Multi-robot Path Research Articles

An intelligent multi-robot path planning in a dynamic environment using improved gravitational search algorithm

This paper proposes a new methodology to optimize trajectory of the path for multi-robots using improved gravitational search algorithm (IGSA) in clutter environment. Classical GSA has been improved in this paper based on the communication and memory characteristics of particle swarm optimization (PSO). IGSA technique is incorporated into the multi-robot system in a dynamic framework, which will provide robust performance, self-deterministic cooperation, and coping with an inhospitable environment. The robots in the team make independent decisions, coordinate, and cooperate with each other to accomplish a common goal using the developed IGSA. A path planning scheme has been developed using IGSA to optimally obtain the succeeding positions of the robots from the existing position in the proposed environment. Finally, the analytical and experimental results of the multi-robot path planning were compared with those obtained by IGSA, GSA and differential evolution (DE) in a similar environment. The simulation and the Khepera environment result show outperforms of IGSA as compared to GSA and DE with respect to the average total trajectory path deviation, average uncovered trajectory target distance and energy optimization in terms of rotation.

A hybrid improved PSO-DV algorithm for multi-robot path planning in a clutter environment

This paper proposed a novel approach to determine the optimal trajectory of the path for multi-robots in a clutter environment using hybridization of improved particle swarm optimization (IPSO) with differentially perturbed velocity (DV) algorithm. The objective of the algorithm is to minimize the maximum path length that corresponds to minimize the arrival time of all the robots to their respective destination in the environment. The robots on the team make independent decisions, coordinate, and cooperate with each other to determine the next positions from their current position in the world map using proposed hybrid IPSO-DV. The proposed scheme adjusts the velocity of the robots by incorporating a vector differential operator inherited from Differential Evolution (DE) in IPSO. Finally the analytical and experimental results of the multi-robot path planning have been compared to those obtained by IPSO-DV, IPSO, DE in a similar environment. Simulation and khepera environment results are compared with those obtained by IPSO-DV to ensure the integrity of the algorithm. The results obtained from Simulation as well as Khepera environment reveal that, the proposed IPSO-DV performs better than IPSO and DE with respect to optimal trajectory path length and arrival time.

A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning

This paper proposed a new methodology to determine the optimal trajectory of the path for multi-robot in a clutter environment using hybridization of improved particle swarm optimization (IPSO) with an improved gravitational search algorithm (IGSA). The proposed approach embedded the social essence of IPSO with motion mechanism of IGSA. The proposed hybridization IPSO–IGSA maintain the efficient balance between exploration and exploitation because of adopting co-evolutionary techniques to update the IGSA acceleration and particle positions with IPSO velocity simultaneously. The objective of the algorithm is to minimize the maximum path length that corresponds to minimize the arrival time of all robots to their respective destination in the environment. The robot on the team make independent decisions, coordinate, and cooperate with each other to determine the next positions from their current position in the world map using proposed hybrid IPSO–IGSA. Finally the analytical and experimental results of the multi-robot path planning were compared to those obtained by IPSO–IGSA, IPSO, IGSA in a similar environment. The Simulation and the Khepera environment result show outperforms of IPSO–IGSA as compared with IPSO and IGSA with respect to optimize the path length from predefine initial position to designation position ,energy optimization in the terms of number of turn and arrival time.

SHP: Smooth Hypocycloidal Paths with Collision-Free and Decoupled Multi-Robot Path Planning

Generating smooth and continuous paths for robots with collision avoidance, which avoid sharp turns, is an important problem in the context of autonomous robot navigation. This paper presents novel smooth hypocycloidal paths (SHP) for robot motion. It is integrated with collision-free and decoupled multi-robot path planning. An SHP diffuses (i.e., moves points along segments) the points of sharp turns in the global path of the map into nodes, which are used to generate smooth hypocycloidal curves that maintain a safe clearance in relation to the obstacles. These nodes are also used as safe points of retreat to avoid collision with other robots. The novel contributions of this work are as follows: (1) The proposed work is the first use of hypocycloid geometry to produce smooth and continuous paths for robot motion. A mathematical analysis of SHP generation in various scenarios is discussed. (2) The proposed work is also the first to consider the case of smooth and collision-free path generation for a load carrying robot. (3) Traditionally, path smoothing and collision avoidance have been addressed as separate problems. This work proposes integrated and decoupled collision-free multi-robot path planning. ‵Node caching‵ is proposed to improve efficiency. A decoupled approach with local communication enables the paths of robots to be dynamically changed. (4) A novel ‵multi-robot map update‵ in case of dynamic obstacles in the map is proposed, such that robots update other robots about the positions of dynamic obstacles in the map. A timestamp feature ensures that all the robots have the most updated map. Comparison between SHP and other path smoothing techniques and experimental results in real environments confirm that SHP can generate smooth paths for robots and avoid collision with other robots through local communication.

