2,488 research outputs found
A Survey of Multi-Robot Motion Planning
Multi-robot Motion Planning (MRMP) is an active research field which has
gained attention over the years. MRMP has significant roles to improve the
efficiency and reliability of multi-robot system in a wide range of
applications from delivery robots to collaborative assembly lines. This survey
provides an overview of MRMP taxonomy, state-of-the-art algorithms, and
approaches which have been developed for multi-robot systems. This study also
discusses the strengths and limitations of each algorithm and their
applications in various scenarios. Moreover, based on this, we can draw out
open problems for future research.Comment: This is my Ph.D. comprehensive exam repor
Risk analysis and decision making for autonomous underwater vehicles
Risk analysis for autonomous underwater vehicles (AUVs) is essential to enable AUVs to
explore extreme and dynamic environments. This research aims to augment existing risk
analysis methods for AUVs, and it proposes a suite of methods to quantify mission risks and to
support the implementation of safety-based decision making strategies for AUVs in harsh
marine environments. This research firstly provides a systematic review of past progress of risk
analysis research for AUV operations. The review answers key questions including fundamental
concepts and evolving methods in the domain of risk analysis for AUVs, and it highlights future
research trends to bridge existing gaps. Based on the state-of-the-art research, a copula-based
approach is proposed for predicting the risk of AUV loss in underwater environments. The
developed copula Bayesian network (CBN) aims to handle non-linear dependencies among
environmental variables and inherent technical failures for AUVs, and therefore achieve
accurate risk estimation for vehicle loss given various environmental observations. Furthermore,
path planning for AUVs is an effective decision making strategy for mitigating risks and
ensuring safer routing. A further study presents an offboard risk-based path planning approach
for AUVs, considering a challenging environment with oil spill scenarios incorporated. The
proposed global Risk-A* planner combines a Bayesian-based risk model for probabilistic risk
reasoning and an A*-based algorithm for path searching. However, global path planning
designed for static environments cannot handle the unpredictable situations that may emerge,
and real-time replanned solutions are required to account for dynamic environmental
observations. Therefore, a hybrid risk-aware decision making strategy is investigated for AUVs
to combine static global planning with dynamic local re-planning. A dynamic risk analysis
model based on the system theoretic process analysis (STPA) and BN is applied for generating
a real-time risk map in target mission areas. The dynamic window algorithm (DWA) serves for
local path planning to avoid moving obstacles. The proposed hybrid risk-aware decisionmaking
architecture is essential for the real-life implementation of AUVs, leading eventually to
a real-time adaptive path planning process onboard the AUV
Planning Algorithms for Multi-Robot Active Perception
A fundamental task of robotic systems is to use on-board sensors and perception algorithms to understand high-level semantic properties of an environment. These semantic properties may include a map of the environment, the presence of objects, or the parameters of a dynamic field. Observations are highly viewpoint dependent and, thus, the performance of perception algorithms can be improved by planning the motion of the robots to obtain high-value observations. This motivates the problem of active perception, where the goal is to plan the motion of robots to improve perception performance. This fundamental problem is central to many robotics applications, including environmental monitoring, planetary exploration, and precision agriculture. The core contribution of this thesis is a suite of planning algorithms for multi-robot active perception. These algorithms are designed to improve system-level performance on many fronts: online and anytime planning, addressing uncertainty, optimising over a long time horizon, decentralised coordination, robustness to unreliable communication, predicting plans of other agents, and exploiting characteristics of perception models. We first propose the decentralised Monte Carlo tree search algorithm as a generally-applicable, decentralised algorithm for multi-robot planning. We then present a self-organising map algorithm designed to find paths that maximally observe points of interest. Finally, we consider the problem of mission monitoring, where a team of robots monitor the progress of a robotic mission. A spatiotemporal optimal stopping algorithm is proposed and a generalisation for decentralised monitoring. Experimental results are presented for a range of scenarios, such as marine operations and object recognition. Our analytical and empirical results demonstrate theoretically-interesting and practically-relevant properties that support the use of the approaches in practice
Cognitive Modeling for Computer Animation: A Comparative Review
Cognitive modeling is a provocative new paradigm that paves the way towards intelligent graphical characters by providing them with logic and reasoning skills. Cognitively empowered self-animating characters will see in the near future a widespread use in the interactive game, multimedia, virtual reality and production animation industries. This review covers three recently-published papers from the field of cognitive modeling for computer animation. The approaches and techniques employed are very different. The cognition model in the first paper is built on top of Soar, which is intended as a general cognitive architecture for developing systems that exhibit intelligent behaviors. The second paper uses an active plan tree and a plan library to achieve the fast and robust reactivity to the environment changes. The third paper, based on an AI formalism known as the situation calculus, develops a cognitive modeling language called CML and uses it to specify a behavior outline or sketch plan to direct the characters in terms of goals. Instead of presenting each paper in isolation then comparatively analyzing them, we take a top-down approach by first classifying the field into three different categories and then attempting to put each paper into a proper category. Hopefully in this way it can provide a more cohesive, systematic view of cognitive modeling approaches employed in computer animation
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