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

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 well-formed 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.Comment: AAAI 201

Prioritized data synchronization with applications

We are interested on the problem of synchronizing data on two distinct devices with differed priorities using minimum communication. A variety of distributed sys- tems require communication efficient and prioritized synchronization, for example, where the bandwidth is limited or certain information is more time sensitive than others. Our particular approach, P-CPI, involving the interactive synchronization of prioritized data, is efficient both in communication and computation. This protocol sports some desirable features, including (i) communication and computational com- plexity primarily tied to the number of di erences between the hosts rather than the amount of the data overall and (ii) a memoryless fast restart after interruption. We provide a novel analysis of this protocol, with proved high-probability performance bound and fast-restart in logarithmic time. We also provide an empirical model for predicting the probability of complete synchronization as a function of time and symmetric differences. We then consider two applications of our core algorithm. The first is a string reconciliation protocol, for which we propose a novel algorithm with online time com- plexity that is linear in the size of the string. Our experimental results show that our string reconciliation protocol can potentially outperform existing synchroniza- tion tools such like rsync in some cases. We also look into the benefit brought by our algorithm to delay-tolerant networks(DTNs). We propose an optimized DTN routing protocol with P-CPI implemented as middleware. As a proof of concept, we demonstrate improved delivery rate, reduced metadata and reduced average delay

Maximizing Routing Throughput with Applications to Delay Tolerant Networks

abstract: Many applications require efficient data routing and dissemination in Delay Tolerant Networks (DTNs) in order to maximize the throughput of data in the network, such as providing healthcare to remote communities, and spreading related information in Mobile Social Networks (MSNs). In this thesis, the feasibility of using boats in the Amazon Delta Riverine region as data mule nodes is investigated and a robust data routing algorithm based on a fountain code approach is designed to ensure fast and timely data delivery considering unpredictable boat delays, break-downs, and high transmission failures. Then, the scenario of providing healthcare in Amazon Delta Region is extended to a general All-or-Nothing (Splittable) Multicommodity Flow (ANF) problem and a polynomial time constant approximation algorithm is designed for the maximum throughput routing problem based on a randomized rounding scheme with applications to DTNs. In an MSN, message content is closely related to users’ preferences, and can be used to significantly impact the performance of data dissemination. An interest- and content-based algorithm is developed where the contents of the messages, along with the network structural information are taken into consideration when making message relay decisions in order to maximize data throughput in an MSN. Extensive experiments show the effectiveness of the above proposed data dissemination algorithm by comparing it with state-of-the-art techniques.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Hybrid Vehicle-drone Routing Problem For Pick-up And Delivery Services Mathematical Formulation And Solution Methodology

The fast growth of online retail and associated increasing demand for same-day delivery have pushed online retail and delivery companies to develop new paradigms to provide faster, cheaper, and greener delivery services. Considering drones’ recent technological advancements over the past decade, they are increasingly ready to replace conventional truck-based delivery services, especially for the last mile of the trip. Drones have significantly improved in terms of their travel ranges, load-carrying capacity, positioning accuracy, durability, and battery charging rates. Substituting delivery vehicles with drones could result in $50M of annual cost savings for major U.S. service providers. The first objective of this research is to develop a mathematical formulation and efficient solution methodology for the hybrid vehicle-drone routing problem (HVDRP) for pick-up and delivery services. The problem is formulated as a mixed-integer program, which minimizes the vehicle and drone routing cost to serve all customers. The formulation captures the vehicle-drone routing interactions during the drone dispatching and collection processes and accounts for drone operation constraints related to flight range and load carrying capacity limitations. A novel solution methodology is developed which extends the classic Clarke and Wright algorithm to solve the HVDRP. The performance of the developed heuristic is benchmarked against two other heuristics, namely, the vehicle-driven routing heuristic and the drone-driven routing heuristic. Anticipating the potential risk of using drones for delivery services, aviation authorities in the U.S. and abroad have mandated necessary regulatory rules to ensure safe operations. The U.S. Federal Aviation Administration (FAA) is examining the feasibility of drone flights in restricted airspace for product delivery, requiring drones to fly at or below 400-feet and to stay within the pilot’s line of sight (LS). Therefore, a second objective of this research is considered to develop a modeling framework for the integrated vehicle-drone routing problem for pick-up and delivery services considering the regulatory rule requiring all drone flights to stay within the pilot’s line of sight (LS). A mixed integer program (MIP) and an efficient solution methodology were developed for the problem. The solution determines the optimal vehicle and drone routes to serve all customers without violating the LS rule such that the total routing cost of the integrated system is minimized. Two different heuristics are developed to solve the problem, which extends the Clarke and Wright Algorithm to cover the multimodality aspects of the problem and to satisfy the LS rule. The first heuristic implements a comprehensive multimodal cost saving search to construct the most efficient integrated vehicle-drone routes. The second heuristic is a light version of the first heuristic as it adopts a vehicle-driven cost saving search. Several experiments are conducted to examine the performance of the developed methodologies using hypothetical grid networks of different sizes. The capability of the developed model in answering a wide variety of questions related to the planning of the vehicle-drone delivery system is illustrated. In addition, a case study is presented in which the developed methodology is applied to provide pick-up and delivery services in the downtown area of the City of Dallas. The results show that mandating the LS rule could double the overall system operation cost especially in dense urban areas with LS obstructions

