Performance measures for locomotion robots

We design two performance measures for a planar locomotion robot, modeled closely after the Platonic Beast. The first measure is proportional to the total motion of all joints during locomotion of the robot. This is a rough approximation to the energy consumption of the robot. The second measure determines the maximal speed of locomotion, for given limits on the joint speeds. We compute optimal modes of locomotion on different slopes for various designs. The results indicate that a variable link length can greatly improve the ability of the robot to walk on steep slopes. © 1997 John Wiley & Sons, Inc.     

A 'sidewinding' locomotion gait for hyper-redundant robots

This paper considers a novel form of hyper-redundant mobile robot locomotion which is analogous to the 'sidewinding' locomotion gait employed by several species of snake. It is shown that this gait can be generated by a repetitive traveling wave of mechanism deformation. This paper considers primarily the kinematics of the sidewinding gait. The kinematic analysis is based on a continuous 'backbone curve' model which captures the robot's important macroscopic features. Using this continuous model, we first develop algorithms which enable travel in a uniform direction. We subsequently extend this basic gait pattern to enable changes in the direction of travel.     

Emergent songs by social robots

This paper reports the first results of an innovative approach to modelling music cognition based on the emergent behaviour of interacting autonomous systems. A group of interactive autonomous singing robots were programmed to develop a shared repertoire of songs from scratch, after a period of spontaneous creations, adjustments and memory reinforcements. The robots interact with each other by means of vocal-like sounds. They use real sounds as opposed to software simulation. They are furnished with a physical model of the vocal tract, which synthesises vocal singing-like intonations, and a listening mechanism, which extracts pitch sequences from audio signals. The robots learn to imitate each other by babbling heard intonation patterns in order to evolve vectors of motor control parameters to synthesise the imitations. Models of the basic mechanisms underlying the emergence of songs are of great interest for musicians looking for hitherto unexplored ways to create music with interactive machines.     

Design and control of tensegrity robots for locomotion

The static properties of tensegrity structures have been widely appreciated in civil engineering as the basis of extremely lightweight yet strong mechanical structures. However, the dynamic properties and their potential utility in the design of robots have been relatively unexplored. This paper introduces robots based on tensegrity structures, which demonstrate that the dynamics of such structures can be utilized for locomotion. Two tensegrity robots are presented: TR3, based on a triangular tensegrity prism with three struts, and TR4, based on a quadrilateral tensegrity prism with four struts. For each of these robots, simulation models are designed, and automatic design of controllers for forward locomotion are performed in simulation using evolutionary algorithms. The evolved controllers are shown to be able to produce static and dynamic gaits in both robots. A real-world tensegrity robot is then developed based on one of the simulation models as a proof of concept. The results demonstrate that tensegrity structures can provide the basis for lightweight, strong, and fault-tolerant robots with a potential for a variety of locomotor gaits.     

Multimode locomotion via SuperBot reconfigurable robots

One of the most challenging issues for a self-sustaining robotic system is how to use its limited resources to accomplish a large variety of tasks. The scope of such tasks could include transportation, exploration, construction, inspection, maintenance,in-situ resource utilization, and support for astronauts. This paper proposes a modular and reconfigurable solution for this challenge by allowing a robot to support multiple modes of locomotion and select the appropriate mode for the task at hand. This solution relies on robots that are made of reconfigurable modules. Each locomotion mode consists of a set of characteristics for the environment type, speed, turning-ability, energy-efficiency, and recoverability from failures. This paper demonstrates a solution using the SuperBot robot that combines advantages from M-TRAN, CONRO, ATRON, and other chain-based and lattice-based robots. At the present, a single real SuperBot module can move, turn, sidewind, maneuver, and travel on batteries up to 500 m on carpet in an office environment. In physics-based simulation, SuperBot modules can perform multimodal locomotions such as snake, caterpillar, insect, spider, rolling track, H-walker, etc. It can move at speeds of up to 1.0 m/s on flat terrain using less than 6 W per module, and climb slopes of no less 40 degrees. Harris Chi Ho Chiu is a PhD Student in Computer Science at the University of Southern California and a research assistant in Polymorphic Robotics Laboratory of Information Science Institute. He received his Master in Computer Science from the University of Southern California and his Bachelor of Engineering from the University of Hong Kong. His research interests include intelligent automated systems, modular self-reconfigurable systems, artificial intelligence, and machine learning. Michael Rubenstein is currently a PhD student at the Polymorphic Robotics Laboratory, working on the CONRO and Superbot self-reconfigurable robotic systems. He has received his bachelors in Electrical Engineering from Purdue University, and his masters in Electrical Engineering from the University of Southern California, and is currently working towards his PhD in Computer Science from the University of Southern California. His interests include modular self-reconfigurable systems, autonomous robots, self-healing systems, and self-replicating systems. Jagadesh B Venkatesh is a member of the Polymorphic Robotics Laboratory at the Information Sciences Institute. He is currently a Master’s candidate in the Product Development Engineering program at the University of Southern California. He received his MS in Computer Science with specialization in Intelligent Robotics, also at the University of Southern California in 2005. His current interest is the commercialization of robotic technologies, specifically in the consumer robotics sector.     

