22 accepted papers
This paper investigates the reliable parking space search problem in structured environments, with the objective of minimizing the linear combination of mean and standard deviation (mean-std) parking space search time. While canonical parking space search algorithms usually target the minimal expect
This paper studies a risk-sensitive formulation of the multi-robot search problem, termed multi-robot mean-standard deviation search (MuRMSS), in which a team of robots cooperatively search for a moving target by minimizing a linear combination of the mean and standard deviation of search time. Howe
This paper investigates the resilient multi-robot efficient search problem (R-MuRES), which aims at coordinating multiple robots for the minimal time detection of a 'non-adversarial' moving target. R-MuRES faces challenges like robot malfunctions and withdrawals during task execution, leading to a v…
This paper investigates the multi-robot efficient search (MuRES) problem in uncertain topological networks. One unique characteristic of the studied problem is that the topology of the underlying network is uncertain, posing great challenges to canonical MuRES solutions which presumes a fixed networ…
This paper studies the multi-robot reliable navigation problem in uncertain topological networks, which aims at maximizing the robot team's on-time arrival probabilities in the face of road network uncertainties. The uncertainty in these networks stems from the unknown edge traversability, which is
Multi-robot navigation in complex environments relies on inter-robot communication and mutual observation for situational awareness. This paper studies the multi-robot navigation problem in unknown environments with line-ofsight (LoS) connectivity constraints. While previous works are limited to kno
Autonomous exploration requires a robot to explore an unknown environment while constructing an accurate map using Simultaneous Localization and Mapping (SLAM) techniques. Without prior information, the exploration performance is usually conservative due to the limited planning horizon. This letter
This paper considers the multi-robot active graph exploration problem, where robots need to collaboratively cover a graph environment while maintaining reliable pose estimation in collaborative Simultaneous Localization and Mapping (SLAM). Considering both objectives presents challenges for multi-ro…
Recent advances in multi-agent reinforcement learning (MARL) have significantly enhanced cooperation capabilities within multi-robot teams. However, the application to heterogeneous teams poses the critical challenge of combinatorial generalization—adapting learned policies to teams with new composi
This letter investigates the multi-robot patrolling (MuRP) problem in a discrete environment with the objective of achieving uniform node coverage probability distribution by the robot team. Existing MuRP solutions for uniform node coverage either involve high computational complexity for the global
Autonomous driving in the rain remains a challenge. One main problem is performance degradation caused by rain. This work introduces a new dataset to study this problem. Our dataset is collected from a full-scale vehicle equipped with a 3D LiDAR sensor and multiple forward-facing cameras under vario…
We present an efficient Neural Neighborhood Search (N2S) approach for pickup and delivery problems (PDPs). In specific, we design a powerful Synthesis Attention that allows the vanilla self-attention to synthesize various types of features regarding a route solution. We also exploit two customized d…
This letter presents a new range of multi-robot search for a non-adversarial moving target problems, namely multi-robot reliable search (MuRRS). The term ‘reliability’ in MuRRS is defined as the expectation of a predefined utility function over the probability density function (PDF) of the target’s
This paper addresses the safe path planning problem for autonomous mobility with multi-modal perception uncertainties. Specifically, we assume that different sensor inputs lead to different Gaussian process regulated perception uncertainties (named as multi-modal perception uncertainties). We implem…
Navigation through dynamic pedestrian environments in a socially compliant manner is still a challenging task for autonomous vehicles. Classical methods usually lead to unnatural vehicle behaviours for pedestrian navigation due to the difficulty in modeling social conventions mathematically. This pa…
This paper studies the group multi-object tracking (MOT) problem in dynamic pedestrian environments, with intended application to safe navigation for autonomous vehicles. We complete a full autonomous vehicle navigation pipeline from object detection, tracking, grouping, to risk map generation and s…
Exploiting multi-scale features has shown great potential in tackling semantic segmentation problems. The aggregation is commonly done with sum or concatenation (concat) followed by convolutional (conv) layers. However, it fully passes down the high-level context to the following hierarchy without c
This paper studies the adaptive reliable shortest path (RSP) planning problem in a Gaussian process (GP) regulated environment. With the reasonable assumption that the travel times of the underlying transportation network follow a multi-variate Gaussian distribution, we propose two algorithms namely…
This paper investigates the safe path planning problem for an autonomous vehicle operating in unstructured, cluttered environments. While some objects may be accurately with canonical perception algorithms, other objects and clutter may be harder to track. We present an approach that combines two me…
Government data identifies driver behaviour errors as a factor in 94% of car crashes, and autonomous vehicles (AVs), which avoids risky driver behaviours completely, are expected to reduce the number of road crashes significantly. Thus, one of the central focuses of developing AVs is to ensure safet…
Learning by demonstration methods have gained considerable interest in human-coupled robot control. It aims at modeling the goal motion trajectories through human demonstration. However, in lower exoskeleton control, the physical human-robot interaction is changing from pilot to pilot or even for on…
Human-powered lower exoskeletons have gained considerable interests from both academia and industry over the past few decades, and thus have seen increasing applications in areas of human locomotion assistance and strength augmentation. One of the most important aspects in those applications is to a…