A local obstacle avoidance method for mobile robots in partially known environment

Local obstacle avoidance is a principle capability for mobile robots in unknown or partially known environment. A series of velocity space methods including the curvature velocity method (CVM), the lane curvature method (LCM) and the beam curvature method (BCM) formulate the local obstacle avoidance problem as one of constrained optimization in the velocity space by taking the physical constraints of the environment and the dynamics of the vehicle into account. We present a new local obstacle avoidance approach that combines the prediction model of collision with the improved BCM. Not only does this method inherit the quickness of BCM and the safety of LCM, but also the proposed prediction based BCM (PBCM) can be used to avoid moving obstacles in dynamic environments.     

Redundancy resolution and obstacle avoidance for cooperative industrial robots

Planning methods for effective manipulation of single or multiple redundant arm systems must take account of DOF, the task, constraints, and joint drifts. Here, a new approach to redundancy resolution and obstacle avoidance for cooperative robot arms is proposed. In this development, a relative Jacobian and a relative dexterity measure for cooperative robot arms are derived. A nonlinear programming method is used to optimize the relative dexterity while satisfying cooperative task requirements, limits on joint angles, and obstacle avoidance. With this approach, it is not necessary to balance the weightings between the cost term to be optimized and the penalty from constraints. Configuration jumps over obstacles are avoided. Further, since globally optimal joint configurations are produced, drifts in joint configurations will be absent from the resulting configurations. This article includes several illustrative examples to demonstrate the effectiveness and usefulness of this approach. Results have indicated the benefits of both the relative dexterity and the sum of individual arm dexterities in planning of cooperative tasks. ©1999 John Wiley & Sons, Inc.     

双轮移动机器人安全目标追踪与自动避障算法

设计了双轮移动机器人安全目标追踪算法和双回路的追踪与避障控制方案.内层控制回路是目标追踪的控制律,用来指导机器人追踪到指定目标并保持一定的安全距离,而且兼顾了机器人在运行速度上的限制和追踪的时间效率,其控制的渐近稳定性用Lyapunov函数法进行了证明.当遇到障碍物时,外层控制回路根据超声传感器的信息和阻抗控制的概念产生阻抗虚拟力,将期望目标调整到虚拟位置,使机器人能够自动转向以避开障碍物.仿真研究和实验结果证明了追踪算法的有效性和避障方法的可行性.     

Neural networks based reinforcement learning for mobile robots obstacle avoidance

This study proposes a new approach for solving the problem of autonomous movement of robots in environments that contain both static and dynamic obstacles. The purpose of this research is to provide mobile robots a collision-free trajectory within an uncertain workspace which contains both stationary and moving entities. The developed solution uses Q-learning and a neural network planner to solve path planning problems. The algorithm presented proves to be effective in navigation scenarios where global information is available. The speed of the robot can be set prior to the computation of the trajectory, which provides a great advantage in time-constrained applications. The solution is deployed in both Virtual Reality (VR) for easier visualization and safer testing activities, and on a real mobile robot for experimental validation. The algorithm is compared with Powerbot's ARNL proprietary navigation algorithm. Results show that the proposed solution has a good conversion rate computed at a satisfying speed.     

自主移动机器人避障技术研究现状

对避障技术的核心内容暨避障算法展开归纳介绍,依照传统算法和智能算法分类,详细介绍了应用较多的各避障算法的基本原理、优缺点及部分改进方案;对自主移动机器人避障技术的应用前景和发展趋势作出了展望.     

Region filling operations with random obstacle avoidance for mobile robots

The article presents a new topic in path planning for mobile robots, region filling. which involves a sweeping operation to fill a whole region with random obstacle avoidance. The approaches for global strip filling and local path searching driven by sensory data procedures are developed. A computer graphic simulation is used to verify the filling strategy available. The research was developed from the program for the design of a robot lawn mower. However, the solution appears generic. The significance is that a problem of wide application and generic solutions for general autonomous mobile robots have been developed.     

Moving obstacle avoidance for cable-driven parallel robots using improved RRT

Microsystem Technologies - Moving obstacle avoidance is one of the most challenging problems for cable-driven parallel robots (CDPRs) due to various constraints. In this work, the improved rapidly...     

A real-time planning algorithm for obstacle avoidance of redundant robots

A computationally efficient, obstacle avoidance algorithm for redundant robots is presented in this paper. This algorithm incorporates the neural networks and pseudodistance function D _p in the framework of resolved motion rate control. Thus, it is well suited for real-time implementation. Robot arm kinematic control is carried out by the Hopfield network. The connection weights of the network can be determined from the current value of Jacobian matrix at each sampling time, and joint velocity commands can be generated from the outputs of the network. The obstacle avoidance task is achieved by formulating the performance criterion as D _p>d _min (d _min represents the minimal distance between the redundant robot and obstacles). Its calculation is only related to some vertices which are used to model the robot and obstacles, and the computational times are nearly linear in the total number of vertices. Several simulation cases for a four-link planar manipulator are given to prove that the proposed collision-free trajectory planning scheme is efficient and practical.     

