Paths In Dynamic Environments Research Articles

Finding Robotic Wide Free Space Path in Dynamic Environment by Improving MAX–MIN Ant System Algorithm

— This paper proposes a new obstacle avoidance method for finding an optimal path passing through wide free space based on the optimized MAX-MIN Ant System (MMAS) algorithm in dynamic environments. The proposed improvement of the MMAS algorithm occurs in two stages. The first stage analyses the environment with two modifications by proposing a new relation of pheromone trail updating to construct the consequence modified (deposited) pheromone trail update in each iteration based on adding a new parameter (clean) to calculate the possible wide empty locations. Another alteration at this level involves checking and identifying the four horizontal and vertical states. Suppose the two nodes on either side of the four diagonal nodes are barriers in the next neighbor locations. In that case, the Tight Tunnel state is then attained, and it is managed by sealing this location to prevent passing through it before the robot movement stage. Another alteration is made to the robot-moving stage by recommending a new relation of tour building probabilities to determine the best option for moving the robot from the start node through a dynamic environment that incorporates dynamic obstacles moving through free space by locating and exhibiting the ideal path via broad free space. The main outcome of the experiment simulation of the new method involves that the robot avoids going through narrow tunnels and chooses the best course through open areas to reach its destination without running into any impediments in various dynamic environments conditions. In addition, the comparison of average time occupied to find the paths by the new method reduces to 54.32% as compared with that required with the method of hybrid the basic D* algorithm with the Lbest PSO algorithm, and to 11.95% in comparison to the time occupied recorded when applying the method of the improved MAX–MIN ACO algorithm.

A Random Sampling-Based Method via Gaussian Process for Motion Planning in Dynamic Environments

Motion planning is widely applied to industrial robots, medical robots, bionic robots, and smart vehicles. Most work environments of robots are not static, which leads to difficulties for robot motion planning. We present a dynamic Gaussian local planner (DGLP) method to solve motion planning problems in dynamic environments. In a dynamic environment, dynamic obstacles sometimes make part of the global path invalid, so the local invalid path needs to be local re-planned online. Compared with the node sampling-based methods building large-scale random trees or roadmaps, the Gaussian random path sampling (GRPS) module integrated in the DGLP directly samples smooth random paths discretized into sparse nodes to improve the local path re-planning efficiency. We also provide the path end orientation constraint (PEOC) method for the local re-planning paths in order to merge them smoothly into the global paths. In the robot experiments, the average planning time of the DGLP is 0.04s, which is at least 92.31% faster than the test methods, and its comprehensive evaluation scores, which consider the consuming time, path quality, and success rate of local re-planning, are at least 44.92% higher than the test methods. The results demonstrate that the proposed DGLP method is able to efficiently provide high-quality local re-planning paths in dynamic environments.

A Two-Layer Heterogeneous Ant Colony System With Applications in Part-Picking Planning

Abstract The tape carrier packaging machine with multiple sucking discs is designed for modern, high-speed packaging production line, which is an important part of the electronic manufacturing process. The efficiency of the part-picking can directly affect the efficiency of the packaging production line. The problem involves at least three coupled sub-problems: the number of picking rounds, the matching relationship between the parts and sucking discs, and the picking order. Because the actual industrial production requires real-time or soft real-time scheduling, the challenge is how to efficiently solve this multi-dimensional combinatorial optimization problem within a fairly limited time. The existing advanced algorithms such as the genetic algorithm and the simulated annealing algorithm usually require complex and time-consuming encoding and decoding while solving this problem. In contrast, the ant colony algorithm has advantages in the convergence rate and parallel computing, especially for searching high dimensional paths in dynamic environments. In order to further improve the comprehensive performance of the ant colony algorithm in solving multi-dimensional coupled optimization problems in extremely limited time, we propose a novel two-layer heterogeneous ant colony system based on three strategies: (1) a bottom-up two-layer solution framework to decouple the original tightly coupled problem into a loosely coupled problem composed of two sub-problems; (2) a candidate cluster augmentation strategy based on the Delaunay triangulation to improve the diversity and quality of the clusters; (3) a co-evolution between a pair of heterogeneous colonies by mixing the high-quality pheromone properly as well as a co-evolution based on the prior knowledge. Finally, through a large set of comparisons, the average picking efficiency of Two-layer Heterogeneous Ant Colony System improved by the three proposed strategies is 13–16% higher than that of other popular heuristic algorithms.