MRPPSim: A Multi-Robot Path Planning Simulation

Multi-robot path planning problem is an interesting problem of research having great potential for several optimization problems in the world. In multi-robot path planning problem domain (MRPP), robots must move from their start locations to their goal locations avoiding collisions with each other. MRPP is a relevant problem in several domains, including; automatic packages inside a warehouse, automated guided vehicles, planetary exploration, robotics mining, and video games. This work introduces MRPPSim; a new modeling, evaluation and simulation tool for multi-robot path planning algorithms and its applications. In doing so, it handles all the aspects related to the multi-robot path planning algorithms. Through its working, MRPPSim unifies the representation for the input. This algorithm provides researchers with a set of evaluation models with each of them serving a set of objectives. It provides a comprehensive method to evaluate and compare the algorithm’s performance to the ones that solve public benchmark problems inas shown in literature. The work presented in this paper also provides a complete tool to reformat and control user input, critical small benchmark, biconnected, random and grid problems. Once all of this is performed, it calculates the common performance measurements of multi-robot path planning algorithms in a unified way. The work presented in this paper animates the results so the researchers can follow their algorithms’ executions. In addition, MRPPSim is designed as set of models, each is dedicated to a specific function, this allows new algorithm, evaluation model, or performance measurements to be easily plugged into the simulator.

Intractability of Optimal Multirobot Path Planning on Planar Graphs

We study the computational complexity of optimally solving multirobot path planning problems on planar graphs. For four common time- and distance-based objectives, we show that the associated path optimization problems for multiple robots are all NP-complete, even when the underlying graph is planar. Establishing the computational intractability of optimal multirobot path planning problems on planar graphs has important practical implications. In particular, our result suggests the preferred approach toward solving such problems, when the number of robots is large, is to augment the planar environment to reduce the sharing of paths among robots traveling in opposite directions on those paths. Indeed, such efficiency boosting structures, such as highways and elevated intersections, are ubiquitous in robotics and transportation applications.

A Multi-Robot Path Planning Scheme and Management based on Power Availability

A Multi-Robot Path Planning Scheme and Management based on Power Availability

Intelligent-based multi-robot path planning inspired by improved classical Q-learning and improved particle swarm optimization with perturbed velocity

Classical Q-learning takes huge computation to calculate the Q-value for all possible actions in a particular state and takes large space to store its Q-value for all actions, as a result of which its convergence rate is slow. This paper proposed a new methodology to determine the optimize trajectory of the path for multi-robots in clutter environment using hybridization of improving classical Q-learning based on four fundamental principles with improved particle swarm optimization (IPSO) by modifying parameters and differentially perturbed velocity (DV) algorithm for improving the convergence. The algorithms are used to minimize path length and arrival time of all the robots to their respective destination in the environment and reducing the turning angle of each robot to reduce the energy consumption of each robot. In this proposed scheme, the improve classical Q-learning stores the Q-value of the best action of the state and thus save the storage space, which is used to decide the Pbest and gbest of the improved PSO in each iteration, and the velocity of the IPSO is adjusted by the vector differential operator inherited from differential evolution (DE). The validation of the algorithm is studied in simulated and Khepera-II robot.

狭隘环境下一种多机器人路径规划方法

狭隘环境下多机器人路径规划使用共享资源时，极易产生冲突，优先顺序化是解决共享资源冲突的一个重要技术。本文提出了一种基于学习分类器的动态分配优先权的方法，提高机器人团队的性能。首先机器人通过XCS优化各自的行为，然后引入和训练高水平的机器人管理者来分配优先权解决冲突。本方法适用于部分可知的Markov环境，仿真实验结果表明本文所提方法用于解决多机器人的路径规划冲突是有效的，提高了多机器人系统解决路径规划冲突的能力。 Under narrow environments, conflict easily occurs when multi-robot path planning uses shared resources, and prioritisation is an important technology to solve this problem. This paper pre- sents a dynamic allocation priority method based on learning classifier to improve the perfor-mance of the robot team. Firstly robots optimize their behaviors by XCS, and then high-level robot managers are introduced and trained to resolve conflicts by assigning priority. The novel approach is designed for partially observable Markov decision process environments. Simulation results show that the method presented is effective to solve the conflict in multi-robot path planning and improves the capacity of multi-robot path planning.

