Type Design and Behavior Control for Six Legged Robots

The research on legged robots attracted much attention both from the academia and industry. Legged robots are multi-input multi-output with multiple end-e ector systems. Therefore,the mechanical design and control framework are challenging issues. This paper reviews the development of type synthesis and behavior control on legged robots; introduces the hexapod robots developed in our research group based on the proposed type synthesis method. The control framework for legged robots includes data driven layer,robot behavior layer and robot execution layer. Each layer consists several components which are explained in details. Finally,various experiments were conducted on several hexapod robots. The summarization of the type synthesis and behavior control design constructed in this paper would provide a unified platform for communications and references for future advancement for legged robots.

Development of an Anthropomorphic Robotic Arm and Hand for Interactive Humanoids

Humanoid robots are designed and built to mimic human form and movement. Ultimately, they are meant to resemble the size and physical abilities of a human in order to function in human-oriented environments and to work autonomously but to pose no physical threat to humans. Here, a humanoid robot that resembles a human in appearance and movement is built using powerful actuators paired with gear trains, joint mechanisms, and motor drivers that are all encased in a package no larger than that of the human physique. In this paper, we propose the construction of a humanoid-applicable anthropomorphic 7-DoF arm complete with an 8-DoF hand. The novel mechanical design of this humanoid ann makes it sufficiently compact to be compatible with currently available narrating-model humanoids, and to be sufficiently powerful and flexible to be functional; the number of degrees of freedom endowed in this robotic arm is sufficient for executing a wide range of tasks, including dexterous hand movements. The developed humanoid arm and hand are capable of sensing and interpreting incoming external force using the motor in each joint current without conventional torque sensors. The humanoid ann adopts an algorithm to avoid obstacles and the dexterous hand is capable of grasping objects. The developed robotic ann is suitable for use in an interactive humanoid robot.

Lessons learned:Symbiotic autonomous robot ecosystem for nuclear environments

Nuclear facilities have a regulatory requirement to measure radiation levels within Post Operational Clean Out(POCO)around nuclear facilities each year,resulting in a trend towards robotic deployments to gain an improved understanding during nuclear decommissioning phases.The UK Nuclear Decommissioning Authority supports the view that human-in-the-loop(HITL)robotic deployments are a solution to improve procedures and reduce risks within radiation characterisation of nuclear sites.The authors present a novel implementation of a Cyber-Physical System(CPS)deployed in an analogue nuclear environment,comprised of a multi-robot(MR)team coordinated by a HITL operator through a digital twin interface.The development of the CPS created efficient partnerships across systems including robots,digital systems and human.This was presented as a multi-staged mission within an inspection scenario for the hetero-geneous Symbiotic Multi-Robot Fleet(SMuRF).Symbiotic interactions were achieved across the SMuRF where robots utilised automated collaborative governance to work together,where a single robot would face challenges in full characterisation of radiation.Key contributions include the demonstration of symbiotic autonomy and query-based learning of an autonomous mission supporting scalable autonomy and autonomy as a service.The coordination of the CPS was a success and displayed further challenges and improvements related to future MR fleets.

Uncanny valley for interactive social agents:an experimental study

Background The uncanny valley hypothesis states that users may experience discomfort when inter-acting with almost human-like artificial characters.Advancements in artificial intelligence,robotics,and computer graphics have led to the development of life-like virtual humans and humanoid robots.Revisiting this hypothesis is necessary to check whether they positively or negatively affect the current population,who are highly accustomed to the latest technologies.Methods In this study,we present a unique evaluation of the uncanny valley hypothesis by allowing participants to interact live with four humanoid robots that have varying levels of human-likeness.Each participant completed a survey questionnaire to evaluate the affinity of each robot.Additionally,we used deep learning methods to quantify the participants’emotional states using multimodal cues,including visual,audio,and text cues,by recording the participant-robot interactions.Results Multi-modal analysis and surveys provided interesting results and insights into the uncanny valley hypothesis.

Deep reinforcement learning for shared control of mobile robots

Shared control of mobile robots integrates manual input with auxiliary autonomous controllers to improve the overall system performance.However,prior work that seeks to find the optimal shared control ratio needs an accurate human model,which is usually challenging to obtain.In this study,the authors develop an extended Twin Delayed Deep Deterministic Policy Gradient(DDPG)(TD3X)-based shared control framework that learns to assist a human operator in teleoperating mobile robots optimally.The robot's states,shared control ratio in the previous time step,and human's control input is used as inputs to the reinforcement learning(RL)agent,which then outputs the optimal shared control ratio between human input and autonomous controllers without knowing the human model.Noisy softmax policies are developed to make the TD3X algorithm feasible under the constraint of a shared control ratio.Furthermore,to accelerate the training process and protect the robot,a navigation demonstration policy and a safety guard are developed.A neural network(NN)structure is developed to maintain the correlation of sensor readings among heterogeneous input data and improve the learning speed.In addition,an extended DAGGER(DAGGERX)human agent is developed for training the RL agent to reduce human workload.Robot simulations and experiments with humans in the loop are conducted.The results show that the DAGGERX human agent can simulate real human inputs in the worst-case scenarios with a mean square error of 0.0039.Compared to the original TD3 agent,the TD3X-based shared control system decreased the average collision number from 387.3 to 44.4 in a simplistic environment and 394.2 to 171.2 in a more complex environment.The maximum average return increased from 1043 to 1187 with a faster converge speed in the simplistic environment,while the performance is equally good in the complex environment because of the use of an advanced human agent.In the human subject tests,participants'average perceived workload was significantly lower in shared control than that in exclusively manual control(26.90 vs.40.07,p=0.013).

An efficient and robust system for human following scenario using differential robot

A novel system for human following using a differential robot,including an accurate 3‐D human position tracking module and a novel planning strategy that ensures safety and dynamic feasibility,is proposed.The authors utilise a combination of gimbal camera and LiDAR for long‐term accurate human detection.Then the planning module takes the target's future trajectory as a reference to generate a coarse path to ensure the following visibility.After that,the trajectory is optimised considering other constraints and following distance.Experiments demonstrate the robustness and efficiency of our system in complex environments,demonstrating its potential in various applications.

Hastily formed knowledge networks and distributed situation awareness for collaborative robotics

In the context of collaborative robotics,distributed situation awareness is essential for supporting collective intelligence in teams of robots and human agents where it can be used for both individual and collective decision support.This is particularly important in applications pertaining to emergency rescue and crisis management.During operational missions,data and knowledge are gathered incrementally and in different ways by heterogeneous robots and humans.We describe this as the creation of Hastily Formed Knowledge Networks(HFKNs).The focus of this paper is the specification and prototyping of a general distributed system architecture that supports the creation of HFKNs by teams of robots and humans.The information collected ranges from low-level sensor data to high-level semantic knowledge,the latter represented in part as RDF Graphs.The framework includes a synchronization protocol and associated algorithms that allow for the automatic distribution and sharing of data and knowledge between agents.This is done through the distributed synchronization of RDF Graphs shared between agents.High-level semantic queries specified in SPARQL can be used by robots and humans alike to acquire both knowledge and data content from team members.The system is empirically validated and complexity results of the proposed algorithms are provided.Additionally,a field robotics case study is described,where a 3D mapping mission has been executed using several UAVs in a collaborative emergency rescue scenario while using the full HFKN Framework.