Kinematic motion model for jumping scout robots

/sup T/he University of Minnesota's Scout is a small cylindrical robot capable of rolling and jumping. Models describing the robot's motion are developed. These models can be employed for motion prediction and simulation. The results suggest that the determining factor of the Scout's behavior is the length of the winch cable.     

Temporal logic motion planning for dynamic robots

In this paper, we address the temporal logic motion planning problem for mobile robots that are modeled by second order dynamics. Temporal logic specifications can capture the usual control specifications such as reachability and invariance as well as more complex specifications like sequencing and obstacle avoidance. Our approach consists of three basic steps. First, we design a control law that enables the dynamic model to track a simpler kinematic model with a globally bounded error. Second, we built a robust temporal logic specification that takes into account the tracking errors of the first step. Finally, we solve the new robust temporal logic path planning problem for the kinematic model using automata theory and simple local vector fields. The resulting continuous time trajectory is provably guaranteed to satisfy the initial user specification.     

Coordinated motion planning for two independent robots

We present anO(n ²) algorithm for planning a coordinated collision-free motion of two independent robot systems of certain kinds, each having two degrees of freedom, which move in the plane amidst polygonal obstacles having a total ofn corners. We exemplify our technique in the case of two planar Stanford arms, but also discuss the case of two discs or convex translating objects. The algorithm improves previous algorithms for this kind of problems, and can be extended to a fairly simple general technique for obtaining efficient coordinated motion planning algorithms.     

High-level motion planning for CPG-driven modular robots

Modular robots may become candidates for search and rescue operations or even for future space missions, as they can change their structure to adapt to terrain conditions and to better fulfill a given task. A core problem in such missions is the ability to visit distant places in rough terrain. Traditionally, the motion of modular robots is modeled using locomotion generators that can provide various gaits, e.g. crawling or walking. However, pure locomotion generation cannot ensure that desired places in a complex environment with obstacles will in fact be reached. These cases require several locomotion generators providing motion primitives that are switched using a planning process that takes the obstacles into account. In this paper, we present a novel motion planning method for modular robots equipped with elementary motion primitives. The utilization of primitives significantly reduces the complexity of the motion planning which enables plans to be created for robots of arbitrary shapes. The primitives used here do not need to cope with environmental changes, which can therefore be realized using simple locomotion generators that are scalable, i.e., the primitives can provide motion for robots with many modules. As the motion primitives are realized using locomotion generators, no reconfiguration is required and the proposed approach can thus be used even for modular robots without self-reconfiguration capabilities. The performance of the proposed algorithm has been experimentally verified in various environments, in physical simulations and also in hardware experiments.     

A versatile locomotion mechanism for amphibious robots: eccentric paddle mechanism

Most of recently developed rescue robots can only be deployed to limited attacked regions after tsunami and the floods, due to their limited mobility on complex amphibious terrains. To access such amphibious environments with improved mobility, we propose a novel eccentric paddle mechanism (ePaddle) which has a set of paddles eccentrically placed in a wheel to perform multiple terrestrial, aquatic, and amphibious gaits. One of the advantages of our proposed ePaddle mechanism is its unique locomotion versatility introduced by the eccentric distance between the paddle shaft and the wheel center. We demonstrate this versatility by proposing five typical gaits for traveling on different terrains. For instance, wheeled rolling gait is used to achieve high-speed locomotion on even terrain. Legged gait is applied to travel on the rough terrains. To access the soft terrains where wheels slip and legs sink, a wheel-leg-integrated gait is performed by digging the paddle into the ground. To swim in the water, rotational paddling and oscillating paddling gaits are proposed. For each of these gaits, standard gait sequence is defined and joint parameters are calculated based on kinematics. An ePaddle prototype is then built and tested with the proposed gait sequences. Experimental results verify the design of the ePaddle mechanism as well as its versatile gaits.     

广义Voronoi图求解多机器人运动规划

在拥挤环境中,由于障碍物的边界形状比较复杂,需要使用广义Voronoi图表示空间环境。且在多移动机器人的运动规划过程中,需要协调多个机器人的运动,必须得到Voronoi图通道的宽度。为此提出了一种计算拥挤障碍物环境中生成的广义Voronoi图及其通道宽度的算法。并在生成的Voronoi图上利用A*算法对多个机器人进行路径规划,并利用分布式方法协调多个机器人运动。对协调两个机器人运动的过程进行了仿真,仿真结果表明利用提出的算法生成的具有通道宽度信息的Voronoi图能够满足多移动机器人运动规划的需要。     

Clock-turning gait synthesis for humanoid robots

Turning gait is a basic motion for humanoid robots. This paper presents a method for humanoid tuming, i.e. clock-turning. The objective of clock-turning is to change robot direction at a stationary spot. The clock-turning planning consists of four steps： ankle trajectory generation, hip trajectory generation, knee trajectory generation, and inverse kinematics calculation. Our proposed method is based on a typical humanoid structure with 12 DOFs （degrees of freedom）. The final output of clock-turning planning is 12 reference trajectories, which are used to control a humanoid robot with 12 DOFs. ZMP （zero moment point） is used as stability criterion for the planning. Simulation experiments are conducted to verify the effectiveness of our proposed clock-turuing method.     