Collision Avoidance and Path Following Control of Unmanned Aerial Vehicle in Hazardous Environment

Recent years have seen a rapidly increasing number of applications using unmanned aerial vehicles (UAVs). In order to greatly enhance the capabilities of UAVs working along with other manned aerial vehicles in more cluttered, hazardous, and sophisticated environments without violating the aviation traffic regulations, this paper proposes a new hybrid collision avoidance method along with a modified path following approach. The proposed hybrid collision avoidance scheme consists of a global path planner and a local collision avoidance mechanism for the purpose of greatly reducing computational efforts raised by global method, while ensuring the safe and satisfactory performance of collision avoidance. The global path planner is designed using rapidly exploring random tree (RRT), and the dynamics of UAV are also taken into consideration to generate a feasible and optimal path. The light-computational local collision avoidance mechanism, which can partially modify the globally planned path in dynamic environment when any hazardous obstacles blocking the desired path, is developed based on an intelligent fuzzy logic approach by integrating a set of decision making strategies and several aviation traffic regulations. Regarding the presented path following methodology, an improvement to the previous cross-track error calculation mechanism is made by employing the extended Kalman filter (EKF) to estimate the cross-track error. Finally, design of height and attitude control system of UAV is also addressed. Extensive simulation and experimental studies on a series of scenarios with both static and dynamic objects are conducted to demonstrate the effectiveness of the proposed hybrid collision avoidance approach and cross-track error prediction based path following method.

Development of Novel Motion Planning and Controls for a Series of Physically Connected Robots in Dynamic Environments

The motion planning and control of physically connected robots, e.g., docked mobile robots (DMR), is a challenging problem due to robots’ underactuated and nonlinear dynamics as well as their physical constraints. The majority of the motion planning approaches developed for DMR are not robust to robots’ mass difference and are only applicable to static environments. This paper proposes a novel motion planning methodology for a DMR, consisting of a nonholonomic circular-shape leader and N holonomic passive-wheels active-joints circular-shape followers, through developing: (i) a trajectory planner to determine the motion of docked followers for tracking a desired path, (ii) a robust motion controller to navigate DMR through the planned trajectory, and (iii) a collision avoidance strategy to provide a collision-free transit for followers in dynamic environments. We propose two novel approaches for collision avoidance: a reactive approach which is a decentralized method that utilizes robots’ on-board measurement sensors, and a cooperative approach which is a centralized approach that uses environment information (e.g. obstacle locations) to prevent imminent collisions. In the reactive approach, the collision avoidance strategy is comprised of two control laws, one for obstacle avoidance and the other for satisfying joint constraints. In the cooperative approach, the collision avoidance strategy replans a collision-free trajectory for docked followers, and then deploys our trajectory planner and motion controller to navigate the DMR through the trajectory. The performance of the proposed reactive and cooperative approaches were shown and compared through simulations as well as implementation in a virtual robot experimentation platform (V-REP). The results showed that while the reactive approach is more efficient in terms of computation time and energy consumption, the cooperative approach requires less lateral deviation for avoiding the obstacles which is beneficial for operation in confined spaces. We finally compared our motion planning methodology with other existing methods in the literature. This comparison proved that our method is applicable to complex paths in dynamic environments, scalable with the number of followers, robust to various robots’ masses, and more computationally efficient.

Haptic Guidance in Dynamic Environments Using Optimal Reciprocal Collision Avoidance

Human guidance in situations where the users cannot rely on their main sensory modalities, such as assistive or search-and-rescue scenarios, is a challenging task. In this letter, we address the problem of guiding users along collision-free paths in dynamic environments, assuming that they cannot rely on their main sensory modalities. In order to safely guide the subjects, we adapt the optimal reciprocal collision avoidance to our specific problem. The proposed algorithm takes into account the stimuli which can be displayed to the users and the motion uncertainty of the users when reacting to the provided stimuli. The proposed algorithm was evaluated in three different dynamic scenarios. A total of 18 blindfolded human subjects were asked to follow haptic cues in order to reach a target area while avoiding real static obstacles and moving users. Three metrics such as time to reach the goal, length of the trajectories, and minimal distance from the obstacles are considered to compare results obtained using this approach and experiments performed without visual impairments. Experimental results reveal that blindfolded subjects are successfully able to avoid collisions and safely reach the targets in all the performed trials. Although in this letter we display directional cues via haptic stimuli, we believe that the proposed approach can be general and tuned to work with different haptic interfaces and/or feedback modalities.