Distributed robust MPC for nonholonomic robots with obstacle and collision avoidance

Considering that the inevitable disturbances and coupled constraints pose an ongoing challenge to distributed control algorithms, this paper proposes a distributed robust model predictive control (MPC) algorithm for a multi-agent system with additive external disturbances and obstacle and collision avoidance constraints. In particular, all the agents are allowed to solve optimization problems simultaneously at each time step to obtain their control inputs, and the obstacle and collision avoidance are accomplished in the context of full-dimensional controlled objects and obstacles. To achieve the collision avoidance between agents in the distributed framework, an assumed state trajectory is introduced for each agent which is transmitted to its neighbors to construct the polyhedral over-approximations of it. Then the polyhedral over-approximations of the agent and the obstacles are used to smoothly reformulate the original nonconvex obstacle and collision avoidance constraints. And a compatibility constraint is designed to restrict the deviation between the predicted and assumed trajectories. Moreover, recursive feasibility of each local MPC optimization problem with all these constraints derived and input-to-state stability of the closed-loop system can be ensured through a sufficient condition on controller parameters. Finally, simulations with four agents and two obstacles demonstrate the efficiency of the proposed algorithm.     

Optimisation-based verification process of obstacle avoidance systems for unicycle-like mobile robots

This paper presents an optimisation-based verification process for obstacle avoidance systems of a unicycle-like mobile robot. It is a novel approach for the collision avoidance verification process. Local and global optimisation based verification processes are developed to find the worst-case parameters and the worst-case distance between the robot and an obstacle. The kinematic and dynamic model of the unicycle-like mobile robot is first introduced with force and torque as the inputs. The design of the control system is split into two parts. One is velocity and rotation using the robot dynamics, and the other is the incremental motion planning for robot kinematics. The artificial potential field method is chosen as a path planning and obstacle avoidance candidate technique for verification study as it is simple and widely used. Different optimisation algorithms are applied and compared for the purpose of verification. It is shown that even for a simple case study where only mass and inertia variations are considered, a local optimization based verification method may fail to identify the worst case. Two global optimisation methods have been investigated: genetic algorithms (GAs) and GLOBAL algorithms. Both of these methods successfully find the worst case. The verification process confirms that the obstacle avoidance algorithm functions correctly in the presence of all the possible parameter variations.     

A method for combining odometry and distance sensor information for effective obstacle avoidance of autonomous mobile robots

In this paper, a concept for virtual sensors is proposed for efficient avoidance of obstacles during the motion of robots. The virtual sensor yields new data by combining encoder values and real distance data, and derives new sensor data that includes the mobility of the robot. Simulation on Windows XP is executed to illustrate the proposed approach with actually acquired distance from virtual and actual sensors. To facilitate comparison with the alternative results developed in this paper, we refer to the conventional artificial potential field method using actual distance. Data from virtual sensors show smoother and safer motion in obstacle avoidance traces in regards to obstacle and robot mobility.     

Decentralized behavior-based formation control of multiple robots considering obstacle avoidance

This paper proposes a decentralized behavior-based formation control algorithm for multiple robots considering obstacle avoidance. Using only the information of the relative position of a robot between neighboring robots and obstacles, the proposed algorithm achieves formation control based on a behavior-based algorithm. In addition, the robust formation is achieved by maintaining the distance and angle of each robot toward the leader robot without using information of the leader robot. To avoid the collisions with obstacles, the heading angles of all robots are determined by introducing the concept of an escape angle, which is related with three boundary layers between an obstacle and the robot. The layer on which the robot is located determines the start time of avoidance and escape angle; this, in turn, generates the escape path along which a robot can move toward the safe layer. In this way, the proposed method can significantly simplify the step of the information process. Finally, simulation results are provided to demonstrate the efficiency of the proposed algorithm.     

