Grid-based Environment Research Articles

Solving the multi-objective path planning problem for mobile robot using an improved NSGA-II algorithm

Path planning is one of the most concerned problems in mobile robotics. Given the NP-hard nature of path planning problems, the non-dominated sorting genetic algorithm (NSGA-II), as one of the outstanding evolutionary algorithms with robust optimization capabilities, is a good candidate to deal with them. In this study, an improved NSGA-II (INSGA-II) is proposed to solve the multi-objective path planning problems, in which path length, path safety and path smoothness are optimized simultaneously. First, a grid-based environment model is created, and each feasible solution is encoded as a discrete ordered point set. Second, a hybrid initialization strategy is employed to enrich the diversity of initial population. Third, besides the usual selection, crossover and mutation operators, problem-specific evolutionary operators are developed. In addition, an adaptive variable neighborhood local search strategy is designed to increase the exploitation ability of the algorithm. Moreover, a hybrid global search strategy is applied to further improve the algorithm’s the exploration ability. Finally, the proposed INSGA-II is compared with six state-of-the-art algorithms on 16 instances of four representative maps. Simulation results indicate that INSGA-II can solve the multi-objective path planning problems with high efficiency.

Cooperative behavior of a heterogeneous robot team for planetary exploration using deep reinforcement learning

As we continue exploration of the Lunar and Martian surfaces and push farther into unknown and unstructured environments, employing a team of distributed heterogeneous robots will increase the odds of success by enabling more complex task planning that utilizes the complementary capabilities and synergy of the team members. A requirement to reap these potential benefits is effective cooperation and collaboration between the members of a robot team. Defining a metric for effective cooperation is a difficult task that will depend on the composition of the team, the task to be performed, and the working environment. This paper establishes a method for determining the reward criteria (figures of merit) that can be used for training the robot swarm through reinforcement learning techniques. A hierarchical framework of rewards is used which, at the lowest level, measures the success of an individual robot in performing its task. The success of all robots performing different subtasks is then measured using the Quantified Cooperation Assessment (QCA) metric which was introduced in our previous research of multi-robot collaboration. Finally, the mission-level success and overall reward is determined by weighting each task using its priority within the overall mission context. A common reward for each of the robotic teammates is then applied within the learning process, which emphasizes group performance over that of an individual and encourages cooperative behavior. This cooperation framework is trained in a grid-based environment representing an exploration mission on a planetary surface by a heterogenous team of robots consisting of a landing craft, a traditional rover, and several small agile robots. The robots are trained concurrently, but with individual policies developed for each agent resulting in a decentralized control scheme. Once trained, the control policies were evaluated in several test environments consisting of novel terrain maps and regions of interest. An average success rate of 90 % was seen in the test environments demonstrating the robustness of the trained policies. The robots have been trained to not only complete the mission but also perform it in a cooperative manner as well as show the scalable and resilient behavior.

Trajectory planning for UAV navigation in dynamic environments with matrix alignment Dijkstra

The trajectory planning for Unmanned aerial vehicles (UAVs) in the dynamic environments is a challenging task. Many restrictions should be taken into consideration, including dynamic terrain collision, no-fly zone criteria, power and fuel criteria and so on. However, some methods treat dynamic restrictions as static in order to reduce cost and obtain efficient and acceptable paths. To achieve optimal, efficient and acceptable paths, we first use a high-dimension matrix, extended hierarchical graph, to model the unexplored dynamic grid environment and to convert weather conditions to passable coefficient. The original shortest path planning task is then translated to plan the safest path. Therefore, we propose a new forward exploration and backward navigation algorithm, matrix alignment Dijkstra (MAD), to pilot UAVs. We use matrix alignment operation to simulate the parallel exploration process of all cells from time t to \(t+1\). This exploration process can be accelerated on a GPU. Finally, we recall the optimal path according to the navigator matrix. In addition, we can achieve UAV path planning from one source cell to multiple target cells within one single run. We validate our method on a real dynamic weather dataset and get competitive results in both efficiency and accuracy. We analyse the performance of MAD on a grid-based benchmark dataset and artificial maze data. Simulated results show MAD is especially helpful when the grid-based environment is large-scale and dynamic.

Parallel Motion Execution and Path Rerouting for a Team of Mobile Robots

This work achieves parallel movements and collision free paths for a team of identical robots evolving in a known environment while satisfying a global Boolean-based formula over a set of regions of interest. The movement capabilities of the robots within the grid-based environment are modeled as a Petri net system. The solution starts from initially known robot paths given by the approach in (Mahulea et al., 2020b). The main advantages of this work are two-folded: (i) it reduces the number of waiting states of the robots throughout their paths, (ii) it considers path rerouting action solved by a MILP problem, without generating new observations along their paths. The rerouting is triggered when the number of robots that are waiting is greater than a threshold, or when some stopped robots inhibit the movement of others. Thus, the proposed method yields parallel movement of robots and path rerouting when needed. The algorithm is integrated in the open-source toolbox RMTool (González et al., 2015).

