Obstacle Avoidance In Unknown Environment Research Articles

Optimization of multi-vehicle obstacle avoidance based on improved artificial potential field method with PID control

In the context of multi-vehicle formation, obstacle avoidance in unknown environments presents a number of challenges, including obstacles near the target, susceptibility to local minima, and dynamic obstacle avoidance. To address these issues in multi-vehicle formation control and obstacle avoidance within unknown environments, this paper uses PID control to optimize the potential field function of the artificial potential field method and conducts simulation experiments. The results demonstrate that the proposed algorithm achieves reductions of 39.7%, 41.9%, 24.8% and 32.0% in four efficiency functions (total iteration times, formation efficiency function value, energy consumption and standard deviation of iteration times) compared to other algorithms. The improved algorithm more effectively addresses the challenge of slow obstacle avoidance when vehicles approach the target and can handle unexpected situations such as local minima and dynamic obstacles. It achieves energy-efficient optimization for multi-vehicle obstacle avoidance in complex environments.

On obstacle avoidance path planning in unknown 3D environments: A fluid-based framework

Compared with preprocessed obstacle environments, unknown environments are more challenging for path planning. In unknown environments, an agent can make decisions only by relying on the obstacle information detected by its onboard sensors. However, when facing non-convex obstacles, this limited detection information can easily trap the agent in a local optimum. In this paper, a nature-inspired methodology called Interfered Fluid Dynamic System (IFDS) is extended to anti-local-optimum obstacle avoidance in unknown 3D environments for the first time and a novel fluid-based path planning framework is proposed. First, the detection region of the agent is discretized. Then, spherical virtual obstacles (SVOs) located at detected discrete points are generated and memorized. Thus, obstacle avoidance in unknown environments is transformed into the avoidance of known SVOs. Next, the currently generated and memorized SVOs are input to the core of the framework, the IFDS algorithm, to produce repulsive effects, and the corresponding 3D avoidance path is resolved. On this basis, to address local optimum in cases with non-convex obstacles, and considering compatibility with the IFDS, the direction coefficient and sink-heading angular rate adjustment strategies, which belong to the same system as the IFDS, are introduced to modify the IFDS in this framework. Finally, receding horizon control is introduced to improve the obstacle avoidance performance. Simulations show that the proposed framework can enable the agent to autonomously jump out of the 3D non-convex obstacle environments with typical features of the local optimum, including wall-like and cave-like obstacles, and safely reach the destination.

Robot navigation with obstacle avoidance in unknown environment

In this paper, a robot navigation model is constructed in MATLAB-Simulink. This robot navigation model make the robot capable for the obstacles avoidance in unknown environment. The navigation model uses two types of controllers: pure pursuit controller and fuzzy logic controller. The role of the pure pursuit controller is to generate linear and angular velocities to drive the robot from its current position to the given goal position. The obstacle avoidance is achieved through the fuzzy logic controller. For the fuzzy controller, two novel fuzzy inference systems (FISs) are developed. Initially, a Mamdani-type fuzzy inference system (FIS) is generated. Using this Mamdani-type FIS in the fuzzy controller, the training data of input and output mapping, is collected. This training data is supplied to the adaptive neuro-fuzzy inference system (ANFIS) to obtain the second FIS as of Sugeno-type. The navigation model, using the proposed FISs, is implemented on the simulated as well as real robots.

Random neural Q-learning for obstacle avoidance of a mobile robot in unknown environments

The article presents a random neural Q-learning strategy for the obstacle avoidance problem of an autonomous mobile robot in unknown environments. In the proposed strategy, two independent modules, namely, avoidance without considering the target and goal-seeking without considering obstacles, are first trained using the proposed random neural Q-learning algorithm to obtain their best control policies. Then, the two trained modules are combined based on a switching function to realize the obstacle avoidance in unknown environments. For the proposed random neural Q-learning algorithm, a single-hidden layer feedforward network is used to approximate the Q-function to estimate the Q-value. The parameters of the single-hidden layer feedforward network are modified using the recently proposed neural algorithm named the online sequential version of extreme learning machine, where the parameters of the hidden nodes are assigned randomly and the sample data can come one by one. However, different from the original online sequential version of extreme learning machine algorithm, the initial output weights are estimated subjected to quadratic inequality constraint to improve the convergence speed. Finally, the simulation results demonstrate that the proposed random neural Q-learning strategy can successfully solve the obstacle avoidance problem. Also, the higher learning efficiency and better generalization ability are achieved by the proposed random neural Q-learning algorithm compared with the Q-learning based on the back-propagation method.

Real-time Autonomous UAV Formation Flight with Collision and Obstacle Avoidance in Unknown Environment

In this paper autonomous formation control for Unmanned Aerial Vehicles (UAVs) has been discussed and a real time solution has been put forward by benefiting General Purpose Graphical Processing Units (GPGPU) accelerated potential field approach while ensuring obstacle and collision avoidance in unknown environment by using real-time sensors. GPGPU accelerated real time formation control for UAVs was designed and the basic model of the approach has been explained in our previous work (Cetin and Yilmaz 2014). As the deficiencies of the previous approach, autonomous real time collision and coordinated obstacle avoidance features in unknown environments are also handled while maintaining formation flight conditions in this work. With these features, improved autonomous formation control approach is discussed as a real time solution. The computation is performed by using Graphical Processing Units (GPUs) as parallel computation architectures by benefiting from Single Instruction Multiple Data (SIMD) type parallel algorithms. Classic binary map conversation, connected component labeling and minimum bounding box algorithms which are commonly used for image processing applications, has been evaluated for real time obstacle detection and avoidance features by developing GPGPU suitable parallel algorithms. Real-time solution has been developed by integrated these parallel algorithms with parallel Artificial Potential Field (APF) computation algorithm. Simulation results are proved that this novel autonomous improved formation control approach is successful and it would be used in real time applications like UAV formation flight missions.

