Obstacle Avoidance In Dynamic Environment Research Articles

A Multiple Environment Available Path Planning Based on an Improved A* Algorithm

The objective of the path planning for a mobile robot is to generate a collision-free path from a starting position to a target position, aiming to realize a higher quality of path planning, an improved A* algorithm and a hybrid approach incorporating the dynamic window algorithm have been proposed for robot path planning in various environments in this paper. In global path planning, first, a bidirectional search strategy was introduced into to improve the searching efficiency, and an adaptive heuristic function was designed to reduce redundant search nodes. In the meantime, a filtering function for key path nodes and an enhanced jump point optimization method help to remove redundant nodes in the path, reduce turning angles, greatly shorten the path length, and smooth the path using cubic B-spline curves. Furthermore, in local path planning, the combination of key path nodes and the dynamic window approach (DWA) algorithm is utilized to achieve obstacle avoidance in dynamic environments and adjust the heading angle of the section enables seamless locomotion of the robot. Finally, the simulation experiments and physical experiments on the robot were conducted to validate that the proposed improved algorithm significantly improves the speed of path planning while also reducing the length of the planned path and improve the reliability of the algorithm when compared with other algorithms.

Risk-Aware Trajectory Sampling for Quadrotor Obstacle Avoidance in Dynamic Environments

Obstacle avoidance of quadrotors in dynamic environments is still a very open problem. Current works commonly leverage traditional static maps to represent static obstacles, and the detection and tracking of moving objects (DATMO) method to model dynamic obstacles separately. The detection module requires pre-training, and the dynamic obstacles can only be modeled with certain shapes, such as cylinders or ellipsoids. This work utilizes the dual-structure particle-based (DSP) dynamic occupancy map to represent the arbitrarily-shaped static obstacles and dynamic obstacles simultaneously, and proposes an efficient risk-aware sampling-based local trajectory planner to realize safe flights. The trajectory is planned by sampling motion primitives generated in the state space. Each motion primitive is divided into two phases: a short-term phase with a strict risk limitation and a relatively long-term phase designed to avoid high-risk regions. The risk is evaluated with the predicted particle-form future occupancy status, considering the time dimension. With an approach of splitting from and merging to an arbitrary global trajectory, the planner can also be used in tasks with preplanned global trajectories. Comparison experiments show that the obstacle avoidance system composed of the DSP map and our planner performs the best in dynamic environments. In real-world tests, our quadrotor reaches a speed of 6 m/s with the motion capture system and 2.5 m/s with everything running on a low-price single-board computer.

Robust Dynamic Modeling and Trajectory Tracking Controller of a Universal Omni-Wheeled Mobile Robot

Abstract Omnidirectional mobile robots are widely used in studies and services as they are effective and efficient in moving in any direction regardless of their current orientation. These significant properties are very useful in energy-efficient navigation and obstacle avoidance in dynamic environments. The literature on modeling and control of omni-wheel robots usually relies on the kinematic model or simplified kinematic model. Then developing control laws based on these reduced-effect models. In this article, we developed an efficient full dynamic model of a nonholonomic omni-wheel robot, including roller dynamics. That allows for a proportional–integral–derivative control law to accurately follow arbitrary paths. Kane’s approach was used for the dynamic model derivation. Kinematic modeling is less complex than multibody dynamic modeling. But to have an accurate simulation of the realistic motions of a mechanical system, the multibody dynamic model is required.

Perception-Aware Planning for Active SLAM in Dynamic Environments

This paper presents a perception-aware path planner for active SLAM in dynamic environments using micro-aerial vehicles (MAV). The “Next-Best-View” planner (NBVP planner) is combined with an active loop closing, which is called the Active Loop Closing Planner (ALCP planner). The planner is proposed to avoid both static and dynamic obstacles in unknown environments while reducing the uncertainty of the SLAM system and further improving the accuracy of localization. First, the receding horizon strategy is adopted to find the next waypoint. The cost function that combines the exploration gain and the loop closing gain is designed. The former can reduce the mapping uncertainty, while the latter takes the loop closing possibility into consideration. Second, a key waypoint selection strategy is designed. The selected key waypoints, instead of all waypoints, are treated as potential loop-closing points to make the algorithm more efficient. Moreover, a fuzzy RRT-based dynamic obstacle avoidance algorithm is adopted to realize obstacle avoidance in dynamic environments. Simulations in different challenging scenarios are conducted to verify the effectiveness of the proposed algorithm.