Subdimensional expansion for multirobot path planning

Planning optimal paths for large numbers of robots is computationally expensive. In this paper, we introduce a new framework for multirobot path planning called subdimensional expansion, which initially plans for each robot individually, and then coordinates motion among the robots as needed. More specifically, subdimensional expansion initially creates a one-dimensional search space embedded in the joint configuration space of the multirobot system. When the search space is found to be blocked during planning by a robot–robot collision, the dimensionality of the search space is locally increased to ensure that an alternative path can be found. As a result, robots are only coordinated when necessary, which reduces the computational cost of finding a path. We present the M⁎ algorithm, an implementation of subdimensional expansion that adapts the A⁎ planner to perform efficient multirobot planning. M⁎ is proven to be complete and to find minimal cost paths. Simulation results are presented that show that M⁎ outperforms existing optimal multirobot path planning algorithms.

Automated robotic parking systems: real-time, concurrent and multi-robot path planning in dynamic environments

This paper presents an integrated framework for a suite of dynamic path planning algorithms for real time and concurrent movement of multiple robotic carts across a parking garage floor layout without driving lanes. Planning and search algorithms were formulated to address major aspects of automation for storage and retrieval of cars loaded onto robotic mobile carts. Path planning algorithms including A*, D* Lite and Uniform Cost Search were implemented and integrated within a unified framework to guide the robotic carts from a starting point to their destination during the storage and the retrieval processes. For the circumstances where there is no clear path for a car being stored or retrieved, a procedure was developed to unblock the obstacles in the path. A policy that minimizes obstructions was defined for assigning the parking spots on a given floor for arriving cars. Performance evaluation of the overall proposed system was done using a multithreaded software application. A variety of rectangular parking lot layouts including those with 20 × 20, 20 × 40, 30 × 40, and 40 × 40 parking spaces were considered in the simulation study. Performance metrics of path length, planning or search time and memory space requirements were monitored. Simulation results demonstrate that the proposed design facilitates near optimal paths, and is able to handle tens of concurrent requests in real time and in the presence of immobilized carts.

Decentralized trajectory optimization using virtual motion camouflage and particle swarm optimization

This paper investigates a decentralized trajectory optimization method to solve a nonlinear constrained trajectory optimization problem. Especially, we consider a problem constrained on the terminal time and angle in a multi-robot application. The proposed algorithm is based on virtual motion camouflage (VMC) and particle swarm optimization (PSO). VMC changes a typical full space optimal problem to a subspace optimal problem, so it can reduce the dimension of the original problem by using path control parameters (PCPs). If PCPs are optimized, then the optimal path can be obtained. In this work, PSO is used to optimize these PCPs. In multi-robot path planning, each robot generates its own optimal path by using VMC and PSO, and sends its path information to the other robots. Then, the other robots use this path information when planning their own paths. Simulation and experimental results show that the optimal paths considering the terminal time and angle constraints are effectively generated by decentralized VMC and PSO.

Multi-Robot Fish Path Planning Based on the Modified A* Algorithm

Based on improved A* algorithm, this paper proposes the optimal path planning of robot fish in globally known environment, so as to achieve better coordination between the robot fish by means of improving their path planning. In the known obstacle environment which is rasterized, target nodes are generated via smoothing A* algorithm. The unnecessary connection points are removed then and the path is smoothed at the turning points. That improved algorithm, in combination with distributed scroll algorithms, is applied to multi-robot path planning in an effort to optimize the path with the avoidance of collision. The experimental results on the 2D simulation platform have verified the feasibility of that method.

An Improved Ant Colony Optimization for the Multi-Robot Path Planning with Timeliness

To achieve efficient search performance for the multi-robot system which carries out the goal search task with consideration of timeliness, a multi-robot collaborative path planning system is designed to guide the robots during the search process. In the system, a planning method based on an Improved Ant Colony Optimization (IACO) algorithm is proposed. In the solution procedure, the path cost and goal timeliness are taken as two optimization goals. Compared to the traditional optimization algorithm, the IACO algorithm has global superiority which brings about better solution. Experiment results show that all robots accomplish the search task safely and efficiently by collaborative work using the proposed approach.

