scholarly journals Lifelong Path Planning with Kinematic Constraints for Multi-Agent Pickup and Delivery

2019 ◽  
Vol 33 ◽  
pp. 7651-7658 ◽  
Author(s):  
Hang Ma ◽  
Wolfgang Hönig ◽  
T. K. Satish Kumar ◽  
Nora Ayanian ◽  
Sven Koenig
Keyword(s):  
Path Planning ◽  
Search Algorithm ◽  
Single Agent ◽  
Pickup And Delivery ◽  
Combinatorial Search ◽  
Kinematic Constraints ◽  
Processing Step ◽  
Automated Warehouses ◽  
Online Setting ◽  
Multi Agent

The Multi-Agent Pickup and Delivery (MAPD) problem models applications where a large number of agents attend to a stream of incoming pickup-and-delivery tasks. Token Passing (TP) is a recent MAPD algorithm that is efficient and effective. We make TP even more efficient and effective by using a novel combinatorial search algorithm, called Safe Interval Path Planning with Reservation Table (SIPPwRT), for single-agent path planning. SIPPwRT uses an advanced data structure that allows for fast updates and lookups of the current paths of all agents in an online setting. The resulting MAPD algorithm TP-SIPPwRT takes kinematic constraints of real robots into account directly during planning, computes continuous agent movements with given velocities that work on non-holonomic robots rather than discrete agent movements with uniform velocity, and is complete for wellformed MAPD instances. We demonstrate its benefits for automated warehouses using both an agent simulator and a standard robot simulator. For example, we demonstrate that it can compute paths for hundreds of agents and thousands of tasks in seconds and is more efficient and effective than existing MAPD algorithms that use a post-processing step to adapt their paths to continuous agent movements with given velocities.

scholarly journals Multi-Agent Pathfinding with Continuous Time

2019 ◽  
Author(s):  
Anton Andreychuk ◽  
Konstantin Yakovlev ◽  
Dor Atzmon ◽  
Roni Stern
Keyword(s):  
Path Planning ◽  
Continuous Time ◽  
Single Agent ◽  
Optimal Solutions ◽  
Prior Work ◽  
Multiple Agents ◽  
Multi Agent ◽  
Pros And Cons ◽  
Planning Algorithms

Multi-Agent Pathfinding (MAPF) is the problem of finding paths for multiple agents such that every agent reaches its goal and the agents do not collide. Most prior work on MAPF were on grids, assumed agents' actions have uniform duration, and that time is discretized into timesteps. In this work, we propose a MAPF algorithm that do not assume any of these assumptions, is complete, and provides provably optimal solutions. This algorithm is based on a novel combination of Safe Interval Path Planning (SIPP), a continuous time single agent planning algorithms, and Conflict-Based Search (CBS). We analyze this algorithm, discuss its pros and cons, and evaluate it experimentally on several standard benchmarks.

scholarly journals Integrated Task Assignment and Path Planning for Capacitated Multi-Agent Pickup and Delivery

2021 ◽  
pp. 1-1
Author(s):  
Zhe Chen ◽  
Javier Alonso-Mora ◽  
Xiaoshan Bai ◽  
Daniel Damir Harabor ◽  
Peter James Stuckey
Keyword(s):  
Path Planning ◽  
Task Assignment ◽  
Pickup And Delivery ◽  
Multi Agent

Path planning towards non-compulsory multiple targets using TWIN-RRT*

2016 ◽  
Vol 43 (4) ◽  
pp. 370-379 ◽  
Author(s):  
Nino Pereira ◽  
A.Fernando Ribeiro ◽  
Gil Lopes ◽  
Jorge Lino
Keyword(s):  
Path Planning ◽  
Closed Loop ◽  
Low Cost ◽  
Single Agent ◽  
Computational Cost ◽  
Practical Implementation ◽  
Planning Problem ◽  
Multiple Targets ◽  
Kinematic Constraints ◽  
Content Type