Fuzzy Control of an AGV Obstacle Avoidance System in Unknown Environments

An avoiding obstacle fuzzy controller for AGV is presented in this paper. Multi-sensors are used to detect obstacles information. The fuzzy controller processes the distance of obstacles by using a 3×3 rule table. As to convert the fuzzy output into non-fuzzy value, Centre of Gravity Method (COG) is used to calculate an accurate control output. This fuzzy control algorithm can solve the problem of obstacle avoidance in unknown environment. And the AGV running path is smooth and the changing of speed is moderate.

Decentralized Motion Coordination for a Formation of Rovers

In this paper, a decentralized formation control is proposed which enables collision free coordination and navigation of agents. We present a simple method to define the formation of multi-agents and individual identities (IDs) of agents. Two decentralized coordination and navigation techniques are proposed for the formation of rovers. Agents decide their own behaviors onboard depending upon the motion initiative of the master agent of the formation. In these approaches, any agent can estimate behavior of other agents in the formation. These will reduce the dependency of individual agent on other agents while taking decisions. These approaches reduce the communication burden on the formation where only the master agent broadcasts its motion status per sampled time. Any front agent can act as a master agent without affecting the formation in case of fault in initial master agent. The main idea of this paper is to develop an adequate computational model under which agents in the formation will perform to coordinate among each other. Assignments of IDs to agents are very useful in real-time applications. These proposed schemes are suitable for obstacle avoidance in unknown environment as a whole formation. Agents are free from collision among each other during navigation. These schemes can be used for velocity as well as orientation alignment problems for a multi-agent rover network. These schemes are tested with extensive simulations and responses of agents show satisfactory performances to deal with different environmental conditions.

Modeling and Analysis for Obstacle Avoidance of a Behavior-Based Robot with Objected Oriented Methods

Object Modeling Technique is widely applied in the field of software engineering; and in this paper we applied this technique to model a mobile robot including its behaviors and interactions with environment. The paper first describes key background knowledge about object oriented analysis in software engineering, behavior based robotics and their similarities. Then, based on these similarities, the paper uses object oriented methods of software engineering, such as unified modeling language (UML), to analyze and model the architecture; and to design behaviors for a behavior-based robot, which is expected to wander with autonomous obstacle avoidance in unknown environment. Object oriented methods permit a translation from conceptual behavior models to computer programming representations, and separate concrete control algorithms from robot modeling. With this approach, the paper implements a fuzzy algorithm for obstacle avoidance behavior of the constructed behavior models in a physical robot, and made experiments in the given indoor environment.

A Decentralized Control Algorithm for Swarm Behavior and Obstacle Avoidance in Unknown Environments

In this paper a decentralized control algorithm for swarm behavior and obstacle avoidance in unknown environment is presented. The proposed technique is based on the concept of Null Space Based behavioral approach to merge different swarm tasks. In particular, the algorithm focuses on the possibility of sharing sensors’ data in order to avoid obstacles that can be detected in the environment. The effectiveness of our approach is investigated by means of numerical simulations with different typology of environment.

GA BASED SENSOR PLACEMENT AND CONTROL DESIGN FOR REACTIVE NAVIGATION OF MOBILE ROBOTS

In this paper, a novel genetic algorithm based approach is proposed for optimal sensor placement and controller design of a mobile robot to facilitate its reactive navigation and obstacle avoidance in unknown environments. The mobile robots considered in this paper have flexible sensor and control structure. A genetic algorithm is developed to evolve the parameters of optimal sensor placement and controller design simultaneously. The effectiveness of the proposed GA based co-evolution approach to robot sensor placement and control design is demonstrated by simulation studies.

異なるタスクを持つ複数ロボットによる単一物体の協調搬送

A cooperative control method for multiple robots of single object transportation is proposed, which is applicable to unknown environment. In this method, all robots are controlled by the same algorithm, and they can have different objective tasks simultaneously. A task is expressed as the desired position of the object and the associated weight matrix, and each robot can set its own desired position and weight matrix independently of the other robots' tasks. By the proposed method, the robots can cooperatively transport the object to the composite desired position which is the weighted mid point of the desired positions set by all robots, without communicating their tasks with one another. The forces between the robots and the object are controlled as equally as possible during transportation. Furthermore, the internal forces can be suppressed only if some robots in danger of applying large forces broadcast on/off signal to the other robots. Accordingly, this method requires little amount of real-time communication among the robots and does not depend on the number of robots. Owing to these features, the method can deal with the situation where one robot has “path tracking task” and another robot which detects an obstacle has “obstacle avoidance task”; that allows cooperative object transportation with obstacle avoidance in unknown environment. The effectiveness and usefulness of the method are ascertained by computer simulation and experiment.