CARE: A Collision-Aware Mobile Robot Navigation in Grid Environment using Improved Breadth First Search

In recent years, there is an increasing interest in designing autonomous vehicles such as Mobile Robots. However, one of the fundamental needs of Mobile Robots is a collision-free navigation with an optimal path from the source to the destination. In this paper, a collision-free low-complexity Mobile Robot navigation scheme called Collision Aware Mobile Robot navigation in Grid-Environment is designed. The proposed scheme uses the Radio Frequency based Identification method for Mobile Robot localization, the hybrid approach for the path planning, and a predefined decision table for the navigation. The algorithms are implemented in two stages, construction of virtual world and generation of optimal shortest path. The algorithms’ performance is analyzed in grid-based environment of size 5×5,20×20,35×35, and 50 × 50 with four different cases. It is observed that for the environment with no obstacles, the robot explores fewer cells in finding the shortest path. However, the number of turning in the shortest path is always less in the environment with some obstacles than in the case of whole virtual world exploration. The performance results show the effectiveness of the proposed scheme for Mobile Robot navigation in the grid environment.

Critical Comparative Study of Robot Path Planning in Grid-Based Environment

The path planning problem under two-dimensional maps is a fundamental issue in mobile robotics and other applications (unmanned vehicles, and so forth). The traditional algorithms (graph searching, artificial potential field and so forth) are used to find the shortest path that rely on grid-by-grid searching. The main disadvantage of the traditional methods is robot size is not considered and has much redundant in sharp curves which is difficult to guide a robot movement. In this paper, we compared the five baseline approaches of heuristic methods to find the optimal path which reduces the number of turns and time costs to attain a target. The experimental analysis shows that the second-order x derivative has an advantage over other baseline approaches that the search time and the number of turns are reduced.

Path Planning of Mobile Robot With Improved Ant Colony Algorithm and MDP to Produce Smooth Trajectory in Grid-Based Environment.

This approach has been derived mainly to improve quality and efficiency of global path planning for a mobile robot with unknown static obstacle avoidance features in grid-based environment. The quality of the global path in terms of smoothness, path consistency and safety can affect the autonomous behavior of a robot. In this paper, the efficiency of Ant Colony Optimization (ACO) algorithm has improved with additional assistance of A* Multi-Directional algorithm. In the first part, A* Multi-directional algorithm starts to search in map and stores the best nodes area between start and destination with optimal heuristic value and that area of nodes has been chosen for path search by ACO to avoid blind search at initial iterations. The path obtained in grid-based environment consist of points in Cartesian coordinates connected through line segments with sharp bends. Therefore, Markov Decision Process (MDP) trajectory evaluation model is introduced with a novel reward policy to filter and reduce the sharpness in global path generated in grid environment. With arc-length parameterization, a curvilinear smooth route has been generated among filtered waypoints and produces consistency and smoothness in the global path. To achieve a comfort drive and safety for robot, lateral and longitudinal control has been utilized to form a set of optimal trajectories along the reference route, as well as, minimizing total cost. The total cost includes curvature, lateral and longitudinal coordinates constraints. Additionally, for collision detection, at every step the set of optimal local trajectories have been checked for any unexpected obstacle. The results have been verified through simulations in MATLAB compared with previous global path planning algorithms to differentiate the efficiency and quality of derived approach in different constraint environments.

LEO: Liquid Exploration Online

This article introduces a novel approach to the online complete- coverage path planning (CCPP) problem that is specically tailored to the needs of skid-steer tracked robots. In contrast to most of the current state-of-the-art algorithms for this task, the proposed algorithm reduces the number of turning maneuvers, which are responsible for a large part of the robot's energy consumption. Nevertheless, the approach still keeps the total distance traveled at a competitive level. The algorithm operates on a grid-based environment representation and uses a 3x3 prioritization matrix for local navigation decisions. This matrix prioritizes cardinal di- rections leading to a preference for straight motions. In case no progress can be achieved based on a local decision, global path planning is used to choose a path to the closest known unvisited cell, thereby guaranteeing completeness of the approach. In an extensive evaluation using simulation experiments, we show that the new algorithm indeed generates competi- tively short paths with largely reduced turning costs, compared to other state-of-the-art CCPP algorithms. We also illustrate its performance on a real robot.