Packet Selection and Scheduling for Multipath Video Streaming

This paper addresses the problem of choosing the best streaming policy for distortion optimal multipath video delivery, under delay constraints. The streaming policy consists in a joint selection of the video packets to be transmitted, as well as their sending time, and the transmission path. A simple streaming model is introduced, which takes into account the video packet importance, and the dependencies among packets, and allows to compute the quality perceived by the receiver, as a function of the streaming policy. We derive an optimization problem based on the video abstraction model, under the assumption that the server knows, or can predict the state of the network. A detailed analysis of the timing constraints in multipath video streaming provides helpful insights that lead to an efficient algorithm to solve the NP-hard streaming policy optimization problem. We eventually propose a fast heuristic-based algorithm, that still provides close to optimal performance. Thanks to its limited complexity, this novel algorithm is finally demonstrated in live streaming scenarios, where it only induces a negligible distortion penalty compared to an optimal strategy. Simulation results finally show that the proposed scheduling solutions perform better than common scheduling algorithms, and represent very efficient multipath streaming strategies for both stored and live video services

Computational Intelligence Inspired Data Delivery for Vehicle-to-Roadside Communications

We propose a vehicle-to-roadside communication protocol based on distributed clustering where a coalitional game approach is used to stimulate the vehicles to join a cluster, and a fuzzy logic algorithm is employed to generate stable clusters by considering multiple metrics of vehicle velocity, moving pattern, and signal qualities between vehicles. A reinforcement learning algorithm with game theory based reward allocation is employed to guide each vehicle to select the route that can maximize the whole network performance. The protocol is integrated with a multi-hop data delivery virtualization scheme that works on the top of the transport layer and provides high performance for multi-hop end-to-end data transmissions. We conduct realistic computer simulations to show the performance advantage of the protocol over other approaches

Distortion Optimized Multipath Video Streaming

This paper addresses the problem of choosing the best streaming policy for distortion optimal multipath video delivery, under delay constraints. The streaming policy consists in a joint selection of the video packets to be transmitted, as well as their sending time, and the transmission path. A simple streaming model is introduced, which takes into account the video packet importance, and the dependencies among packets, and allows to compute the quality perceived by the receiver, as a function of the streaming policy. We derive an optimization problem based on the video abstraction model, under the assumption that the server knows the state of the network. A detailed analysis of the timing constraints in multipath video streaming provides helpful insights that lead to an efficient algorithm to solve the NP-hard policy optimization problem. We eventually propose a fast heuristic-based algorithm, that still provides close to optimal performance. Thanks to its limited complexity, this novel algorithm is finally implemented in live streaming scenarios, where it only induces a negligible distortion penalty compared to the optimal strategy. Simulation results finally show that the proposed scheduling solutions perform better than common scheduling algorithms, and represent very efficient strategies for both stored and live video streaming scenarios

Cross-Layer Optimization of Fast Video Delivery in Cache-Enabled Relaying Networks

This paper investigates the cross-layer optimization of fast video delivery and caching for minimization of the overall video delivery time in a two-hop relaying network. The half-duplex relay nodes are equipped with both a cache and a buffer which facilitate joint scheduling of fetching and delivery to exploit the channel diversity for improving the overall delivery performance. The fast delivery control is formulated as a two-stage functional non-convex optimization problem. By exploiting the underlying convex and quasi-convex structures, the problem can be solved exactly and efficiently by the developed algorithm. Simulation results show that significant caching and buffering gains can be achieved with the proposed framework, which translates into a reduction of the overall video delivery time. Besides, a trade-off between caching and buffering gains is unveiled.Comment: 7 pages, 4 figures; accepted for presentation at IEEE Globecom, San Diego, CA, Dec. 201