The potential field method is widely used for autonomous mobile robot path planning due to its elegant mathematical analysis and simplicity. However, most researches have been focused on solving the motion planning problem in a stationary environment where both targets and obstacles are stationary. This paper proposes a new potential field method for motion planning of mobile robots in a dynamic environment where the target and the obstacles are moving. Firstly, the new potential function and the corresponding virtual force are defined. Then, the problem of local minima is discussed. Finally, extensive computer simulations and hardware experiments are carried out to demonstrate the effectiveness of the dynamic motion planning schemes based on the new potential field method.

Dynamic Motion Planning for Mobile Robots Using Potential Field Method

This paper introduces the potential-function based method for secondary (as well as tertiary) control of a microgrid, in both islanded and grid-connected modes. A potential function is defined for each controllable unit of the microgrid such that the minimum of the potential function corresponds to the control goal. The dynamic set points are updated, using communication within the microgrid. The proposed potential function method is applied for the secondary voltage control of two microgrids with single and multiple feeders. Both islanded and grid-connected modes are investigated. The studies are conducted in the time-domain, using the PSCAD/EMTDC software environment. The study results demonstrate feasibility of the proposed potential function method and viability of the secondary voltage control method for a microgrid.

Potential-Function Based Control of a Microgrid in Islanded and Grid-Connected Modes

Intelligent techniques foster the dissemination of new discoveries and novel technologies that advance the ability of robots to assist and support humans. The human-centered intelligent robot has become an important research field that spans all of the robot capabilities including navigation, intelligent control, pattern recognition and human-robot interaction. This paper focuses on the recent achievements and presents a survey of existing works on human-centered robots. Furthermore, we provide a comprehensive survey of the recent development of the human-centered intelligent robot and discuss the issues and challenges in the field.

A survey of human-centered intelligent robots: issues and challenges

A constructive method is presented to design cooperative controllers that force a group of N unicycle-type mobile robots with limited sensing ranges to perform desired formation tracking and guarantee no collisions between the robots. Physical dimensions and dynamics of the robots are also considered in the control design. Smooth and p times differential bump functions are introduced and incorporated into novel potential functions to design a formation tracking control system. Despite the robot limited sensing ranges, no switchings are needed to solve the collision avoidance problem. Simulations illustrate the results.

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Formation Tracking Control of Unicycle-Type Mobile Robots With Limited Sensing Ranges

Unmanned aerial vehicle (UAV) systems are one of the most rapidly developing, highest level and most practical applied unmanned aerial systems. Collision avoidance and trajectory planning are the core areas of any UAV system. However, there are theoretical and practical problems associated with the existing methods. To manage these problems, this paper presents an optimized artificial potential field (APF) algorithm for multi-UAV operation in 3-D dynamic space. The classic APF algorithm is restricted to single UAV trajectory planning and usually fails to guarantee the avoidance of collisions. To overcome this challenge, a method is proposed with a distance factor and jump strategy to solve common problems, such as unreachable targets, and ensure that the UAV will not collide with any obstacles. The method considers the UAV companions as dynamic obstacles to realize collaborative trajectory planning. Furthermore, the jitter problem is solved using the dynamic step adjustment method. Several resolution scenarios are illustrated. The method has been validated in quantitative test simulation models and satisfactory results were obtained in a simulated urban environment.

J. Sun

Papers

New Potential Functions for Mobile Robot Path Planning