一种多移动机器人避障的改进算法

为了使多机器人在有障碍物的环境中可靠地运行,针对多机器人的避障问题,融合沿墙行为的避障模式,构造出一类具有自适应特性l-ψ闭环控制律下的多机器人避障算法,以作为基于行为的控制策略的有益补充。仿真结果表明,该算法可以成功地解决机器人因融合参数不当而形成的避障"死锁"问题,使多机器人在有障碍物的环境下,在障碍物区能够顺利地通过障碍物,在离开障碍物后,快速恢复至稳定。     

ABC2 an Agenda Based Multi-Agent Model for Robots Control and Cooperation

This paper presents a model for the control of autonomous robots that allows cooperation among them. The control structure is based on a general purpose multi-agent architecture using a hybrid approach made up by two levels. One level is composed of reactive skills capable of achieving simple actions by their own. The other one uses an agenda used as an opportunistic planning mechanism to compound, activate and coordinate the basic skills. This agenda handles actions both from the internal goals of the robot or from other robots. This two level approach allows the integration of real-time response of reactive systems needed for robot low-level behavior, with a classical high level planning component that permits a goal oriented behavior. The paper describes the architecture itself, and its use in three different domains, including real robots, as well as the issues arising from its adaptation to the RoboCup simulator domain.     

多移动机器人系统个体控制体系结构

本文面向多移动机器人系统,提出了一种适合于移动机器人个体的分层式体系结构, 包括系统监控层、协作规划层和行为控制层三个层次．其中系统监控层主要实现人对系统的 实时监控功能;协作规划层在与其它机器人相应层的交互过程中建立系统的分层式组织形式 ,合理快速地完成任务的分解和分配,实现了机器人之间的任务级协作;行为控制层主要采 用基于行为的方法实现具体的运动控制．该结构满足了移动机器人渐趋复杂的应用环境和日 益增大的系统规模的要求．     

An adaptive particle filter for soft fault compensation of mobile robots

Soft fault compensation plays an important role in mobile robot locating, mapping, and navigating. It is difficult to achieve fast and accurate compensation for mobile robots because they are usually highly non-linear, non-Gaussian systems with limited computation and memory resources. An adaptive particle filter is presented to compensate two kinds of soft faults for mobile robots, i.e., noise or factor faults of dead reckoning sensors and slippage of wheels. Firstly, the kinematics models and the fault models are discussed, and five kinds of residual features are extracted to detect soft faults. Secondly, an adaptive particle filter is designed for fault compensation, and two kinds of adaptive scheme are discussed： 1） the noise variances of linear speed and yaw rate are adjusted according to residual features; 2） the particle number is adapted according to Kullback-Leibler divergence （KLD） of two approximate distribution denoted with two particle sets with different particles, i.e., increasing particle number if the KLD is large and decreasing particle number if the KLD is small. The theoretic proof is given and experimental results show the efficiency and accuracy of the presented approach.     

Robot populations and their controlled evolution

This paper reports recent results in controllability theory for a class of positive linear discrete-time systems which were employed to examine and analyse the reachability and controllability properties of cohort-type population models representing the dynamics of autonomous intelligent robot communities. This work was presented in part at the Fourth International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–22, 1999     

Application of Model Reference Adaptive Control to Industrial Robot Impedance Control

The paper deals with the application of model reference adaptive control to robot impedance control, which is actually a technique of steering the end-effector on a prescribed path and satisfying a prescribed dynamic relationship between the force and the end-effector position. Due to unknown parameters of the environment (stiffness, exact position), a model reference algorithm is proposed which differs from classical algorithms in its method of excitation. The results of the proposed procedure are illustrated by implementation on the ASEA IRb 6 industrial robot.     

