Dynamic Vehicle Routing for Robotic Systems

Abstract

Abstract-Recent years have witnessed great advancements in the science and technology of autonomy, robotics and networking. This paper surveys recent concepts and algorithms for dynamic vehicle routing (DVR), that is, for the automatic planning of optimal multi-vehicle routes to perform tasks that are generated over time by an exogenous process. We consider a rich variety of scenarios relevant for robotic applications. We begin by reviewing the basic DVR problem: demands for service arrive at random locations at random times and a vehicle travels to provide on-site service while minimizing the expected wait time of the demands. Next, we treat different multi-vehicle scenarios based on different models for demands (e.g., demands with different priority levels and impatient demands), vehicles (e.g., motion constraints, communication and sensing capabilities), and tasks. The performance criterion used in these scenarios is either the expected wait time of the demands or the fraction of demands serviced successfully. In each specific DVR scenario, we adopt a rigorous technical approach that relies upon methods from queueing theory, combinatorial optimization and stochastic geometry. First, we establish fundamental limits on the achievable performance, including limits on stability and quality of service. Second, we design algorithms, and provide provable guarantees on their performance with respect to the fundamental limits.

Distributed Online Patrolling with Multi-agent Teams of Sentinels and Searchers

“…Of closest relevance to this paper are formulations of the dynamic vehicle routing problem in relation to algorithmic queuing theory, such as in [3,8], in which events requiring servicing appear in the environment stochastically, such as random arrivals of intruders in a protected area, requiring one or more agents to prioritize and visit these locations in an online manner. Alternate formulations consider patrol sequences under different assumptions for intruder arrivals, such as cases where intrusion sites are determined according to known probability distributions or by assuming adversarial intruders requiring game-theoretic design of patrols [9].…”

“…We consider a situation where the environment is constantly under the threat of attacks which can randomly occur at any time and at any place. We adopt a common assumption from patrolling literature (see, for example, [3]) according to which arrival times for attacks obey a Poisson distribution while their spatial location is determined according to some discrete probability distribution over G . More formally, the inter-arrival time in the whole environment is modeled by an exponential variable with parameter λ while the specific cell c is chosen with a probability proportional to the value l(c), i.e., once an attack arrived in the environment, the probability that it will be located at cell c is…”

“…1 as a function of the various parameters characterizing the system. Inspired by [3], in this section we answer this question relying on queueing theory. The relevant parameters are:…”

Co-Optimization of Communication and Motion Planning of a Robotic Operation under Resource Constraints and in Fading Environments

“…3 As mentioned in Section II-B, the required target BER is part of K: K = −1.5/ ln(5p b,th ). In the formulation of (1), we assume that the robot travels at a constant speed along each sub-trajectory.…”

“…In recent years, considerable progress has been made in the area of mobile sensor networks [2] and networked robotic systems [3]- [6]. In order to truly realize the full potential of these systems, an integrative approach to both communication and navigation issues is needed.…”