This paper discusses the workload distribution problem in an Active Sensor Network, which integrates multiple sensor network-friendly mobile robots into a traditional sensor network and introduces adaptivity, self-healing, responsiveness and longer lifetime to the network. In order to distribute the workload among the service robots, first, a Service Set Partition (SSP) algorithm is developed to disperse the robots so that each robot can attend one subset of the sensors. Second, to service multiple sensors simultaneously, a Contract Network Protocol (CNP) is adopted to assign tasks to multiple service robots. Through CNP, certain performances such as energy efficiency and service time can be optimized. The proposed algorithms are verified through simulations.

For autonomous driving research, using a scaled vehicle platform is a viable alternative compared to a full-scale vehicle. However, using embedded solutions such as small robotic platforms with differential driving or radio-controlled (RC) car-based platforms can be limiting on, for example, sensor package restrictions or computing challenges. Furthermore, for a given controller, specialized expertise and abilities are necessary. To address such problems, this paper proposes a feasible solution, the Ridon vehicle, which is a spacious ride-on automobile with high-driving electric power and a custom-designed drive-by-wire system powered by a full-scale machine-learning-ready computer. The major objective of this paper is to provide a thorough and appropriate method for constructing a cost-effective platform with a drive-by-wire system and sensor packages so that machine-learning-based algorithms can be tested and deployed on a scaled vehicle. The proposed platform employs a modular and hierarchical software architecture, with microcontroller programs handling the low-level motor controls and a graphics processing unit (GPU)-powered laptop computer processing the higher and more sophisticated algorithms. The Ridon vehicle platform is validated by employing it in a deep-learning-based behavioral cloning study. The suggested platform’s affordability and adaptability would benefit broader research and the education community.

Spray forming is a new manufacturing process. The automated tool planning for this process is a challenging problem, especially for geometry-complicated parts consisting of multiple freeform surfaces. We have developed a tool path planning system which can automatically generate tool plans. This paper discusses the path integration problem, i.e, how to connect the paths from different surface patches so that the overall path length is minimized. An ant colony optimization (ACO)-based path integration algorithm is developed to solve this problem. Experimental tests are carried out on sample data as well as real world automotive body parts. The results proved to he better (by up to 10%) compared with those of a genetic algorithm (GA)-based solution we developed before

This paper illustrates the MIR (Mobile Intelligent Robotics) Vehicle: a feasible option of transforming an electric ride-on-car into a modular Graphics Processing Unit (GPU) powered autonomous platform equipped with the capability that supports test and deployment of various intelligent autonomous vehicles algorithms. To use a platform for research, two components must be provided: perception and control. The sensors such as incremental encoders, an Inertial Measurement Unit (IMU), a camera, and a LIght Detection And Ranging (LIDAR) must be able to be installed on the platform to add the capability of environmental perception. A microcontroller-powered control box is designed to properly respond to the environmental changes by regulating drive and steering motors. This drive-by-wire capability is controlled by a GPU powered laptop computer where high-level perception algorithms are processed and complex actions are generated by various methods including behavior cloning using deep neural networks. The main goal of this paper is to provide an adequate and comprehensive approach for fabricating a cost-effective platform that would contribute to the research quality from the wider community. The proposed platform is to use a modular and hierarchical software architecture where the lower and simpler motor controls are taken care of by microcontroller programs, and the higher and complex algorithms are processed by a GPU powered laptop computer. The platform uses the Robot Operating System (ROS) as middleware to maintain the modularity of the perceptions and decision-making modules. It is expected that the level three and above autonomous vehicle systems and Advanced Driver Assistance Systems (ADAS) can be tested on and deployed to the platform with a decent real-time system behavior due to the capabilities and affordability of the proposed platform.