Purpose The purpose of this paper is to characterise the TWIN-RRT* algorithm which solves a motion planning problem in which an agent has multiple possible targets where none of them is compulsory and retrieves feasible, “low cost”, asymptotically optimal and probabilistically complete paths. The TWIN-RRT* algorithm solves path planning problems for both holonomic and non-holonomic robots with or without kinematic constraints in a 2D environment. Design/methodology/approach It was designed to work equally well with higher degree of freedom agents in different applications. It provides a practical implementation of feasible and fast planning, namely where a closed loop is required. Initial and final configurations are allowed to be exactly the same. Findings The TWIN-RRT* algorithm computes an efficient path for a single agent towards multiple targets where none of them is mandatory. It inherits the low computational cost, probabilistic completeness and asymptotical optimality from RRT*. Research limitations/implications It uses efficiency as cost function, which can be adjusted to the requirements of any given application. TWIN-RRT also shows compliance with kinematic constraints. Practical implications The practical application where this work has been used consists of an autonomous mobile robot that picks up golf balls in a driving range. The multiple targets are the golf balls and the optimum path is a requirement to reduce the time and energy to refill as quickly as possible the balls dispensing machine. Originality/value The new random sampling algorithm – TWIN-RRT* – is able to generate feasible efficient paths towards multiple targets retrieving closed-loop paths starting and finishing at the same configuration.

A Comparison among Multi-Agent Stochastic Optimization Algorithms for State Feedback Regulator Design of a Twin Rotor MIMO System

2016 ◽  
pp. 409-448 ◽  
Author(s):  
Kaushik Das Sharma
Keyword(s):  
Stochastic Optimization ◽  
State Feedback ◽  
Search Algorithm ◽  
Mimo System ◽  
Single Agent ◽  
The State ◽  
Multi Agent ◽  
Twin Rotor Mimo System ◽  
Twin Rotor ◽  
Feedback Regulator

Multi-agent optimization or population based search techniques are increasingly become popular compared to its single-agent counterpart. The single-agent gradient based search algorithms are very prone to be trapped in local optima and also the computational cost is higher. Multi-Agent Stochastic Optimization (MASO) algorithms are much powerful to overcome most of the drawbacks. This chapter presents a comparison of five MASO algorithms, namely genetic algorithm, particle swarm optimization, differential evolution, harmony search algorithm, and gravitational search algorithm. These MASO algorithms are utilized here to design the state feedback regulator for a Twin Rotor MIMO System (TRMS). TRMS is a multi-modal process and the design of its state feedback regulator is quite difficult using conventional methods available. MASO algorithms are typically suitable for such complex process optimizations. The performances of different MASO algorithms are presented and discussed in light of designing the state regulator for TRMS.

scholarly journals Distributed Heuristic Forward Search for Multi-agent Planning

2014 ◽  
Vol 51 ◽  
pp. 293-332 ◽  
Author(s):  
R. Nissim ◽  
R. Brafman
Keyword(s):  
Partial Order ◽  
Private Information ◽  
Search Algorithm ◽  
Single Agent ◽  
Privacy Preserving ◽  
Forward Search ◽  
Planning Techniques ◽  
State Expansion ◽  
Multi Agent ◽  
Multi Agent Planning

This paper deals with the problem of classical planning for multiple cooperative agents who have private information about their local state and capabilities they do not want to reveal. Two main approaches have recently been proposed to solve this type of problem -- one is based on reduction to distributed constraint satisfaction, and the other on partial-order planning techniques. In classical single-agent planning, constraint-based and partial-order planning techniques are currently dominated by heuristic forward search. The question arises whether it is possible to formulate a distributed heuristic forward search algorithm for privacy-preserving classical multi-agent planning. Our work provides a positive answer to this question in the form of a general approach to distributed state-space search in which each agent performs only the part of the state expansion relevant to it. The resulting algorithms are simple and efficient -- outperforming previous algorithms by orders of magnitude -- while offering similar flexibility to that of forward-search algorithms for single-agent planning. Furthermore, one particular variant of our general approach yields a distributed version of the A* algorithm that is the first cost-optimal distributed algorithm for privacy-preserving planning.