Rendezvous Planning for Multiple AUVs With Mobile Charging Stations in Dynamic Currents

Operation of autonomous underwater vehicles (AUVs) in large spatiotemporal missions is currently challenged by onboard energy resources requiring manned support. With current methods, AUVs are programmed to return to a static charging station based on a threshold in their energy level. Although this approach has shown success in extending the operational life, it becomes impractical due to interruption of AUV operation and loss of energy needed to return to charging station. It is also not practical for large networks due to shortage of charging stations. We introduce mobile onsite power delivery, which will fundamentally change the range and duration of underwater operations. This letter presents a mission planning method to generate mobile charger trajectories, given pre-defined working AUV trajectories, considering environmental constraints such as currents and obstacles. The problem is formulated as a multiple generalized traveling salesman problem, which is then transformed into a traveling salesman problem. Energy cost in dynamic currents is integrated with a path planning algorithm using a grid-based environment model. A scheduling strategy extends the problem over multiple charging cycles. Simulation results show that the planning method significantly improves mission success and energy expenditure. Field experiments in Lake Superior using two types of AUVs, an unmanned surface vessel, and a manned support vessel validate the feasibility of the planned trajectories for long-term marine missions.

A Proficient Node Deployment Mechanism Using Adjustable Sensing Range in Wireless Sensor Networks

In this paper, a proficient node deployment mechanism is proposed which covers all the target points using adjustable sensing ranges in grid-based wireless sensor networks. In the beginning, the sink and the target points are randomly deployed in a grid-based environment where locations are arbitrarily selected. At first, the ants are positioned at the sink location (grid point) from where the deployment of sensors starts. An ant can move in one of the four possible directions, i.e., {1 (left); 2 (right); 3 (top); 4 (bottom)} from the sink location depending on the maximum number of uncovered target points. In case there is no path to move forward, an existing sensor is selected as the next location corresponding to that next sensor point where a deployed sensor can cover maximum number of uncovered targets in one of the four possible directions. Our method deploys minimum number of sensors to cover all the targets with least number of iterations. It also provides full coverage and connectivity with minimum deployment cost.

Grid-Based Environment Estimation Using Evidential Mapping and Particle Tracking

Modeling and estimating the current local environment by processing sensor measurement data is essential for intelligent vehicles. Static obstacles, dynamic objects, and free space have to be appropriately represented, classified, and filtered. Occupancy grids, known for mapping static environments, provide a common low-level representation using occupancy probabilities with an implicit data association through the discrete grid structure. Extending this idea toward dynamic environments with moving objects requires a static/dynamic classification of measured occupancy and a tracking of the dynamic state of grid cells. In this paper, we propose a new dynamic grid mapping approach. An evidential representation using the Dempster-Shafer framework is used to model hypotheses for static occupancy, dynamic occupancy, free space, and their combined hypotheses. These hypotheses are consistently estimated and accumulated in a dynamic grid map by an adapted evidential filtering, allowing one to distinguish static and dynamic occupancy. The evidential grid mapping is combined with a low-level particle filter tracking that is used to estimate cell velocity distributions and predict dynamic occupancy of the grid map. Static occupancy is directly modeled in the grid map without requiring particles, increasing efficiency, and improving the static/dynamic classification due to the persistent map accumulation. Experimental results with real sensor data show the effectiveness of the proposed approach in challenging scenarios with occlusions and dense traffic.

Integration of Enhanced Jump Point Search Algorithm and Modified Bresenham Technique for Path Finding in Grid-Based Map Environment

Integration of Enhanced Jump Point Search Algorithm and Modified Bresenham Technique for Path Finding in Grid-Based Map Environment

Putting it together: the computational complexity of designing robot controllers and environments for distributed construction

Creating target structures through the coordinated efforts of teams of autonomous robots (possibly aided by specific features in their environments) is a very important problem in distributed robotics. Many specific instances of distributed robotic construction teams have been developed manually. An important issue is whether automated controller design algorithms can both quickly produce robot controllers and guarantee that teams using these controllers will build arbitrary requested target structures correctly; this task may also involve specifying features in the environment that can aid the construction process. In this paper, we give the first computational and parameterized complexity analyses of several problems associated with the design of robot controllers and environments for creating target structures. These problems use a simple finite-state robot controller model that moves in a non-continuous deterministic manner in a grid-based environment. Our goal is to establish whether algorithms exist that are both fast and correct for all inputs and if not, under which restrictions such algorithms are possible. We prove that none of these problems are efficiently solvable in general and remain so under a number of plausible restrictions on controllers, environments, and target structures. We also give the first restrictions relative to which these problems are efficiently solvable and discuss what theoretical solvability and unsolvability results derived relative to the problems examined here mean for real-world construction using robot teams.

