Collision-free Routing Research Articles

Addressing unpredictable movements of dynamic obstacles with deep reinforcement learning to ensure safe navigation for omni-wheeled mobile robot

The safe and efficient navigation of mobile robots in the presence of unknown dynamic obstacles remains a complex and unresolved challenge. This paper presents collision-free path planning for a mobile robot that safely deals with multi-directional obstacles, that is, randomly moving dynamic obstacles, using a Deep Reinforcement Learning (DRL) algorithm named Deep Q-Network (DQN) with inflated robot reward functions. The robot moves in a time-efficient and collision-free route while maintaining a safe distance with both static and unpredictable dynamic obstacles. The modified DQN algorithm takes RGB images of the environment as input for training a Convolution Neural Network (CNN) and provides a safe and short path for navigation. The robot used for training is an omni-wheeled mobile robot exploring outdoors, that is, concourse environment, and indoors, that is, home environment. The Closed-Loop Inverse Kinematics (CLIK) algorithm is employed to control a mobile robot to follow the desired path. The simulation results indicate that the proposed algorithm with inflated robot reward functions demonstrates remarkable performance as compared to recently used Reinforcement Learning (RL) algorithms when dealing with both stationary and randomly moving obstacles in the given environment.

Path Planning for Unmanned Aerial Vehicles in Complex Environments

This paper introduces a comprehensive framework for generating obstacle-free flight paths for unmanned aerial vehicles (UAVs) in intricate 3D environments. The system leverages the Rapidly Exploring Random Tree (RRT) algorithm to design trajectories that effectively avoid collisions with structures of diverse shapes and sizes. Discussion revolves around the challenges encountered during development and the successful achievement of generating collision-free routes. While the system represents an initial iteration, it serves as a foundation for future projects aiming to refine and expand upon its capabilities. Future work includes simulation testing and integration into UAV missions for image acquisition and structure scanning. Additionally, considerations for swarm deployment and 3D reconstruction using various sensor combinations are outlined. This research contributes to the advancement of autonomous UAV navigation in real-world scenarios.

Simultaneous scheduling of jobs and transporters in a hybrid flow shop with collision-free transporter routing: A novel parallel heuristic

A computationally potent two-stage parallel heuristic (TSPH) and a mixed integer linear programming (MILP) are suggested to address the simultaneous scheduling of jobs and transporters in a hybrid flow shop system, wherein multiple transporters, stage omission, transporter eligibility, machine eligibility, and collision-free transporter routing are assumed. To the best of our knowledge, the significance of transporter collision-free routing has not been spotlighted in the literature on flow shop systems. Collision-free routing of transporters is a requisite technical attribute as transport means may collide on routes and break down the whole system. Our MILP and TSPH assume collision-free routing to impede the issue. Being equipped with parallel computing, TSPH can deal with large problems in a considerably short time. To support, TSPH is analogized against the suggested MILP, two-step MILP (TSMILP), and an efficient parallel meta-heuristic (i.e., parallel particle swarm optimization and genetic algorithm (PPSOGA)) that outperformed many literarily prominent meta-heuristics in the literature. The benchmark results uncover that TSPH outclasses both TSMILP and PPSOGA in the quality of solutions. Eventually, utilizing the convergence plot and Nemenyi’s post-hoc procedure for Friedman’s test, it is revealed that the outcomes of TSPH are remarkably more desirable than those of TSMILP and PPSOGA.

Hybrid path planning method for USV using bidirectional A* and improved DWA considering the manoeuvrability and COLREGs

This paper proposes a hybrid dynamic path planning algorithm that integrates global path planning (GPP) and local path planning (LPP). The objective is to effectively address the challenges associated with path planning and collision avoidance for unmanned surface vehicles (USV) in complex environments. The bidirectional A* algorithm is used to search the optimized strategy based on the actual geographical information map for the navigation routes of USV. The Dynamic Window Algorithm (DWA) is employed, and a novel cost function is formulated considering the USV's manoeuvring characteristics, dynamic constraints, and environmental information. The primary purpose is to ensure the compliance of the USV with COLREGs during dynamic obstacle avoidance. It accomplishes this by providing positive reinforcement for achieving the correct turning speed when the USV determines the orientation of an approaching ship. Another evaluation function is introduced to mitigate the problem of local optima in complex environments that the LPP may encounter. The actual work map is used to model the application scenario with the obstacles in the presence of incoming ships, the simulation results show that the proposed algorithm is capable of generating adaptive, collision-free routes while adhering to COLREGs rules.