scholarly journals Multi-Agent Reinforcement Learning: A Review of Challenges and Applications

2021 ◽  
Vol 11 (11) ◽  
pp. 4948
Author(s):  
Lorenzo Canese ◽  
Gian Carlo Cardarilli ◽  
Luca Di Di Nunzio ◽  
Rocco Fazzolari ◽  
Daniele Giardino ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Mathematical Models ◽  
Learning Algorithms ◽  
Single Agent ◽  
Critical Issues ◽  
Multi Agent ◽  
Pros And Cons ◽  
Application Fields

In this review, we present an analysis of the most used multi-agent reinforcement learning algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the most critical issues that must be taken into account in their extension to multi-agent scenarios. The analyzed algorithms were grouped according to their features. We present a detailed taxonomy of the main multi-agent approaches proposed in the literature, focusing on their related mathematical models. For each algorithm, we describe the possible application fields, while pointing out its pros and cons. The described multi-agent algorithms are compared in terms of the most important characteristics for multi-agent reinforcement learning applications—namely, nonstationarity, scalability, and observability. We also describe the most common benchmark environments used to evaluate the performances of the considered methods.

Dynamic Path Planning Algorithm Based on Chaos Genetic Vehicle Navigation

2011 ◽  
Vol 142 ◽  
pp. 12-15
Author(s):  
Ping Feng
Keyword(s):  
Path Planning ◽  
Search Algorithm ◽  
Traffic Condition ◽  
Planning Time ◽  
Dynamic Path Planning ◽  
Short Period ◽  
Planning Algorithm ◽  
Dynamic Path ◽  
Path Planning Algorithm ◽  
Chaos Genetic Algorithm

The paper puts forward the dynamic path planning algorithm based on improving chaos genetic algorithm by using genetic algorithms and chaos search algorithm. In the practice of navigation, the algorithm can compute at the best path to meet the needs of the navigation in such a short period of planning time. Furthermore,this algorithm can replan a optimum path of the rest paths after the traffic condition in the sudden.

scholarly journals Scalable information-theoretic path planning for a rover-helicopter team in uncertain environments

2021 ◽  
Vol 18 (2) ◽  
pp. 172988142199958
Author(s):  
Larkin Folsom ◽  
Masahiro Ono ◽  
Kyohei Otsu ◽  
Hyoshin Park
Keyword(s):  
Path Planning ◽  
Lower Risk ◽  
Information Gain ◽  
Probability Distributions ◽  
Single Agent ◽  
Uncertain Environments ◽  
Information Theoretic ◽  
Highly Correlated ◽  
Naive Approach

Mission-critical exploration of uncertain environments requires reliable and robust mechanisms for achieving information gain. Typical measures of information gain such as Shannon entropy and KL divergence are unable to distinguish between different bimodal probability distributions or introduce bias toward one mode of a bimodal probability distribution. The use of a standard deviation (SD) metric reduces bias while retaining the ability to distinguish between higher and lower risk distributions. Areas of high SD can be safely explored through observation with an autonomous Mars Helicopter allowing safer and faster path plans for ground-based rovers. First, this study presents a single-agent information-theoretic utility-based path planning method for a highly correlated uncertain environment. Then, an information-theoretic two-stage multiagent rapidly exploring random tree framework is presented, which guides Mars helicopter through regions of high SD to reduce uncertainty for the rover. In a Monte Carlo simulation, we compare our information-theoretic framework with a rover-only approach and a naive approach, in which the helicopter scouts ahead of the rover along its planned path. Finally, the model is demonstrated in a case study on the Jezero region of Mars. Results show that the information-theoretic helicopter improves the travel time for the rover on average when compared with the rover alone or with the helicopter scouting ahead along the rover’s initially planned route.

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