This paper presents a new Multi-Objective Particle Swarm Optimization (MOPSO) approach to a Cooperative Multi-Robot Task Allocation (CMRTA) problem, where the robots have to minimize the total team cost and, additionally, balance their workloads. We formulate the CMRTA problem as a more complex variant of multiple Travelling Salesman Problems (mTSP) and, in particular, address how to minimize the total travel distance of the entire robot team, as well as how to minimize the highest travel distance of an individual robot. The proposed approach extends the standard single-objective Particle Swarm Optimization (PSO) to cope with the multiple objectives, and its novel feature lies in a Pareto front refinement strategy and a probability-based leader selection strategy. To validate the proposed approach, we first use three benchmark functions to evaluate the performance of finding the true Pareto fronts in comparison with four existing well-known algorithms in continuous spaces. Afterwards, we use six datasets to investigate the task allocation mechanisms in dealing with the CMRTA problem in discrete spaces.

Objective . A motor-imagery-based brain–computer interface (MI-BCI) provides an alternative way for people to interface with the outside world. However, the classification accuracy of MI signals remains challenging, especially with an increased number of classes and the presence of high variations with data from multiple individual people. This work investigates electroencephalogram (EEG) signal processing techniques, aiming to enhance the classification performance of multiple MI tasks in terms of tackling the challenges caused by the vast variety of subjects. Approach . This work introduces a novel method to extract discriminative features by combining the features of functional brain networks with two other feature extraction algorithms: common spatial pattern (CSP) and local characteristic-scale decomposition (LCD). After functional brain networks are established from the MI EEG signals of the subjects, the measures of degree in the binary networks are extracted as additional features and fused with features in the frequency and spatial domains extracted by the CSP and LCD algorithms. A real-time BCI robot control system is designed and implemented with the proposed method. Subjects can control the movement of the robot through four classes of MI tasks. Both the BCI competition IV dataset 2a and real-time data acquired in our designed system are used to validate the performance of the proposed method. Main results . As for the offline data experiment results, the average classification accuracy of the proposed method reaches 79.7%, outperforming the majority of popular algorithms. Experimental results with real-time data also prove the proposed method to be highly promising in its real-time performance. Significance . The experimental results show that our proposed method is robust in extracting discriminative brain activity features when performing different MI tasks, hence improving the classification accuracy in four-class MI tasks. The high classification accuracy and low computational demand show a considerable practicality for real-time rehabilitation systems.

Object Goal Navigation (ObjectNav) refers to an agent navigating to an object in an unseen environment, which is an ability often required in the accomplishment of complex tasks. Though it has drawn increasing attention from researchers in the Embodied AI community, there has not been a contemporary and comprehensive survey of ObjectNav. In this survey, we give an overview of this field by summarizing more than 70 recent papers. First, we give the preliminaries of the ObjectNav: the definition, the simulator, and the metrics. Then, we group the existing works into three categories: 1) end-to-end methods that directly map the observations to actions, 2) modular methods that consist of a mapping module, a policy module, and a path planning module, and 3) zero-shot methods that use zero-shot learning to do navigation. Finally, we summarize the performance of existing works and the main failure modes and discuss the challenges of ObjectNav. This survey would provide comprehensive information for researchers in this field to have a better understanding of ObjectNav. Note to Practitioners —This work was motivated by the increased interest in real-world applications of mobile robots. Object Goal Navigation (ObjectNav), which is an important task in these applications, requires an agent to find an object in an unseen environment. To accomplish that, the agent needs to be equipped with the capability to move in the environment, decide where to go, and recognize the object categories. So far, most works on ObjectNav have been done in a simulation environment. We present an overview of the existing works in ObjectNav and introduce them in three categories. Additionally, we analyze the current performance of ObjectNav and the challenges for future research. This paper provides researchers and practitioners with a comprehensive overview of the developed methods in ObjectNav, which can help them to have a good understanding of this task and develop suitable solutions for applications in the real world.

In the past decade, there is an increasing interest in the deployment of unmanned surface vehicles (USVs) for undertaking ocean missions in dynamic, complex maritime environments. The success of these missions largely relies on motion planning algorithms that can generate optimal navigational trajectories to guide a USV. Apart from minimising the distance of a path, when deployed a USVs’ motion planning algorithms also need to consider other constraints such as energy consumption, the affected of ocean currents as well as the fast collision avoidance capability. In this paper, we propose a new algorithm named anisotropic GPMP2 to revolutionise motion planning for USVs based upon the fundamentals of GP (Gaussian process) motion planning (GPMP, or its updated version GPMP2). Firstly, we integrated the anisotropy into GPMP2 to make the generated trajectories follow ocean currents where necessary to reduce energy consumption on resisting ocean currents. Secondly, to further improve the computational speed and trajectory quality, a dynamic fast GP interpolation is integrated in the algorithm. Finally, the new algorithm has been validated on a WAM-V 20 USV in a ROS environment to show the practicability of anisotropic GPMP2. Note to Practitioners—The work reported in this article will be significant for USVs to conduct missions in complex, dynamic maritime environments where various obstacles and time-varying ocean currents exit. We develop this novel motion planning algorithm based on Gaussian process and optimise the trajectory using probabilistic inferences. The new algorithm can generate collision free trajectories that also minimise the influences caused by adverse ocean currents in a highly efficient way. In addition, the planning has been undertaken in a continuous-time domain making the generated trajectory have a guaranteed smoothness and readily feasible for autopilots to track. We use a coastal area with time-varying vortexes to present a challenging practical maritime environment. The presented algorithm integrates the available information about a fluid field regarding energy consumption and hazard level, along with the density of obstacles to plan a navigational route efficiently. To increase the practical performance of the proposed method, diverse models for generating ocean currents need to be developed in the future to tackle unpredictable situations.

Autonomous unmanned aerial vehicle (UAV) landing on a moving unmanned ground vehicle (UGV) remains a challenge as it is difficult for the UAV to track the real-time state of the UGV and adjust its landing policy accordingly. This paper proposes a learning framework for a quadrotor UAV to land on a moving UGV without knowing its motion dynamics. Specifically, the learning framework consists of two main systems: a Landing Vision System (LVS) using deep learning and a Landing Control System (LCS) using deep reinforcement learning. The LVS enables the UAV to recognize and localize the UGV in real time to estimate the relative position and velocity between them. Besides, the location of the UGV is tracked in the field of view of the UAV using consecutive images, alleviating the tracking failure problem. We propose a Memory Consolidated TD3 (MCTD3) algorithm to generate optimal policies to enable precise tracking and landing control of the UAV. In addition, we propose an adaptive COACH (ACOACH) algorithm that allows human intervention in the action space of the UAV to speed up the training process. We demonstrate the effectiveness of the proposed method in both simulation and real-world experiments.