Optimization of trucks and drones in tandem delivery network with drone trajectory planning

Drones are widely recognized as an efficient method for delivering small and lightweight parcels, such as medicines, to customers who are difficult to reach by logistic vehicles. Various studies and projects have been conducted to integrate drones into the traditional logistic service. In this approach, drones stay on trucks, take off from trucks at specific locations to make deliveries to customers, and return to the trucks to replenish and recharge. However, drone trajectories could overlap when multiple drones are used for delivery, which can lead to the risk of collisions. Consequently, the need to address the trajectory planning of drones becomes essential. This work introduces a novel truck-drone routing problem. The novelty presented by this problem lies in the integration of drone trajectory planning problems into the truck-drone routing to generate collision-free routes for drones. This concept has not been previously explored in the existing body of literature. The problem is formulated as a mixed-integer programming problem, and a matheuristic is proposed to solve it. Computational experiments show that the proposed matheuristic outperforms the commercial solver and genetic algorithm in terms of solution quality and search efficiency. Results demonstrate that the integrated truck-drone delivery mode has a lower operating cost than the mere truck mode, and including trajectory planning can prevent potential collisions.

PRM Path Smoothening by Circular Arc Fillet Method for Mobile Robot Navigation

The problem of motion planning and navigation for mobile robots in complex environments has been a central issue in robotics. Navigating these environments requires sophisticated algorithms that handle obstacles and provide smooth, efficient paths. The Probabilistic Roadmap (PRM) method is a widespread technique in robotics for constructing paths for mobile robot navigation. In this study, we propose a novel path-smoothing method using arc fillets for path planning, building on PRM's foundation in the presence of obstacles. Our method operates in two primary stages to improve path efficiency and quality. The first stage generates the shortest path between the initial and goal states in an obstacle-rich environment using PRM, constructing a straight-line, collision-free route. The second stage smooths corners caused by nodes with arc fillets, ensuring smooth turns and minimizing abrupt changes in direction, resulting in more natural and efficient robot motion. We conducted simulations and tests using various PRM features to evaluate the proposed method. The results indicate that the built route offers a smooth turning motion and quicker, more compact movement while evading obstacles. This study contributes to mobile robot navigation by offering a practical approach to improving pathway quality in challenging environments.

Pipe Routing Design for Aero-Engine Using Monte Carlo Tree Search Method

Pipe routing design (PRD) is a complex process that involves a large search space and requires experienced professionals. Despite advancements in aero-engine design, PRD remains a stage that is not completely automated. In this article, we present a new approach to PRD by formulating it as a Markov decision process and proposing a pipe routing design method based on Monte Carlo Tree Search (PRD-MCTS). Firstly, this paper uses an intelligent algorithm to look for enough paths for each pair of joints. Secondly, PRD-MCTS regards each path as a candidate choice, and then PRD-MCTS randomly chooses a path and calculates the probability of collision-free routing until the set time. The method selects the path with the highest probability and updates the environment for the next selection. A simplified environment from the aero engine verifies the correctness of the methods.

Collision risk-informed weather routing for sailboats

Selected COLREG rules, good seamanship and sheer common sense indicate that it is in a sailboat's interest to follow collision-free routes without relying on large power-driven ships to give way. Until now, however, no method has integrated a sailboat's weather routing with collision risk monitoring and collision avoidance. Therefore, a new deterministic approach to combine the above features within one method is introduced here. The proposed method is based on Dijkstra's algorithm, where edges may be temporarily removed due to the presence of other ships. This paper presents a design of the main weather routing algorithm and the collision risk monitoring part, which applies an elliptic domain generated automatically around the target and dependent on the target's length. The method has been implemented and tested in a series of computer simulations. The results are provided and discussed here. They confirm the method's effectiveness in terms of determining collision-risk-free routes, as well as its acceptable computational time. They also show how the latter can be shortened at the cost of obtaining suboptimal routes. Finally, they emphasize the importance of considering successive weather forecasts, risk monitoring and route updates.