Coordination in Navigation of Multiple Mobile Robots

Coordination in the problem of multirobot path planning enables the individual robots to escape collision when planned in a decentralized manner. A good coordination strategy should not be time consuming, should be probabilistically complete, and should not overly prefer a robot. In this article, an altering local search-based strategy is studied. Experiments reveal the ability of the algorithm to place the different robots judiciously for near-optimal collision avoidance. The algorithm takes less time compared to the centralized approaches, is probabilistically complete as opposed to the reactive approaches, and treats the robots alike as opposed to the prioritized approaches.

Probabilistic Multi Robot Path Planning in Dynamic Environments: A Comparison between A* and DFS

paper, a probabilistic roadmap planner algorithm with the multi robot path planning problem have been proposed by using the A* search algorithm in a dynamic environment. The whole process consists of two phases. In the first phase: Preprocessing phase, the work space is converted into the configuration space, constructing a probabilistic roadmap graph in the free space, and finding the optimal path for each robot using a global planner that avoids the collision with the static obstacles. The second phase: Moving phase, moves each robot in a prioritized manner from its starting point to its ending point through a near optimal path with avoiding collision with the moving obstacles and the other robots. A comparison has been done with the depth first algorithm to see the difference. The simulation results shows that choosing A* search algorithm affect positively the speed of the two phases together in comparison to the depth first search algorithm.

REDUNDANCY ELIMINATION IN HIGHLY PARALLEL SOLUTIONS OF MOTION COORDINATION PROBLEMS

Problems of motion coordination of multiple entities are addressed in this paper. These problems are dealt on the abstract level where they can be viewed as tasks of constructing a spatial-temporal plan for a set of identical mobile entities. The entities reside in a certain environment where they can move. Each entity need to reach a given goal position supposed it is starting from some initial position. The most abstract formal representations of coordinated motion problems are known as “pebble motion on a graph” and “multi-robot path planning”. The existent algorithms for pebble motion and multi-robot problems were suspected of generating solutions containing redundancies. This hypothesis eventually confirmed in this work. We present several techniques for identifying and eliminating redundancies from solutions generated by existent algorithms. An extensive experimental evaluation was performed and it showed that the quality of generated solutions can be improved up to the order of magnitude. We also identify parameters characterizing instances of problems where a significant improvement is expectable.

A Hierarchical Conflict Resolution Method for Multi-Robot Path Planning

Multi-robot path planning using shared resources, easily conflict, prioritisation is the shared resource conflicts to resolve an important technology. This paper presents a learning classifier based on dynamic allocation of priority methods to improve the performance of the robot team. Individual robots learn to optimize their behaviors first, and then a high-level planner robot is introduced and trained to resolve conflicts by assigning priority. The novel approach is designed for Partially Observable Markov Decision Process environments. Simulation results show that the method used to solve the conflict in multi-robot path planning is effective and improve the capacity of multi-robot path planning.

Multi-Robot Path Planning Based on Learning Classifier System with Policy Gradient Reinforcement Learning and Support Vector Machine (PGRL-SVM)

This paper presented a novel approach to solving the problem of robot avoidance collision planning. A Learning Classifier System is a accuracy-based machine learning system using gradient descent that combines covering operator and genetic algorithm. The covering operator is responsible for adjusting precision and large search space according to some reward obtained from the environment. The genetic algorithm acts as an innovation discovery component which is responsible for discovering new better path planning rules. The advantages of this approach are its accuracy-based representation, that can be easily reduce learning space,online learning ability,robustness due to the use of genetic algorithm.

Path Planning of Multi-Robot Fish Based on Cooperative Game Model

In order to improve the sense of cooperation of robotic fishes between each other to complete the task with high efficiency, this paper presents a path planning method for multi-robot fish based on the cooperative game model. With a mathematical model established on the cooperative game which is based on the game theory, the robotic fish in this system corresponds to the other participants in the game, benefit function of the game player on the cooperation model of multi-robot fish path construction is studied in this paper, which gets system Pareto optimal solution, then getting the optimal path of the entire system. Using the above method, the authors simulate and analyze on the robotic fish in water 2D simulation platform, and to use Matlab to simulate robot fishs track. The results of analysis indicate: Applying the path planning theory in the multi-robot fish movement system is effective and feasible, and that avoids planning only a single robotic fishs optimal path in the system, while leaving the movement of system in the beneath coordination.