in uncertain environments. Fuzzy control system is a simple way to lay out robot tracking rapidly. The fuzzy control system is consistency and consistency, and easily realized. Simulation results and experiments on the actual robot show that the proposed method can be efficiently applied to robot navigation in complex and unknown environments.

Autonomous navigation in unknown environments is still a challenge for robotics. Many efforts have been exerted to develop truly autonomous goal-oriented robot navigation models based on the neural mechanism of spatial cognition and mapping in animals’ brains. Inspired by the Semantic Pointer Architecture Unified Network (SPAUN) neural model and neural navigation mechanism, we developed a brain-like biologically plausible mathematical model and applied it to robotic spatial navigation tasks. The proposed cognitive navigation framework adopts a one-dimensional ring attractor to model the head-direction cells, uses the sinusoidal interference model to obtain the grid-like activity pattern, and gets optimal movement direction based on the entire set of activities. The application of adaptive resonance theory (ART) could effectively reduce resource consumption and solve the problem of stability and plasticity in the dynamic adjustment network. This brain-like system model broadens the perspective to develop more powerful autonomous robotic navigation systems. The proposed model was tested under different conditions and exhibited superior navigation performance, proving its effectiveness and reliability.

A novel navigation strategy for mobile robot in large unknown environment is put forward here. Globally, the robot's path is planned with genetic algorithm, which is better than A* algorithm and neural networks. Locally, the robot avoids obstacle and moves with some basic behaviors and path memory. With GPS and INS information fusion, the robot can go from one sub target to another sub target with enough accurate positioning. Finally, the robot reaches the target accurately with feature positioning. Finally experiments are carried out on our mobile robot in many kinds of man-made complex environments, and the navigation strategy based on global geographical planning and local feature positioning is proved successful, which give a solid support for our further research on mobile robot navigation in unknown environments.

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.

Mobile robot navigation in unknown environments is a central problem in mobile robot control and several studies were carried out in the past decades. This paper compares three mobile robot navigation methods that use fuzzy inference systems (FIS) to develop a controller for the robot: Multiple Fuzzy Inference System (MFIS), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Fuzzy Artificial Potential Field (FAPF). The mobile robot navigation task was divided into two behaviors: go-to-target and obstacle avoidance. Four scenarios were used to validate the methods: a cluttered environment, a narrow path with the robot starting from the center point between walls, a narrow path with the robot starting from the left point misaligned with the center of the two walls, and an environment with an obstacle positioned to generate a point of local minima followed by a narrow gap. Simulation results were presented using MATLAB and CoppeliaSim software and the classical Artificial Potential Field (APF) method was used as the basis for the comparison. The FAPF method used a fuzzy inference system to weigh the value of the attraction force and the repulsion force of the artificial potential fields method ended up presenting the best general performance related to the distance traveled considering all four scenarios, being the method that the robot passes more centralized when between obstacles on both sides, while the MFIS method that used two fuzzy inference systems for each behavior ended up presenting the best performance related to the execution time of the tasks for all four scenarios.

A two-layer fuzzy inference system which is capable of integrating the distance readings from different sensors and mapping the integrated results to the motion of the robot is proposed. The navigation system fuzzifies and defuzzifies the readings of the sensors and the left and right clearances of the robot were found as the result of the first-layered fuzzy system. These two signals, together with the direction of the goal relative to the robot, were used as the input of the second-stage inference system, and the linear velocity and the turning rate of the robot were returned as the final output. With the collision avoiding, obstacle following and goal trading behaviors employed inside the second-stage fuzzy inference system, robust mobile robot navigation in unknown environments can be realized. Furthermore, a multiple state property of the second-stage inference system with each state accompanied by a unique fuzzy associative memory was introduced to avoid dead end situations where the mobile robot becomes trapped between an obstacle and the goal.

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This paper proposed a PSO-based algorithm for parameters optimization of multi-robot formation navigation in unknown environment. In order to achieve formation navigation in unknown environment, each robot in formation adopts motor schema-based reactive control architecture which has four primitive behaviors called move_to_goal, keep_formation, avoid_static_obstacle and avoid_robot behaviors.The behavior output to direct the movement of robot is made by the combination of four primitive behaviors. Particle Swarm Optimization algorithm as an unsupervised learning method for a reactive control architecture greatly reduces the effort required to configure reactive control parameters of multi-robot formation system. The validity of this method is verified through computer simulations in different types of in environments by robot simulation software MissionLab.

Developing an autonomous indoor mobile robot navigation system from scratch is very difficult and it takes a long time. It requires a series of complex tasks such as handling given sensor inputs, calculating all the information needed for autonomous navigation, and controlling actuators required for movement. In this paper, an autonomous navigation system for indoor mobile robots is introduced mainly based on open source provided by the robot operating system. The presented system is capable of autonomously navigating an unstructured indoor environment avoiding collision with static or dynamic objects. To this end, the system consists of three main modules: mapping, localization, and planning. The mapping module builds a global map for an unknown environment by means of a simultaneous localization and mapping algorithm based on laser scanner data. The localization module estimates the mobile robot’s pose within the prebuilt map by way of an adaptive Monte Carlo localization approach. The planning module builds a local cost map for collision avoidance, generates collision-free trajectories to reach a goal pose based on the cost map, and produces control commands to follow the trajectories. The presented system has been tested not only in simulation environments built in the Gazebo simulator but also in real environments utilizing the Jackal mobile robot, to validate its performance for autonomous navigation including collision avoidance.