A review of motion planning algorithms for intelligent robots

Journal of Intelligent Manufacturing - Principles of typical motion planning algorithms are investigated and analyzed in this paper. These algorithms include traditional planning algorithms,...     

Multi-objective optimization for efficient motion of underwater snake robots

Underwater snake robots constitute a bio-inspired solution within underwater robotics. Increasing the motion efficiency in terms of the forward speed by improving the locomotion methods is a key issue for underwater robots. Furthermore, the energy efficiency is one of the main challenges for long-term autonomy of these systems. In this study, we will consider both these aspects of efficiency, which in some cases can be conflicting. To this end, we formulate a multi-objective optimization problem to minimize power consumption and maximize forward velocity. In particular, the optimal values of the gait parameters for different motion patterns are calculated in the presence of trade-offs between power consumption and velocity. As is the case with all multi-objective optimization problems, the solution is not a single point but rather a set of points. We present a weighted-sum method to combine power consumption and forward velocity optimization problems. Particle swarm optimization is applied to obtain optimal gait parameters for different weighting factors. Trade-off curves or Pareto fronts are illustrated in a power consumption–forward velocity plane for both lateral and eel-like motion pattern. They give information on objective trade-offs and can show how improving power consumption is related to deteriorating the forward velocity along the trade-off curve. Therefore, decision makers can specify the preferred Pareto optimal point along the trade-off curve. Moreover, we address some interesting questions regarding the optimal gait parameters, which is a significant issue for the control of underwater snake robots in the future.     

HOJO-brain for motion control of robots and biological systems

The purpose of this research was to propose and develop a control method in the robotic and biomedical fields which is configured by a robotic/biological simulator, an analytical control frame which has phase sequences, sensory feedback, and an artificial central pattern generator (CPG) which is constructed by a recurrent neural network (RNN) and a genetic algorithm (GA). We call such a controller a “HOJO-brain”, which means a supplementary brain for motion control. We applied this method in the robotic and biomedical fields. In the robotic field, the HOJO-brain was applied to a 5-DOF legged-locomotion robot and a 32-DOF humanoid simulation model consisting of antagonistic muscles. In the biomedical field, it was applied to animals as the FES (functional electrical stimulation) controller. This FES control system with a HOJO-brain has the potential to give more effective and emergent motion control to severely physically handicapped people such as quadraplegics. With computer simulations and simple experiments using animals, we abtained performance indices which confirmed the fine adaptability and emergence for motion control. This work was presented, in part, at the Second International Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1997     

A distributed and morphology-independent strategy for adaptive locomotion in self-reconfigurable modular robots

In this paper, we present a distributed reinforcement learning strategy for morphology-independent life-long gait learning for modular robots. All modules run identical controllers that locally and independently optimize their action selection based on the robot’s velocity as a global, shared reward signal. We evaluate the strategy experimentally mainly on simulated, but also on physical, modular robots. We find that the strategy: (i) for six of seven configurations (3–12 modules) converge in 96% of the trials to the best known action-based gaits within 15 min, on average, (ii) can be transferred to physical robots with a comparable performance, (iii) can be applied to learn simple gait control tables for both M-TRAN and ATRON robots, (iv) enables an 8-module robot to adapt to faults and changes in its morphology, and (v) can learn gaits for up to 60 module robots but a divergence effect becomes substantial from 20–30 modules. These experiments demonstrate the advantages of a distributed learning strategy for modular robots, such as simplicity in implementation, low resource requirements, morphology independence, reconfigurability, and fault tolerance.     

移动机器人运动规划中的深度强化学习方法

随着移动机器人作业环境复杂度的提高、随机性的增强、信息量的减少,移动机器人的运动规划能力受到了严峻的挑战.研究移动机器人高效自主的运动规划理论与方法,使其在长期任务中始终保持良好的复杂环境适应能力,对保障工作安全和提升任务效率具有重要意义.对此,从移动机器人运动规划典型应用出发,重点综述了更加适应于机器人动态复杂环境的运动规划方法——深度强化学习方法.分别从基于价值、基于策略和基于行动者-评论家三类强化学习运动规划方法入手,深入分析深度强化学习规划方法的特点和实际应用场景,对比了它们的优势和不足.进而对此类算法的改进和优化方向进行分类归纳,提出了目前深度强化学习运动规划方法所面临的挑战和亟待解决的问题,并展望了未来的发展方向,为机器人智能化的发展提供参考.