Many high-level multi-agent planning problems, such as multi-robot navigation and path planning, can be modeled with deterministic actions and observations. In this work, we focus on such domains and introduce the class of Deterministic Decentralized POMDPs (Det-Dec-POMDPs)—a subclass of Dec-POMDPs with deterministic transitions and observations given the state and joint actions. We then propose a practical solver, Iterative Deterministic POMDP Planning (IDPP), based on the classic Joint Equilibrium Search for Policies framework, specifically optimized to handle large-scale Det-Dec-POMDPs that existing Dec-POMDP solvers cannot handle efficiently.

Currently, large partially observable Markov decision processes (POMDPs) are often solved by sampling-based online methods which interleave planning and execution phases. However, a pre-computed offline policy is more desirable in POMDP applications with time or energy constraints. But previous offline algorithms are not able to scale up to large POMDPs. In this article, we propose a new sampling-based algorithm, the partially observable Monte-Carlo graph search (POMCGS) to solve large POMDPs offline. Different from many online POMDP methods, which progressively develop a tree while performing (Monte-Carlo) simulations, POMCGS folds this search tree on the fly to construct a policy graph, so that computations can be drastically reduced, and users can analyze and validate the policy prior to embedding and executing it. Moreover, POMCGS, together with action progressive widening and observation clustering methods provided in this article, is able to address certain continuous POMDPs. Through experiments, we demonstrate that POMCGS can generate policies on the most challenging POMDPs, which cannot be computed by previous offline algorithms, and these policies' values are competitive compared with the state-of-the-art online POMDP algorithms.

It is challenging to find optimum kinematic designs for non-standard robotic manipulators, e.g., medical, nuclear, and space manipulators, which are demanded to adapt to arbitrary complex tasks in constraints. Such design optimization can be modelled as a multi-dimensional non-convex optimization problem with nonlinear constrained conditions. However, it is non-trivial to ensure the essential reachability condition, i.e., the existence of continuous trajectories between demand positions for serial articulated manipulators, given complex spatial constraints, like obstacles and boundaries. Traditional solutions integrate standard motion planning or inverse kinematics algorithms within a kinematic-design optimization process, resulting in significant demand for time and computing resources. To accelerate design optimization at improved efficiency, we design a novel robust design framework built on a new kinematic design synthesis, which allows for simultaneously optimizing dimension and topology of a serial manipulator's kinematics for arbitrary tasks in constrained environments, using a generalised parametric kinematic model. Significantly, in contrast to standard solutions, we develop a novel computationally effective reachability verification method, which rapidly aborts infeasible motions by exploiting efficient collision checks, based on the Rapidly-exploring Random Tree (RRT) algorithm. The effectiveness of the proposed design framework is verified and evaluated by comparing to baseline benchmarks. Results demonstrate the novel design framework can accelerate kinematic design optimization by an order of magnitude compared to the current state-of-the-art, and optimise link dimension and joint type simultaneously of serial robots for cluttered environments.

This paper presents a detailed user study aimed at experimentally comparing the experience levels within bi-lateral teleoperation. The primary objective is to elucidate the key performance metrics that can effectively evaluate the competency level of human operators. Existing methodologies typically focus on the quantitative psychological evaluation of human-in-the-loop systems rather than operator performance. In our experimental study, six novice and four professional operators participated in various telerobotic activities. Various parameters, including task completion duration, errors, remote manipulators' motion, and subjects' gaze information, were captured. Subsequently, the measured performance parameters across all subjects were compared with respect to their level of proficiency through statistical analyses. The results indicate that tasks were performed more quickly by experienced operators, fewer mistakes were made, and remote manipulators were operated more smoothly (e.g., fewer jerks and better maintenance within the centre of the workspace). Additionally, better compensation for the lack of depth perception was demonstrated by experienced operators through effective scanning of multiple viewpoints.

We evaluate AR for Plant maintenance by measuring how a prototype guidance system, tested under representative conditions, impacts performance. We are motivated to determine the cost-benefit of interactive guidance for hazardous, repetitive tasks and we observe an improvement of 21% efficiency, 50% accuracy and 19% reduced task load. AR has already been shown to deliver improvements in task performance, however, there is limited research exploring the integration of AR into complete task routines which presents a barrier to adoption. We apply mixed reality guidance via two within-group experiments. We measure efficiency and accuracy over a complete routine conducted under simulated conditions. Results compare AR versus Static and AR versus Adaptive. We conclude AR is best suited to demanding spatial translation and completion under pressure. We suggest AR offers potential in similar routines and propose further work to integrate in a live setting.