Adaptive Path Planning for Fusing Rapidly Exploring Random Trees and Deep Reinforcement Learning in an Agriculture Dynamic Environment UAVs

Unmanned aerial vehicles (UAV) are a suitable solution for monitoring growing cultures due to the possibility of covering a large area and the necessity of periodic monitoring. In inspection and monitoring tasks, the UAV must find an optimal or near-optimal collision-free route given initial and target positions. In this sense, path-planning strategies are crucial, especially online path planning that can represent the robot’s operational environment or for control purposes. Therefore, this paper proposes an online adaptive path-planning solution based on the fusion of rapidly exploring random trees (RRT) and deep reinforcement learning (DRL) algorithms applied to the generation and control of the UAV autonomous trajectory during an olive-growing fly traps inspection task. The main objective of this proposal is to provide a reliable route for the UAV to reach the inspection points in the tree space to capture an image of the trap autonomously, avoiding possible obstacles present in the environment. The proposed framework was tested in a simulated environment using Gazebo and ROS. The results showed that the proposed solution accomplished the trial for environments up to 300 m3 and with 10 dynamic objects.

COLREGs Experience-Based Real-Time Route Planning of Intelligent Ship

The route planning of intelligent ship has been studied a lot, especially in the dynamic planning of collision avoidance, but it is still faced with some difficulties, such as complex model structure, poor compatibility of collision avoidance rules, difficult to practice and so on. Therefore, this paper adopts the integration of field model theory and COLREGs for real-time ship route planning, taking into account the model efficiency and application value. It uses FM algorithm as the basic route planning algorithm, introducing the virtual electric field model to construct the ship navigation situation, and gives the guidance strategy of collision avoidance behavior under different encounter situations considering the COLREGs and daily navigation experience, which integrates it into the field model to form a real-time collision avoidance adjustment route planning algorithm. The proposed algorithm has been compared and verified by simulation in multiple scenes, it is worth mentioning that the algorithm can potentially be developed to advance collision-free route planning in unconstructed environment.

Mobile Robot Navigation Using Planning Algorithm and Sliding Mode Control in a Cluttered Environment

The research contribution of the present work is to solve the path planning and path tracking problems in static and dynamic environments. A new Planning Navigation Algorithm Technique is developed in order to solve the problem of navigation with obstacle avoidance. The basic idea of this algorithm searches for a safe path for navigation. First, this algorithm is focused to identify an optimal collision-free route to a spatially defined objective. Then, in each displacement, the developed algorithm handles to maximize the distance between the obstacles and minimize the distance to the goal. This is to obtain the optimal trajectory for navigation. On the other side, a sliding mode controller is adopted to solve the tracking trajectory task. The basic idea of this control system is to allow the robot mobile to track the desired trajectory with minimum error. In addition, the comparative study between the proposed approach and the previous work is presented in order to demonstrate the satisfaction of the proposed strategy. Finally, simulation results which are developed using Matlab software are presented to show the robustness and efficiency of the developed algorithm and the reactivity of the proposed sliding mode controller.

Algorithms for Smooth, Safe and Quick Routing on Sensor-Equipped Grid Networks.

Automation plays an important role in modern transportation and handling systems, e.g., to control the routes of aircraft and ground service equipment in airport aprons, automated guided vehicles in port terminals or in public transportation, handling robots in automated factories, drones in warehouse picking operations, etc. Information technology provides hardware and software (e.g., collision detection sensors, routing and collision avoidance logic) that contribute to safe and efficient operations, with relevant social benefits in terms of improved system performance and reduced accident rates. In this context, we address the design of efficient collision-free routes in a minimum-size routing network. We consider a grid and a set of vehicles, each moving from the bottom of the origin column to the top of the destination column. Smooth nonstop paths are required, without collisions nor deviations from shortest paths, and we investigate the minimum number of horizontal lanes allowing for such routing. The problem is known as fleet quickest routing problem on grids. We propose a mathematical formulation solved, for small instances, through standard solvers. For larger instances, we devise heuristics that, based on known combinatorial properties, define priorities, and design collision-free routes. Experiments on random instances show that our algorithms are able to quickly provide good quality solutions.