Outdoor Location Estimation Using Received Signal Strength-Based Fingerprinting

A cluster-based intersection fingerprinting technique for outdoor location estimation using received signal strength (RSS) is proposed. The performance of the proposed scheme is demonstrated by making comparisons using RSS data from a simulated grid-based urban propagation model, RSS data generated by a network planning tool applied to a rural environment, and RSS data from real city environment. The proposed scheme first uses an optimal clustering scheme to portion the large outside area into different small regions based on the use of RSS deviations from the path loss model. For each region, a fine RSS distribution model is built to provide a good support for further positioning. An improved intersection method is then presented to find the most likely geographical area to further estimate a mobile user's location. A comparison between cluster-based and grid-based environment partitioning is made. The experimental results show that the proposed clustering scheme gives good support for location estimation and the positioning accuracy is significantly improved.

Collaborative Approach in Accessing Homogeneous Medical Data in Grid-Based Environment (Enhancing Diseases Classification).

Collaborative Approach in Accessing Homogeneous Medical Data in Grid-Based Environment (Enhancing Diseases Classification).

Efficient Evaluation of Collisions and Costs on Grid Maps for Autonomous Vehicle Motion Planning

Collision checking is the major computational bottleneck for many robot path and motion planning applications, such as for autonomous vehicles, particularly with grid-based environment representations. Apart from collisions, many applications benefit from incorporating costs into planning; cost functions or cost maps are a common tool. Similar to checking a single configuration for collision, evaluating its cost using a grid-based cost map also requires examining every cell under the robot footprint. This work gives theoretical and practical insights on how to efficiently check a large number of configurations for collision and cost. As part of this work, configuration space costs are formulated, which can be seen as generalization of configuration space obstacles allowing a complete configuration check incorporating the robot geometry to be done using a single lookup. Furthermore, this paper presents two efficient algorithms for their calculation: FAMOD, an approximate method based on convolution, which is independent of the size and the shape of the robot mask, and vHGW-360, an exact method based on the van Herk-Gil-Werman morphological dilation algorithm, which can be used if the robot shape is rectangular. Both algorithms were implemented and evaluated on graphics hardware to demonstrate the applicability and benefit to real-time path and motion planning systems.

A Grid based Medical Image Management System using Alchemi

ABSTRACT A large number of medical images in digital format is generated by hospitals every day. It is acknowledged that medical image databases are a key component in diagnosis. The increasing trend towards digitization of medical images creates a need of technologies for storage, and retrieval of medical images. The paper discusses how to query the medical images and presented a medical images management system based on the DICOM (Digital Imaging and Communications in Medicine) standard .Grids are a promising tool to build medical databases and to face health-related challenges involving computations over large datasets. Indeed, grids offer an infrastructure for sharing data and building virtual databases distributed over several medical sites and sharing processing power. Alchemi grid framework has been deployed to provide grid-based environment. Speeding up the retrieval and feature extraction processes was one of the major achievements of this work.

A Study on Grid-Based Geography Visualization Environment

Visualization is an efficient technology to display the information and the inherent properties of geographic data, mine the regularity and development trends between the data and give researchers the unexpected insight. As the using and distribution of visualization resources exists imbalance, in order to meet the need of Geography Science, this paper presented a Grid Computing-based High-performance Visualization Environment. This environment is based on the mature grid middleware, CGSP to achieve the Grid integration and sharing of the geo science visualization software. A demonstration application was discussed to verify the visualization environment. The environment would achieve the visualization of the massive geo-spatial data efficiently and expediently, enhance the collaborative research to solve problems and improve their research environment.

Implementation of Exhaust Gas Monitoring System in Grid-based Environment

Implementation of Exhaust Gas Monitoring System in Grid-based Environment

The Analysis of Efficiency Dependence of the Shortest Path Finding Algorithms A* and HPA*

Many pathfinding problems in real-world applications require real-time computation of the shortest path between two points in a grid-based environment. It is not a trivial task. While some shortest path finding algorithms may perform admissibly in one condition, they may prove inadmissible in other conditions. HPA* pathfinding algorithm is faster and more memory efficient than the A*algorithm in relatively large two- dimensional grids, but this advantage may not apply to very small or very large grids. The paper deals with the efficiency of A* and HPA*in two-dimensional grids of different sizes. For the sake of completeness of the analysis, HPA* efficiency is measured taking into consideration the number of hierarchy levels and different cluster sizes. Both algorithms have been implemented and tests conducted. Experimental evidence is proposed to demonstrate the algorithm efficiency in various conditions.