Recent years have witnessed tremendous progress of intelligent robots brought about by mimicking human intelligence. However, current robots are still far from being able to handle multiple tasks in a dynamic environment as efficiently as humans. To cope with complexity and variability, further progress toward scalability and adaptability are essential for intelligent robots. Here, we report a brain-inspired robotic platform implemented by an unmanned bicycle that exhibits scalability of network scale, quantity and diversity to handle the changing needs of different scenarios. The platform adopts rich coding schemes and a trainable and scalable neural state machine, enabling flexible cooperation of hybrid networks. In addition, an embedded system is developed using a cross-paradigm neuromorphic chip to facilitate the implementation of diverse neural networks in spike or non-spike form. The platform achieved various real-time tasks concurrently in different real-world scenarios, providing a new pathway to enhance robots’ intelligence.

Bicycles and similar two-wheeled vehicles are a scientifically interesting category of means of transportation, whose simultaneous localization and mapping problem, in contrast to other ground vehicles, has yet to be examined in depth and fully resolved. In this article, we introduce for the first time a comprehensive theoretical framework for the application of simultaneous localization and mapping specifically for autonomous bicycles and akin vehicles, based on the kinematics and dynamics models of the bicycle and the specific effects they have on the motion/odometry and measurement models that are essential for the solution of the simultaneous localization and mapping problem. In addition, we present our laboratory’s first autonomous bicycle platform and its functional sensor system and sensor rotation pattern, specifically adjusted to the special characteristics of bicycles. Moreover, we investigate the effect of the bicycle frame roll, the main uncertainty factor of the bicycle simultaneous localization and mapping problem, on the overall simultaneous localization and mapping performance. The experimental results performed on our bicycle platform verify the potency of our proposed modeling and simultaneous localization and mapping application framework and provide further insight on future improvements for the two-wheeled vehicle simultaneous localization and mapping problem.

Due to the flexibility, availability, and scalability of cloud computing services, more and more users seek solutions via cloud computing techniques. A cloud computing platform often consists of a large number of infrastructures, and its energy consumption is a big problem. In this article, we study how to minimize the energy consumption of a cloud computing platform when handling some intermittent real-time tasks. Unlike previous works that abstract users’ submitted tasks as single computation jobs and process them using CPU, this work proposes using CPU and GPU to process intermittent real-time tasks that occur at irregular intervals and their released computation jobs must be completed within required time limits. The energy consumption minimization problem is formulated as an integer nonlinear programming problem that needs to decide on a task assignment plan and a specific resource allocation plan. To effectively solve this problem, we define a state that represents the optimal solution for a given set of tasks with a given amount of resources, as well as a value function that represents the value of a state. In this way, we derive a state-transition equation and develop a dynamic programming method to solve the problem. This method is also extended to handle tasks whose arrival time is dynamic and unpredictable. Experiments show that the proposed algorithm can effectively reduce energy consumption, while its computation time is quite low compared to some other greedy methods.

The planning of whole-body motion and step time for bipedal locomotion is constructed as a model predictive control (MPC) problem, in which a sequence of optimization problems needs to be solved online. While directly solving these problems is extremely time-consuming, we propose a predictive gait synthesizer to offer immediate solutions. Based on the full-dimensional model, a library of gaits with different speeds and periods is first constructed offline. Then the proposed gait synthesizer generates real-time gaits at 1 kHz by synthesizing the gait library based on the online prediction of centroidal dynamics. We prove that the constructed MPC problem can ensure the uniform ultimate boundedness (UUB) of the CoM states and show that our proposed gait synthesizer can provide feasible solutions to the MPC optimization problems. Simulation and experimental results on a bipedal robot with 8 degrees of freedom (DoF) are provided to show the performance and robustness of this approach.