Energy Efficient Proactive Routing Scheme for Enabling Reliable Communication in Underwater Internet of Things

The Underwater Internet of Things (UIoT) is a network of smart interconnected devices operating under various aqueous environments. In UIoT, due to aqueous feature of signal absorption, data signals are transmitted at low frequency. Similarly, significant interference and collisions degrade transmission quality, resulting in a low Packet Delivery Ratio (PDR) and a long End-to-End (E2E) delay. Moreover, a significant amount of energy is being wasted due to the void hole problem, which arises when the source node does not identify an instant forwarder node. Thus, a reliable communication with enhanced network lifetime is highly desired in UIoT. Therefore, this work proposes a proactive energy Efficient Layer-by-Layer Watchman-based Collision Free Routing (ELW-CFR) scheme to address the above mentioned issues. The proposed scheme provides the low E2E delay and high PDR while avoiding the void hole problem. Extensive simulations have been performed to show the performance superiority of the proposed scheme against the state-of-the-art schemes.

Numerical simulations of agent navigation via Half-Sweep Modified Two-Parameter Over-Relaxation (HSMTOR)

The research on the efficiency of route navigation has been continuously developing. Especially, the capability of the generated route to provide a collision-free route for an agent to move in a particular environment. Thus, this study attempts to solve the route navigational problem iteratively via a numerical method. A new method called Half-Sweep Modified Two-Parameter Over-Relaxation (HSMTOR) is used to solve the navigational problems. For numerical simulation purposes, HSMTOR is used to obtain Laplace’s equation solutions called harmonic functions. A gradient descent search algorithm then utilizes the harmonic functions to provide a smooth and collision-free route for an agent to commute inside the environment. In addition, the formulation of the HSMTOR iterative method is presented. Several numerical experiments and simulations are conducted in order to verify the efficiency of the proposed method. The result shows that the proposed method performed better than the existing methods such as full-, half-sweep for Modified Successive Over-Relaxation, Modified Accelerated Over-Relaxation and Modified Two-Parameter Over-Relaxation respectively (FSMSOR, HSMSOR, FSMAOR, HSMAOR and FSMTOR).

Bezier Curve Collision-Free Route Planning Using Meta-Heuristic Optimization


 
 
 
 A collision-free route is very important for achieving sustainability in a manufacturing process and vehicle robot trajectories that commonly operate in a hazardous environment surrounded by obstacles. This paper presents a collision avoidance algorithm using a Bezier curve as a route path. The route planning is modeled as an optimization problem with the objective optimization is to minimize the route length considering an avoiding collision constraint. The collision-avoidance algorithm based on curve point analysis is developed incorporating metaheuristic optimizations, namely a Genetic Algorithm (GA) and a Grey Wolf Optimizer (GWO). In the collision avoidance algorithm, checking of curve point's position is important to evaluate the individual fitness value. The curve points are analyzed in such a way so that only the paths which are outside the obstacle area are selected. In this case, besides the minimum length as a fitness function, the constraint is the position of curve points from an obstacle. With the help of meta-heuristic optimization, the developed collision avoidance algorithm has been applied successfully to different types of obstacle geometries. The optimization problem is converted to the maximization problem so that the highest fitness value is used to measure the performance of the GA and GWO. In general, results show that the GWO outperforms the GA, where it exhibits the highest fitness value. However, the GA has shown better performance for the narrow passage problem than that of the GWO. Thus, for future research, implementing the hybrid technique combining the GA and the GWO to solve the advanced path planning is essential.
 
 
 

Collision-free routing problem with restricted L-path

Given a set of vehicles that are allowed to move in a plane along a predefined directed rectilinear path, the collision-free routing problem seeks a maximum number of vehicles that can move without collision. This problem is known to be NP-hard (Ajaykumar et al., 2016). Here we study a variant of this problem called the constrained collision-free routing problem (in short, CCRP ). In this problem, each vehicle is allowed only to move in a directed L-shaped path. First, we show that CCRP is NP-hard. Further, we prove that any β -approximation algorithm for the maximum independent set problem in B 1 -VPG graphs would produce a β -approximation for CCRP . Also, we propose a 2-approximation algorithm for the maximum independent set problem on intersection graph of n unit-L frames (the union of a unit-length vertical and a unit-length horizontal line segment that shares an endpoint) with O ( n ) space and O ( n 2 ) time.

