A framework for proving the self-organization of dynamic systems

This paper aims at providing a rigorous definition of self- organization, one of the most desired properties for dynamic systems (e.g., peer-to-peer systems, sensor networks, cooperative robotics, or ad-hoc networks). We characterize different classes of self-organization through liveness and safety properties that both capture information re- garding the system entropy. We illustrate these classes through study cases. The first ones are two representative P2P overlays (CAN and Pas- try) and the others are specific implementations of \Omega (the leader oracle) and one-shot query abstractions for dynamic settings. Our study aims at understanding the limits and respective power of existing self-organized protocols and lays the basis of designing robust algorithm for dynamic systems

Autonomous Sweet Pepper Harvesting for Protected Cropping Systems

In this letter, we present a new robotic harvester (Harvey) that can autonomously harvest sweet pepper in protected cropping environments. Our approach combines effective vision algorithms with a novel end-effector design to enable successful harvesting of sweet peppers. Initial field trials in protected cropping environments, with two cultivar, demonstrate the efficacy of this approach achieving a 46% success rate for unmodified crop, and 58% for modified crop. Furthermore, for the more favourable cultivar we were also able to detach 90% of sweet peppers, indicating that improvements in the grasping success rate would result in greatly improved harvesting performance

Adaptive sampling for spatial prediction in environmental monitoring using wireless sensor networks: A review

© 2018 IEEE. The paper presents a review of the spatial prediction problem in the environmental monitoring applications by utilizing stationary and mobile robotic wireless sensor networks. First, the problem of selecting the best subset of stationary wireless sensors monitoring environmental phenomena in terms of sensing quality is surveyed. Then, predictive inference approaches and sampling algorithms for mobile sensing agents to optimally observe spatially physical processes in the existing works are analysed

An Effective Approach for Recovering From Simultaneous Node Failures in Wireless Sensor Networks

In wireless sensor - actor networks, sensors probe their surroundings and forward their data to actor nodes. Actors collaboratively respond to achieve predefined application mission. Since actors have to coordinate their operation, it is nec essary to maintain a stron gly connected network topology at all times. Failure of one or multiple actors may partition the inter - actor network into disjoint segments, and thus hinders the network operation. Autonomous detection and rapid recovery procedures ar e highly desirable in such a case . One of the effective recovery methodologies is to autonomously reposition a subset of the actor nodes to restore connectivity. Contemporary recovery schemes either impose high node relocation overhead or extend some of th e inter - actor data pat hs. This paper overcomes these shortcomings and presents extended version of DCR named RAM, to handle one possible case of a multi - actor failure with Least - Disruptive topology Repair (LeDiR) algorithm for minimal topological changes . Upon failure detection , the backup actor initiates a recovery process that relocates the least num ber of nodes

HUMAN FOLLOWING ON ROS FRAMEWORK A MOBILE ROBOT

Service mobile robot is playing a more critical role in today's society as more people such as a disabled person or the elderly are in need of mobile robot assistance. An autonomous person following ability shows great importance to the overall role of service mobile robot in assisting human. The objective of this paper focuses on developing a robot follow a person. The robot is equipped with the necessary sensors such as a Microsoft Kinect sensor and a Hokuyo laser sensor. Four suitable tracking methods are introduced in this project which is implemented and tested on the person following algorithm. The tracking methods implemented are face detection, leg detection, color detection and person blob detection. All of the algorithms implementations in this project is performed using Robot Operating System (ROS). The result showed that the mobile robot could track and follow the target person based on the person movement.

Sampling-Based Exploration Strategies for Mobile Robot Autonomy

A novel, sampling-based exploration strategy is introduced for Unmanned Ground Vehicles (UGV) to efficiently map large GPS-deprived underground environments. It is compared to state-of-the-art approaches and performs on a similar level, while it is not designed for a specific robot or sensor configuration like the other approaches. The introduced exploration strategy, which is called Random-Sampling-Based Next-Best View Exploration (RNE), uses a Rapidly-exploring Random Graph (RRG) to find possible view points in an area around the robot. They are compared with a computation-efficient Sparse Ray Polling (SRP) in a voxel grid to find the next-best view for the exploration. Each node in the exploration graph built with RRG is evaluated regarding the ability of the UGV to traverse it, which is derived from an occupancy grid map. It is also used to create a topology-based graph where nodes are placed centrally to reduce the risk of collisions and increase the amount of observable space. Nodes that fall outside the local exploration area are stored in a global graph and are connected with a Traveling Salesman Problem solver to explore them later