In the field of autonomous robotics, enhancing the navigation system of robots is a crucial aspect that directly impacts their performance. This study presents a novel approach to addressing this challenge with an Artificial Neural Network (ANN) model. The research focuses on improving the navigation capabilities of a differential drive robot using the line-following method for route tracking and the dead reckoning technique for localization. It investigates a differential drive robot model controlled with a PID controller and derives the transfer function of the PID model. Through simulations, it becomes apparent that the PID model exhibits a continuous overshoot in its response, which negatively affects the behaviour of the robot's wheels. Ordinarily, continuous manual tuning will be required to correctly tune the PID controller to a value where the overshoots will be negligible, and this could be onerous. To overcome this limitation, an ANN controller is proposed, leveraging the learning capabilities of the neural network. Data from the PID controller transfer function is utilized to train the ANN model, enabling it to understand patterns and relationships. The ANN controller is then substituted in place of the PID controller in the simulation. The results showcase a remarkable 13.1% improvement in the robot's wheel response, highlighting the transformative potential of this approach for revolutionizing autonomous robot navigation in industrial applications. By using the transfer function of the PID model to train an ANN model, this study offers a powerful framework for enhancing the navigation performance of a differential drive autonomous robot and shows performance improvements in control, flexibility, and adaptation to changing conditions. These discoveries have significant ramifications for the industry and will pave the way for intelligent and effective autonomous robot navigation systems. The research provides a comprehensive understanding of the challenges associated with the differential drive robot model controlled with a PID controller and offers a robust approach to how this can be alleviated. The significance of this study lies in its ability to address the continuous overshoot issue observed in the PID controller's response by training an ANN controller with data from the PID controller. The proposed approach minimizes overshoot and improves the robot's wheel response, ultimately enhancing its navigation capabilities. Overall, this study demonstrates the potential of an ANN model to revolutionize autonomous robot navigation in industrial applications. The notable improvement achieved in the robot's wheel response validates the effectiveness of this approach. Future research can further optimize this integrated approach in real-world scenarios, leading to intelligent and efficient autonomous robot navigation systems across diverse industrial settings.

Membrane computing models are parallel and distributed natural computing models. These models are often referred to as P systems. This paper proposes a novel multi-behaviors co-ordination controller model using enzymatic numerical P systems for autonomous mobile robots navigation in unknown environments. An environment classifier is constructed to identify different environment patterns in the maze-like environment and the multi-behavior co-ordination controller is constructed to coordinate the behaviors of the robots in different environments. Eleven sensory prototypes of local environments are presented to design the environment classifier, which needs to memorize only rough information, for solving the problems of poor obstacle clearance and sensor noise. A switching control strategy and multi-behaviors coordinator are developed without detailed environmental knowledge and heavy computation burden, for avoiding the local minimum traps or oscillation problems and adapt to the unknown environments. Also, a serial behaviors control law is constructed on the basis of Lyapunov stability theory aiming at the specialized environment, for realizing stable navigation and avoiding actuator saturation. Moreover, both environment classifier and multi-behavior coordination controller are amenable to the addition of new environment models or new behaviors due to the modularity of the hierarchical architecture of P systems. The simulation of wheeled mobile robots shows the effectiveness of this approach.

In this paper, we are interested on the development of hybrid control architecture for autonomous mobile robots navigation. The proposed approach consists of an architecture adapted for partially known environments. It includes both reactive navigation methods based on the principle of Sense & Act and deliberative methods based on the principle of Sense-Plan & Act. The used reactive navigation method is a behavioural approach for navigation in unknown environments. Whereas deliberative approach is based on a polynomial method called Random-Profile-Approach (RPA) for optimal trajectory planning in known environments. Controllers used for both trajectories tracking and reactive navigation are fuzzy inference systems. Simulation and experimental results to validate the proposed navigation strategy are presented.

The main problems with autonomous navigation system that uses GPS is the satellite outages and the mapping of the ambience in unknown environment. Techniques was presented to increase position accuracy and navigation robustness in unknown environment by means of collaborative navigation. When vehicle one is navigating, lines of sight to satellites will often be blocked, however, vehicle two at different locations can have different sets of visible satellites and act as virtual satellite. Through collaboration, the satellite information can be shared between vehicles; then, better navigation performance will be achieved. At the same time, vehicle one can map its environment and locate itself accurately even when GPS signal is artificially broken. Simulation results indicate increased accuracy when using the method under investigation. Collaborative autonomous navigation can help several relatively simple vehicles achieve the same performance as expensive and complex platforms.

Mobile robot navigation with the combination of supervised learning in cerebellum and reward-based learning in basal ganglia