多移动机器人的领航-跟随编队避障控制

针对多移动机器人的编队控制问题,提出了一种结合Polar Histogram避障法的领航-跟随协调编队控制算法。该算法在领航-跟随l-φ编队控制结构的基础上引入虚拟跟随机器人,将编队控制转化为跟随机器人对虚拟跟随机器人的轨迹跟踪控制。结合移动机器人自身传感器技术,在简单甚至复杂的环境下为机器人提供相应的路径运动策略,实现实时导航的目的。以两轮差动Qbot移动机器人为研究对象,搭建半实物仿真平台,进行仿真实验。仿真结果表明:该方法可以有效地实现多移动机器人协调编队和避障控制。     

Control of antagonistic swarm dynamics via Lyapunov's method

We consider hostile conflicts between two multi‐agent swarms. First, we investigate the complex nature of a single pursuer attempting to intercept a single evader (1P‐1E), and establish some rudimentary rules of engagement. We elaborate on the stability repercussions of these rules. Second, we extend the modelling and stability analysis to multi‐agent swarms with conflicting interests. The present document considers only swarms with equal membership strengths for simplicity. This effort is based on a set of suggested momenta deployed on individual agents. Because pursuers and evaders differ in the influences that they exert on one another, we emphasize asymmetry in momenta between the two types of swarm members. The proposed centralized control law evolves from a Lyapunov concept. Swarm interactions are modelled in two phases: the approach phase during which the two swarms act like individuals in the 1P‐1E interaction; and the individual pursuit phase where each pursuer is assigned to an evader. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Fuzzy neural networks for obstacle pattern recognition and collision avoidance of fish robots

The problems of detection and pattern recognition of obstacles are the most important concerns for fish robots’ path planning to make natural and smooth movements as well as to avoid collision. We can get better control results of fish robot trajectories if we obtain more information in detail about obstacle shapes. The method employing only simple distance measuring IR sensors without cameras and image processing is proposed. The capability of a fish robot to recognize the features of an obstacle to avoid collision is improved using neuro-fuzzy inferences. Approaching angles of the fish robot to an obstacle as well as the evident features such as obstacles’ sizes and shape angles are obtained through neural network training algorithms based on the scanned data. Experimental results show the successful path control of the fish robot without hitting on obstacles.     

Adaptive task assignment for multiple mobile robots via swarm intelligence approach

This paper describes an adaptive task assignment method for a team of fully distributed mobile robots with initially identical functionalities in unknown task environments. A hierarchical assignment architecture is established for each individual robot. In the higher hierarchy, we employ a simple self-reinforcement learning model inspired by the behavior of social insects to differentiate the initially identical robots into “specialists” of different task types, resulting in stable and flexible division of labor; on the other hand, in dealing with the cooperation problem of the robots engaged in the same type of task, Ant System algorithm is adopted to organize low-level task assignment. To avoid using a centralized component, a “local blackboard” communication mechanism is utilized for knowledge sharing. The proposed method allows the robot team members to adapt themselves to the unknown dynamic environments, respond flexibly to the environmental perturbations and robustly to the modifications in the team arising from mechanical failure. The effectiveness of the presented method is validated in two different task domains: a cooperative concurrent foraging task and a cooperative collection task.     

Obstacle avoidance control of redundant robots using variants of particle swarm optimization

Four variants of Particle Swarm Optimization (PSO) are proposed to solve the obstacle avoidance control problem of redundant robots. The study involved simulating the performance of a 5 degree-of-freedom (DOF) robot manipulator in an environment with static obstacle. The robot manipulator is required to move from one position to a desired goal position with minimum error while avoiding collision with obstacles in the workspace. The four variants of PSO are namely PSO-W, PSO-C, qPSO-W and qPSO-C where the latter two algorithms are hybrid version of the first two. The hybrid PSO is created by incorporating quadratic approximation operator (QA) alongside velocity update routine in updating particles' position. The computational results reveal that PSO-W yields better performance in terms of faster convergence and accuracy.     

Neural network approach to acquiring free-gait motion of quadruped robots in obstacle avoidance

In obstacle avoidance by a legged mobile robot, it is not necessary to avoid all of the obstacles by turning only, because it can climb or stride over some of them, depending on the obstacle configuration and the state of the robot, unlike a wheel-type or a crawler-type robot. It is thought that mobility efficiency to a destination is improved by crawling over or striding over obstacles. Moreover, if robots have many legs, like 4-legged or 6-legged types, then the robot's movement range is affected by the order of the swing leg. In this article a neural network (NN) is used to determine the action of a quadruped robot in an obstacle-avoiding situation by using information about the destination, the obstacle configuration, and the robot's self-state. To acquire a free gait in static walking, the order of the swing leg is realized using an alternative NN whose inputs are the amount of movement and the robot's self-state. The design parameters of the former NN are adjusted by a genetic algorithm (GA) off-line. This work was presented in part at the 9th International Symposium on Artificial Life and Robotics, Oita, Japan, January 28–30, 2004     

A hybrid tactile sensor-based obstacle overcoming method for hexapod walking robots

Intelligent Service Robotics - Walking robots are considered as a promising solution for locomotion across irregular or rough terrain. While wheeled or tracked robots require flat surface like...