Optimizing robot path in dynamic environments using Genetic Algorithm and Bezier Curve

Optimizing robot path in dynamic environments using Genetic Algorithm and Bezier Curve

Generating optimal paths in dynamic environments using River Formation Dynamics algorithm

The paper presents a comparison of four optimisation algorithms implemented for the purpose of finding the shortest path in static and dynamic environments with obstacles. Two classical graph algorithms – the Dijkstra complete algorithm and A* heuristic algorithm – were compared with metaheuristic River Formation Dynamics swarm algorithm and its newly introduced modified version. Moreover, another swarm algorithm has been compared – the Ant Colony Optimization and its modification. Terms and conditions of the simulation are thoroughly explained, paying special attention to the new, modified River Formation Dynamics algorithm. The algorithms were used for the purpose of generating the shortest path in three different types of environments, each served as a static environment and as a dynamic environment with changing goal or changing obstacles. The results show that the proposed modified River Formation Dynamics algorithm is efficient in finding the shortest path, especially when compared to its original version. In cases where the path should be adjusted to changes in the environment, calculations carried out by the proposed algorithm are faster than the A*, Dijkstra, and Ant Colony Optimization algorithms. This advantage is even more evident the more complex and extensive the environment is.

Application of an Evolutionary Optimization Technique to Routing in Mobile Wireless Networks

main goal of this work is to consider the problem of path optimization between the sender and receiver in a dynamic mobile network. A new adaptive algorithm based on Evolutionary technique called Genetic Algorithm (AGA) has been proposed to find out the Optimal Path in dynamic nature problem. In this paper, the proposed AGA for solving the shortest path routing problem is presented to find the shortest path in a mobile wireless networks to satisfy the minimum end-to-end delay quality of service (QoS). The proposed algorithm provides the solution of optimal path using a technique that is inspired by the natural process that is initial population, selection crossover and mutation. The proposed AGA uses a multiple selection methods to improve the performance of particular implementations. Simulations have been done using both MATLAB and Visual basic environments and the results show that the proposed algorithm performs excellently by finding a path with minimum end-to-end delay between source and destination; it finds the shortest path in dynamic environment efficiently and quickly adapts to the environmental changes (i.e., the network topology change) and produces good solutions after each change.

Intelligent Mobile Robot Navigation in an Uncertain Dynamic Environment

This paper presents a modified sensor-based online method for mobile robot navigation generating paths in dynamic environments. The core of the navigation algorithm is based on the velocity obstacle avoidance method and the guidance-based tracking algorithm. A fuzzy decision maker is designed to combine the two mentioned algorithms intelligently. Hence the robot will be able to decide intelligently in various situations when facing the moving obstacles and moving target. A noble noise cancellation algorithm using Neural Network is designed to navigate the robot in an uncertain dynamic environment safely. The results show that the robot can track a moving target while maneuvering safely in dynamic environment and avoids stationary and moving obstacles.

Robot Path Planning using Dynamic Programming with Accelerating Nodes

Abstract We solve the problem of robot path planning using Dynamic Programming (DP) designed to perform well in case of a sudden path blockage. A conventional DP algorithm works well for real time scenarios only when the update frequency is high i.e. changes can be readily propagated. In case updates are costly, for a sudden blockage the robot continues moving along the wrong path or stands stationary. We propose a modified DP that has nodes with additional processing (called accelerating nodes) to enable different segments of the map to become informed about the blockage rapidly. We further quickly compute an alternative path in case of a blockage. Experimental results verify that usage of accelerating nodes makes the robot follow optimal paths in dynamic environments.

Multimodal function optimization using minimal representation size clustering and its application to planning multipaths.

A novel genetic algorithm (GA) using minimal representation size cluster (MRSC) analysis is designed and implemented for solving multimodal function optimization problems. The problem of multimodal function optimization is framed within a hypothesize-and-test paradigm using minimal representation size (minimal complexity) for species formation and a GA. A multiple-population GA is developed to identify different species. The number of populations, thus the number of different species, is determined by the minimal representation size criterion. Therefore, the proposed algorithm reveals the unknown structure of the multimodal function when a priori knowledge about the function is unknown. The effectiveness of the algorithm is demonstrated on a number of multimodal test functions. The proposed scheme results in a highly parallel algorithm for finding multiple local minima. In this paper, a path-planning algorithm is also developed based on the MRSC_GA algorithm. The algorithm utilizes MRSC_GA for planning paths for mobile robots, piano-mover problems, and N-link manipulators. The MRSC_GA is used for generating multipaths to provide alternative solutions to the path-planning problem. The generation of alternative solutions is especially important for planning paths in dynamic environments. A novel iterative multiresolution path representation is used as a basis for the GA coding. The effectiveness of the algorithm is demonstrated on a number of two-dimensional path-planning problems.