Coordination of multi-robot path planning for warehouse application using smart approach for identifying destinations

Path planning and coordination in a multi-robot system are important and complex tasks in any environment. In a multi-robot system, there can be multiple objectives to be achieved by multiple robots simultaneously. Nowadays, many mobile service robots are being used in warehouses to reduce running costs and overheads. In a large warehouse, there can be multiple robots to handle the number of operations. Planning a path means to find out the optimal route, and coordinating them means a collision-free route. To get both the parameters to reach their optimal level becomes a tedious task to achieve. The efficiency of overall warehouse operation can be improved by adequately addressing the coordination and path planning issues among the robots. In warehouses, each robot has to navigate to its destination by finding a collision-free optimal route in coordination with other robots. In this paper, a comparative study with the acclaimed path planning and coordination has been presented. The proposed smart approach has been presented for a multi-robot system to find a collision-free optimal path in a warehouse to handle storage pods. This paper proposes a smart distance metric-based approach for a multi-robot system to identify their goals smartly and traverse only a minimal path to reach their goal without getting being collided. It uses a smart distance metric-based approach to find the intended path. The proposed work performs better when compared with other works like A* and ILP. It is strictly monitored that there is no collision occurred during execution. Three different instances of a warehouse have been considered to carry out the experiments with parameters such as path length, average path and elapsed time. The experiments with 800 pods and 16 robots report the improvement in performance up to 2.5% and 13% in average path length and elapsed time.

물류센터 내에서의 다수 로봇 경로 최적화

The Robotic Mobile Fulfillment System (RMFS) is a robotized material handling system where multiple robots transport movable shelves to and from stocking points and workstations in a warehouse. In this paper, we deal with the problem of finding collision-free routes for multiple robots to move from their starting points to destinations in a RMFS, which is defined as a generalization of the multi-agent path finding problem (MAPFP). In RMFSs, collisions among moving robots may occur due to the physical volume of robots and the inexact execution of planned routes by robots in terms of the planned timing of passing through points on the routes, which makes it difficult to directly apply the existing algorithms for MAPFP that assume the exact execution of planned routes by agents with no physical volume. Therefore, we defined the problem as a multi-agent path finding problem with the prespecified time spacing at points to address the possible sources of collisions among robots, and propose an integer optimization model for the problem. Since the proposed integer optimization model has a large number of decision variables and constraints, we propose heuristic and exact algorithms based on the Lagrangian relaxation technique and the conflict based search. Experimental results show that the proposed algorithms are effective in finding good quality solutions within a limited time even when the number of robots or the time spacing are large.

Sequential graph-based routing algorithm for electrical harnesses, tubes, and hoses in a commercial vehicle

The routing design of the various electrical wires, tubes, and hoses of a commercial vehicle requires a significant number of man-hours because of the variety of the commercial vehicles, frequent design changes of other vehicular components and the manual trial-and-error approaches. This study proposes a new graph-based routing algorithm to find the collision-free routing path in the constrained space of a commercial vehicle. Minimal spanning tree is adopted to connect multi-terminal points in a graph and Dijkstra’s algorithm is used to find the shortest route among the candidate paths; the design domain is divided into several sub-domains to simplify the graph and the proposed algorithm solves the routing problems in a sequential manner to deal intermediate points. Then, the proposed method was applied to the design of the routes for four different routing components of a commercial truck. The results indicate that the developed methodology can provide a satisfactory routing design satisfying all the requirements of the design experts in the automotive industry.

Mechanism of dynamic automatic collision avoidance and the optimal route in multi-ship encounter situations

Autonomous navigation on the open sea involving automatic collision avoidance and route planning helps to ensure navigational safety. To judge whether all target ships (TSs) will pass safely and find the optimal route under multi-ship encounter situations, the relationship between the variations in the own ship (OS) velocity vector after nonlinear course altering motion and the collision avoidance result, which is defined as the collision avoidance mechanism, was analyzed. Methods producing the optimal route were also proposed. First, the static collision avoidance mechanism based on the ship domain and velocity obstacle (VO) was introduced. On that basis, the collision-free course alteration range of the OS, without consideration of the real manoeuvring process, was presented. Second, the ship motion equations and fuzzy adaptive proportion integral derivative (PID) control method were combined to develop a course control system. This system was then used to predict OS motions during the course-altering process. Based on this prediction, TS positions were calculated. Subsequently, the dynamic collision-free course altering ranges for the OS were obtained. Third, a model to compute the optimal route was introduced. Finally, simulations were performed under a situation including six TSs and two static objects, and the shortest collision-free route that satisfies both regulations for preventing collisions and good seamanship was found.