Double Deep Q-Learning and Faster R-CNN-Based Autonomous Vehicle Navigation and Obstacle Avoidance in Dynamic Environment.

Autonomous vehicle navigation in an unknown dynamic environment is crucial for both supervised- and Reinforcement Learning-based autonomous maneuvering. The cooperative fusion of these two learning approaches has the potential to be an effective mechanism to tackle indefinite environmental dynamics. Most of the state-of-the-art autonomous vehicle navigation systems are trained on a specific mapped model with familiar environmental dynamics. However, this research focuses on the cooperative fusion of supervised and Reinforcement Learning technologies for autonomous navigation of land vehicles in a dynamic and unknown environment. The Faster R-CNN, a supervised learning approach, identifies the ambient environmental obstacles for untroubled maneuver of the autonomous vehicle. Whereas, the training policies of Double Deep Q-Learning, a Reinforcement Learning approach, enable the autonomous agent to learn effective navigation decisions form the dynamic environment. The proposed model is primarily tested in a gaming environment similar to the real-world. It exhibits the overall efficiency and effectiveness in the maneuver of autonomous land vehicles.

Obstacle avoidance in dynamic environments based on velocity space optimization

Robotic obstacle avoidance is an important issue in robotic navigation for unknown or partially known, dynamic environments. A good number of techniques have already been proposed to navigate obstacles in this kind of environment. They include a series of velocity space methods that have been successful implemented in several applications. They formulate the problem as one of constrained optimization in the velocity space of the robot. The constraints include the obstacles in the environment assuming they are static. In this paper, we present an efficient, real-time method (BCM-DO) to include the restrictions imposed by dynamic objects. The optimization function has also been adapted to include these new restrictions. The new function is evaluated in two sets of points. A first set is obtained from a coarse sampling in the reachable window of velocities and a second set is selected in the limits of each curvature interval to avoid missing small openings between static objects. The whole system has first been extensively tested in several simulated robots and finally applied to a hotel assistant robot (BellBot) resulting in an efficient, real-time obstacle avoidance method that produces smooth and reliable routes.

The autonomous navigation and obstacle avoidance for USVs with ANOA deep reinforcement learning method

The unmanned surface vehicle (USV) has been widely used to accomplish missions in the sea or dangerous marine areas for ships with sailors, which greatly expands protective capability and detection range. When USVs perform various missions in sophisticated marine environment, autonomous navigation and obstacle avoidance will be necessary and essential. However, there are few effective navigation methods with real-time path planning and obstacle avoidance in dynamic environment. With tailored design of state and action spaces and a dueling deep Q-network, a deep reinforcement learning method ANOA (Autonomous Navigation and Obstacle Avoidance) is proposed for the autonomous navigation and obstacle avoidance of USVs. Experimental results demonstrate that ANOA outperforms deep Q-network (DQN) and Deep Sarsa in the efficiency of exploration and the speed of convergence not only in static environment but also in dynamic environment. Furthermore, the ANOA is integrated with the real control model of a USV moving in surge, sway and yaw and it achieves a higher success rate than Recast navigation method in dynamic environment.

Hybrid Spiral STC-Hedge Algebras Model in Knowledge Reasonings for Robot Coverage Path Planning and Its Applications

Robotics is a highly developed field in industry, and there is a large research effort in terms of humanoid robotics, including the development of multi-functional empathetic robots as human companions. An important function of a robot is to find an optimal coverage path planning, with obstacle avoidance in dynamic environments for cleaning and monitoring robotics. This paper proposes a novel approach to enable robotic path planning. The proposed approach combines robot reasoning with knowledge reasoning techniques, hedge algebra, and the Spiral Spanning Tree Coverage (STC) algorithm, for a cleaning and monitoring robot with optimal decisions. This approach is used to apply knowledge inference and hedge algebra with the Spiral STC algorithm to enable autonomous robot control in the optimal coverage path planning, with minimum obstacle avoidance. The results of experiments show that the proposed approach in the optimal robot path planning avoids tangible and intangible obstacles for the monitoring and cleaning robot. Experimental results are compared with current methods under the same conditions. The proposed model using knowledge reasoning techniques in the optimal coverage path performs better than the conventional algorithms in terms of high robot coverage and low repetition rates. Experiments are done with real robots for cleaning in dynamic environments.