Fractal Fitness Landscape and Loss of Robustness in Evolutionary Robot Navigation

An autonomous robot Khepera was simulated with a sensory-motor model, which evolves in the genetic algorithm (GA) framework, with the fitness evaluation in terms of the navigation performance in a maze course. The sensory-motor model is a developed neural network decoded from a graph-represented chromosome, which is evolved in the GA process with several genetic operators.It was found that the fitness landscape is very rugged when it is observed at the starting point of the course. A hypothesis for this ruggedness is proposed, and is supported by the measurement of fractal dimension. It is also observed that the performance is sometimes plagued by Loss of Robustness, after the robot makes major evolutionary jumps. Here, the robustness is quantitatively defined as a ratio of the averaged fitness of the evolved robot navigating in perturbed environments over the fitness of the evolved robot in the referenced environment.Possible explanation of robustness loss is the over-adaptation occurred in the environment where the evolution was taken place. Testing some other possibilities for this loss of robustness, many simulation experiments were conducted which smooth out the discrete factors in the model and environment. It was found that smoothing the discrete factors does not solve the loss of robustness. An effective method for maintaining the robustness is the use of averaged fitness over different navigation conditions.The evolved models in the simulated environment were tested by down-loading the models into the real Khepera robot. It is demonstrated that the tendency of fitness values observed in the simulation were adequately regenerated.     

Admittance controller complemented with real-time singularity avoidance for rehabilitation parallel robots

Rehabilitation tasks demand robust and accurate trajectory-tracking performance, mainly achieved with parallel robots. In this field, limiting the value of the force exerted on the patient is crucial, especially when an injured limb is involved. In human–robot interaction studies, the admittance controller modifies the location of the robot according to the user efforts driving the end-effector to an arbitrary location within the workspace. However, a parallel robot has singularities within the workspace, making implementing a conventional admittance controller unsafe. Thus, this study proposes an admittance controller that overcomes the limitations of singular configurations by using a real-time singularity avoidance algorithm. The singularity avoidance algorithm modifies the original trajectory based on the actual location of the parallel robot. The complemented admittance controller is applied to a 4 degrees of freedom parallel robot for knee rehabilitation. In this case, the actual location is measured by a 3D tracking system because the location calculated by the forward kinematics is inaccurate in the vicinity of a singularity. The experimental results verify the effectiveness of the proposed admittance controller for safe knee rehabilitation exercises.     

3D-Printed Photoresponsive Liquid Crystal Elastomer Composites for Free-Form Actuation

Direct ink writing of liquid crystal elastomers (LCEs) offers a new opportunity to program geometries for a wide variety of shape transformation modes toward applications such as soft robotics. So far, most 3D-printed LCEs are thermally actuated. Herein, a 3D-printable photoresponsive gold nanorod (AuNR)/LCE composite ink is developed, allowing for photothermal actuation of the 3D-printed structures with AuNR as low as 0.1 wt.%. It is shown that the printed filament has a superior photothermal response with 27% actuation strain upon irradiation to near-infrared (NIR) light (808 nm) at 1.4 W cm⁻² (corresponding to 160 °C) under optimal printing conditions. The 3D-printed composite structures can be globally or locally actuated into different shapes by controlling the area exposed to the NIR laser. Taking advantage of the customized structures enabled by 3D printing and the ability to control locally exposed light, a light-responsive soft robot is demonstrated that can climb on a ratchet surface with a maximum speed of 0.284 mm s⁻¹ (on a flat surface) and 0.216 mm s⁻¹ (on a 30° titled surface), respectively, corresponding to 0.428 and 0.324 body length per min, respectively, with a large body mass (0.23 g) and thickness (1 mm).     

An Intelligent Robotic System Capable of Sensing and Describing Objects Based on Bimodal,Self-Powered Flexible Sensors

This study presents an intelligent soft robotic system capable of perceiving, describing, and sorting objects based on their physical properties. This work introduces a bimodal self-powered flexible sensor (BSFS) based on the triboelectric nanogenerator and giant magnetoelastic effect. The BSFS features a simplified structure comprising a magnetoelastic conductive film and a packaged liquid metal coil. The BSFS can precisely detect and distinguish touchless and tactile models, with a response time of 10 ms. By seamlessly integrating the BSFSs into the soft fingers, this study realizes an anthropomorphic soft robotic hand with remarkable multimodal perception capabilities. The touchless signals provide valuable insights into object shape and material composition, while the tactile signals offer precise information regarding surface roughness. Utilizing a convolutional neural network (CNN), this study integrates all sensing information, resulting in an intelligent soft robotic system that accurately describes objects based on their physical properties, including materials, surface roughness, and shapes, with an accuracy rate of up to 97%. This study may lay a robotic foundation for the hardware of the general artificial intelligence with capacities to interpret and interact with the physical world, which also serves as an interface between artificial intelligence and soft robots.