Computing the Closest Approach Distance of Two Ellipsoids

This paper presents two practical methods for computing the closest approach distance of two ellipsoids in their inter-center direction. The closest approach distance is crucial for collision handling in the dynamic simulation of rigid and deformable bodies approximated with ellipsoids. To find the closest approach distance, we formulate a set of equations for two ellipsoids contacting each other externally in terms of the inter-center distance, contact point, and normal vector. The equations are solved robustly and efficiently using a hybrid of the fixed-point iteration method and bisection method with root bracketing, and a hybrid of Newton’s method and the bisection method. In addition to a stopping criterion expressed with the progress of the solution, we introduce a novel criterion expressed in terms of the error in distance. This criterion can be effectively employed in real-time applications such as computer games by allowing an unnoticeable error. Experimental results demonstrate the robustness and efficiency of the proposed methods in various experiments.

A Hybrid based Distributed Slot Scheduling Approach for WSN MAC

In Wireless Sensor Networks(WSNs), collision handling during transmission of data is an important challenge. MAC protocol plays a vital role in handling those collisions. Among different types of MAC protocols, schedule based MAC protocol is one where a valid schedule is prepared to handle the collision. The existing schedule based MAC protocols focus on preparing either a feasible schedule or an optimal schedule. In order to satisfy both feasibility as well as optimality feature, in this paper, we proposed a hybrid approach for slot scheduling that prepares a feasible schedule in a distributed manner and at the same time reduces the number of feasible schedule to achieve optimality. In this paper, we named this algorithm as Distributed hybrid Slot Scheduling(DHSS) algorithm. The proposed DHSS algorithm initially prepares a feasible schedule which is furthertuned in quick time to prepare a valid schedule with a reducednumber of slots. The reduction of the number of slots in theschedule improves the efficiency of data transmission in termsof latency. The simulation results show that the DHSS algorithmoutperforms RD-TDMA with respect to both the number of slotsallotted for a feasible schedule as well as the data transmissionlatency.

Flow: Deep Reinforcement Learning for Control in SUMO

We detail the motivation and design decisions underpinning Flow, a computational framework integrating SUMO with the deep reinforcement learning libraries rllab and RLlib, allowing researchers to apply deep reinforcement learning (RL) methods to traffic scenarios, and permitting vehicle and infrastructure control in highly varied traffic envi- ronments. Users of Flow can rapidly design a wide variety of traffic scenarios in SUMO, enabling the development of controllers for autonomous vehicles and intelligent infrastruc- ture across a broad range of settings.Flow facilitates the use of policy optimization algorithms to train controllers that can optimize for highly customizable traffic metrics, such as traffic flow or system-wide average velocity. Training reinforcement learning agents using such methods requires a massive amount of data, thus simulator reliability and scalability were major challenges in the development of Flow. A contribution of this work is a variety of practical techniques for overcoming such challenges with SUMO, including parallelizing policy rollouts, smart exception and collision handling, and leveraging subscriptions to reduce computational overhead.To demonstrate the resulting performance and reliability of Flow, we introduce the canonical single-lane ring road benchmark and briefly discuss prior work regarding that task. We then pose a more complex and challenging multi-lane setting and present a trained controller for a single vehicle that stabilizes the system. Flow is an open-source tool and available online at https://github.com/cathywu/flow.