In this paper, we present a novel trajectory planning method for externally-actuated modular manipulators (EAMMs), consisting of multiple rotor-actuated links with joints that can be either locked or unlocked. This joint-locking feature allows effective balancing of the payload capacity and dexterity of the robot but significantly complicates the planning problem by introducing binary decision variables. To address this challenge, we leverage the problem’s intrinsic structure, i.e., the payload at the end-effector being enhanced by merely locking its immediate connected links; this allows us to break down the complex planning problem into a series of manageable subproblems and solve them sequentially. Our approach significantly reduces the problem’s complexity: in a serial n-link EAMM with m joint-lock mechanisms, where there could potentially be 2mdistinct configurational dynamics, we require solving only n + 1 trajectory optimization problems for single rigid body dynamics sequentially, thereby rendering the problem tractable. We substantiate the efficacy of our method through various simulation and experimental studies, covering ground-free and ground-bound configurations as well as both motion-only and manipulation tasks.

Aerial robots have a wide range of applications, such as collecting data in hard-to-reach areas. This requires the longest possible operation time. However, because currently available commercial batteries have limited specific energy of roughly 300 W h kg-1, a drone's flight time is a bottleneck for sustainable long-term data collection. Inspired by birds in nature, a possible approach to tackle this challenge is to perch drones on trees, and environmental or man-made structures, to save energy whilst in operation. In this paper, we propose an algorithm to automatically generate trajectories for a drone to perch on a tree branch, using the proposed tethered perching mechanism with a pendulum-like structure. This enables a drone to perform an energy-optimised, controlled 180° flip to safely disarm upside down. To fine-tune a set of reachable trajectories, a soft actor critic-based reinforcement algorithm is used. Our experimental results show the feasibility of the set of trajectories with successful perching. Our findings demonstrate that the proposed approach enables energy-efficient landing for long-term data collection tasks.

Public perceptions of Robotics and Artificial Intelligence (RAI) are important in the acceptance, uptake, government regulation and research funding of this technology. Recent research has shown that the public's understanding of RAI can be negative or inaccurate. We believe effective public engagement can help ensure that public opinion is better informed. In this paper, we describe our first iteration of a high throughput in-person public engagement activity. We describe the use of a light touch quiz-format survey instrument to integrate in-the-wild research participation into the engagement, allowing us to probe both the effectiveness of our engagement strategy, and public perceptions of the future roles of robots and humans working in dangerous settings, such as in the off-shore energy sector. We critique our methods and share interesting results into generational differences within the public's view of the future of Robotics and AI in hazardous environments. These findings include that older peoples' views about the future of robots in hazardous environments were not swayed by exposure to our exhibit, while the views of younger people were affected by our exhibit, leading us to consider carefully in future how to more effectively engage with and inform older people.

We propose a novel robotic platform for aerial operation and manipulation, a spherically connected multiquadrotor (SmQ) platform, which consists of a rigid frame and multiple quadrotors that are connected to the frame via passive spherical joints and act as distributed rotating thrust generators to collectively propel the frame by adjusting their attitude and thrust force. Depending on the number of quadrotors and their configuration, this SmQ platform can fully (or partially) overcome the issues of underactuation of the standard multirotor drones for aerial operation/manipulation (e.g., body-tilting with sideway gust/force, dynamic interaction hard to attain, complicated arm-drone integration, etc.). We present the dynamics modeling of this SmQ platform system and establish the condition for its full actuation in SE(3). We also show how to address limited range of spherical joints and rotor saturations as a constrained optimization problem by noticing the similarity with the multifingered grasping problem under the friction-cone constraint. We then design and analyze feedback control laws for the S3Q and S2Q systems as a combination of high-level Lyapunov control design and low-level constrained optimization and show that the (fully actuated) S3Q system can assume any trajectory in SE(3), whereas the S2Q system in R3× S2with its unactuated dynamics is still internally stable. Experiments are also performed to show the efficacy of the theory.

We propose a new aerial tool operation system consisting of multiple quadrotors connected to a tool by spherical joints to perform tool operation tasks. We model the system and show that the attitude dynamics of each quadrotor is decoupled from the tool dynamics, so that we can consider the quadrotors as thrusters and control the tool by adjusting the orientation and magnitude of these thrusters. We also show that the 6-DOF tool dynamics could be under-actuated or fully-actuated, depending on the number of quadrotors attached to the tool and the geometric configuration of their attachments. We then design control laws for the tool-tip position/orientation tracking of the (under-actuated) tool system with two quadrotors and the (fully-actuated) tool system with three quadrotors. We use Lyapunov approach to find the desired thrust command for each quadrotor while also taking into account the spherical joint limits in a form of constrained optimization. Simulation and implementation results are performed to support the theory.

This paper presents a hybrid force/motion control framework for quadrotors with a rigid/light tool attached on it. By transforming the quadrotor dynamics into that of the tool-tip position y and applying the passive decomposition to decompose its dynamics into tangential and normal components w.r.t. a contact surface, we design hybrid position/force control. We also elucidate the internal dynamics (i.e., the dynamics hidden from the tool-tip position and yaw angle output and not directly affected by the control action due to the quadro-tor's underactuation), reveal a (seemingly counter-intuitive) necessary condition for internal stability (i.e., tool above the quadrotor, not beneath it), and propose a stabilizing control action to ensure the angular rates still be bounded while preventing the finite-time escape. Simulations are performed to support the theory.