A new method using knowledge reasoning techniques for improving robot performance in coverage path planning

Robots are a rapidly evolving development field encompassing variable domains ranging from industrial robots to empathetic robots for human companions. Future robots will be highly dependent on the ability to understand, interpret, and generate a representation of the environment in which they are operating, ideally in both a human and machine-readable formalism. An important element in this process lies in Path Planning (PP) with obstacle avoidance in dynamic environments (including cleaning and monitoring in robotics) to identify optimal coverage paths. The study in this paper presents a new approach which combines knowledge reasoning techniques with Breadth First Search to find the optimal path for a cleaning robot in a dynamic environment. This approach is used to apply knowledge inference with conventional coverage PP algorithms to enable robot control and avoid obstacles with optimal coverage PP. The experimental results show that using the proposed approach a robot avoids fixed and mobile obstacles, optimal PP reducing both computational cost and time. When compared to other current approaches, the proposed approach with high-coverage rate and low-repetition rate in coverage performs better than the conventional robot algorithms.

QRDPSO: A New Optimization Method for Swarm Robot Searching and Obstacle Avoidance in Dynamic Environments

QRDPSO: A New Optimization Method for Swarm Robot Searching and Obstacle Avoidance in Dynamic Environments

QRDPSO: A New Optimization Method for Swarm Robot Searching and Obstacle Avoidance in Dynamic Environments

In this paper we show how the quantum-based particle swarm optimization (QPSO) method is adopted to derive a new derivation for robotics application in search and rescue simulations. The new derivation, called the Quantum... | Find, read and cite all the research you need on Tech Science Press

An Improved VFF Approach for Robot Path Planning in Unknown and Dynamic Environments

Robot path planning in unknown and dynamic environments is one of the hot topics in the field of robot control. The virtual force field (VFF) is an efficient path planning method for robot. However, there are some shortcomings of the traditional VFF based methods, such as the local minimum problem and the higher computational complexity, in dealing with the dynamic obstacle avoidance. In this paper, an improved VFF approach is proposed for the real-time robot path planning, where the environment is unknown and changing. An area ratio parameter is introduced into the proposed VFF based approach, where the size of the robot and obstacles are considered. Furthermore, a fuzzy control module is added, to deal with the problem of obstacle avoidance in dynamic environments, by adjusting the rotation angle of the robot. Finally, some simulation experiments are carried out to validate and demonstrate the efficiency of the proposed approach.

Hybrid ant colony and immune network algorithm based on improved APF for optimal motion planning

SUMMARYInspired by the mechanisms of idiotypic network hypothesis and ant finding food, a hybrid ant colony and immune network algorithm (AC-INA) for motion planning is presented. Taking the environment surrounding the robot and robot action as antigen and antibody respectively, an artificial immune network is constructed through the stimulation and suppression between the antigen and antibody, and the antibody network is searched using improved ant colony algorithm (ACA) with pseudo- random-proportional rule and super excellent ant colony optimization strategy. To further accelerate the convergence speed of AC-INA and realize the optimal dynamic obstacle avoidance, an improved adaptive artificial potential field (AAPF) method is provided by constructing new repulsive potential field on the basis of the relative position and velocity between the robot and obstacle. Taking the planning results of AAPF method as the prior knowledge, the initial instruction definition of new antibody is initialized through vaccine extraction and inoculation. During the motion planning, once the robot meets with moving obstacles, the AAPF method is used for the optimal dynamic obstacle avoidance. The simulation results indicate that the proposed algorithm is characterized by good convergence property, strong planning ability, self-organizing, self-learning, and optimal obstacle avoidance in dynamic environments. The experiment in known indoor environment verifies the validity of AAPF-based AC-INA, too.

Fuzzy Navigation for Robotic Manipulators

This paper describes a novel navigation and obstacle avoidance system for robotic manipulators. The system is divided into separate fuzzy units which individually control the links of a manipulator. The rule base of each unit combines the repelling influence of obstacles with the attracting influence of the target position in a fuzzy way to generate actuating commands for the link. Owing to its simplicity and hence its short response time, the fuzzy navigator is especially suitable in on-line applications with strong real-time requirements. Furthermore, this approach allows obstacle avoidance in dynamic environments. The functioning of the fuzzy navigator with respect to robotic manipulators and results of real-world experiments are presented.