This paper presents a unified approach to realize versatile distributed maneuvering with generalized formations. Specifically, we decompose the robots' maneuvers into two independent components, i.e., interception and enclosing, which are parameterized by two independent virtual coordinates. Treating these two virtual coordinates as dimensions of an abstract manifold, we derive the corresponding singularity-free guiding vector field (GVF), which, along with a distributed coordination mechanism based on the consensus theory, guides robots to achieve various motions (i.e., versatile maneuvering), including (a) formation tracking, (b) target enclosing, and (c) circumnavigation. Additional motion parameters can generate more complex cooperative robot motions. Based on GVFs, we design a controller for a nonholonomic robot model. Besides the theoretical results, extensive simulations and experiments are performed to validate the effectiveness of the approach.

Imitation learning (IL) enables robots to acquire skills quickly by transferring expert knowledge, which is widely adopted in reinforcement learning (RL) to initialize exploration. However, in long-horizon motion planning tasks, a challenging problem in deploying IL and RL methods is how to generate and collect massive, broadly distributed data such that these methods can generalize effectively. In this work, we solve this problem using our proposed approach called self-imitation learning by planning (SILP), where demonstration data are collected automatically by planning on the visited states from the current policy. SILP is inspired by the observation that successfully visited states in the early reinforcement learning stage are collision-free nodes in the graph-search based motion planner, so we can plan and relabel robot's own trials as demonstrations for policy learning. Due to these self-generated demonstrations, we relieve the human operator from the laborious data preparation process required by IL and RL methods in solving complex motion planning tasks. The evaluation results show that our SILP method achieves higher success rates and enhances sample efficiency compared to selected baselines, and the policy learned in simulation performs well in a real-world placement task with changing goals and obstacles.

Reinforcement learning has shown great promise in the training of robot behavior due to the sequential decision making characteristics. However, the required enormous amount of interactive and informative training data provides the major stumbling block for progress. In this study, we focus on accelerating reinforcement learning (RL) training and improving the performance of multi-goal reaching tasks. Specifically, we propose a precision-based continuous curriculum learning (PCCL) method in which the requirements are gradually adjusted during the training process, instead of fixing the parameter in a static schedule. To this end, we explore various continuous curriculum strategies for controlling a training process. This approach is tested using a Universal Robot 5e in both simulation and real-world multi-goal reach experiments. Experimental results support the hypothesis that a static training schedule is suboptimal, and using an appropriate decay function for curriculum learning provides superior results in a faster way.

It has been shown that developing nano-satellites is a good platform to motivate and educate students in Science, Technology, Engineering and Mathematics (STEM) disciplines. In this paper, the authors have explained an innovative project management method, called major-project-review, and its implementations to supervise different batches of multidisciplinary final year engineering students to develop a CubeSat as their Final Year Projects (FYPs) in three to four years. The major-project-review approach has been successfully verified in our first CubeSat project, Galassia, and is continuously used to develop our second CubeSat, Galassia2. From the success of Galassia and student feedbacks, the method has been proven to be very efficient to control and monitor the CubeSat development progress, and to motivate and educate engineering students. With our continuous implementations in Galassia2 project, we hope to collect more experiences and data to further optimize the approach.

For dynamic multi-objective vehicle routing problems, the waiting time of vehicle, the number of serving vehicles, and the total distance of routes were normally considered as the optimization objectives. Except for the above objectives, fuel consumption that leads to the environmental pollution and energy consumption was focused on in this paper. Considering the vehicles' load and the driving distance, a corresponding carbon emission model was built and set as an optimization objective. Dynamic multi-objective vehicle routing problems with hard time windows and randomly appeared dynamic customers, subsequently, were modeled. In existing planning methods, when the new service demand came up, global vehicle routing optimization method was triggered to find the optimal routes for non-served customers, which was time-consuming. Therefore, a robust dynamic multi-objective vehicle routing method with two-phase is proposed . Three highlights of the novel method are: (i) After finding optimal robust virtual routes for all customers by adopting multi-objective particle swarm optimization in the first phase, static vehicle routes for static customers are formed by removing all dynamic customers from robust virtual routes in next phase. (ii) The dynamically appeared customers append to be served according to their service time and the vehicles' statues. Global vehicle routing optimization is triggered only when no suitable locations can be found for dynamic customers. (iii) A metric measuring the algorithms robustness is given. The statistical results indicated that the routes obtained by the proposed method have better stability and robustness, but may be sub-optimum. Moreover, time-consuming global vehicle routing optimization is avoided as dynamic customers appear.

Facial expression recognition utilizes collection of information from characteristic actions to analyze emotions and mental states of a person. It has emerged as the pivotal research topics in areas such as human computer interaction, sentimental analysis and synthetic face animation over the last years. This paper proposes an approach for facial expression by discovering associations between visual feature and Local Binary Pattern (LBP). Unlike many previous studies, the proposed approach automatically tracks the facial area and segments face into meaningful areas based on description of Local Binary Pattern. And then it accumulates the probabilities throughout the frames from video data to capture the temporal characteristics of facial expressions by analyzing facial expressions. Through the proposed approach, the temporal variation of facial expression can be quantified in individual areas. Thus, the recognition process of facial expression tends to be more comprehensible without sacrificing results of recognition. The empirical evaluation results of the approach are realized using video data which is collected from 10 volunteers. The results demonstrated that the proposed approach can effectively segment face into specific area and recognize facial expression.