Integrated path planning and dynamic steering control for autonomous vehicles Krogh, B. Thorpe, C. This paper appears in: Robotics and Automation. Proceedings. 1986 IEEE International Conference on Publication Date: Apr 1986 Volume: 3, On page(s): 1664- 1669 Current Version Published: 2003-01-06 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1087444 A method is presented for combining two previously proposed algorithms for path-planning and dynamic steering control into a computationally feasible scheme for real-time feedback control of autonomous vehicles in uncertain environments. In the proposed approach to vehicle guidance and control, Path Relaxation is used to compute critical points along a globally desirable path using a priori information and sensor data. Generalized potential fields are then used for local feedback control to drive the vehicle along a collision-free path using the critical points as subgoals. Simulation results are presented to demonstrate the control scheme. ---------------------------------------------------------------------------------- Exact robot navigation by means of potential functions: Some topological considerations Koditschek, D. Yale University, New Haven, CT, USA; This paper appears in: Robotics and Automation. Proceedings. 1987 IEEE International Conference on Publication Date: Mar 1987 Volume: 4, On page(s): 1- 6 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1088038 The limits in global navigation capability of potential function based robot control algorithms are explored. Elementary tools of algebraic and differential topology are used to advance arguments suggesting the existence of potential functions over a bounded planar region with arbitrary fixed obstacles possessed of a unique local minimum. A class of such potential functions is constructed for certain cases of a planar disk region with an arbitrary number of smaller disks removed. ---------------------------------------------------------------------------------- Swarm aggregations using artificial potentials and sliding-mode control Gazi, V. Dept. of Electr. & Electron. Eng., TOBB Univ. of Econ. & Technol., Ankara, Turkey; This paper appears in: Robotics, IEEE Transactions on Publication Date: Dec. 2005 Volume: 21, Issue: 6 On page(s): 1208- 1214 ISSN: 1552-3098 INSPEC Accession Number: 8676039 Digital Object Identifier: 10.1109/TRO.2005.853487 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1549948 In this paper, we consider a control strategy of multiagent systems, or simply, swarms, based on artificial potential functions and the sliding-mode control technique. First, we briefly discuss a "kinematic" swarm model in n-dimensional space introduced in an earlier paper. In that model, the interindividual interactions are based on artificial potential functions, and the motion of the individuals is along the negative gradient of the combined potential. After that, we consider a general model for vehicle dynamics of each agent (swarm member), and use sliding-mode control theory to force their motion to obey the dynamics of the kinematic model. In this context, the results for the initial model serve as a "proof of concept" for multiagent coordination and control (swarm aggregation), whereas the present results serve as a possible implementation method for engineering swarms with given vehicle dynamics. The presented control scheme is robust with respect to disturbances and system uncertainties. ---------------------------------------------------------------------------------- A hybrid of genetic algorithm and particle swarm optimization for recurrent network design Chia-Feng Juang Dept. of Electr. Eng., Nat. Chung Hsing Univ., Taichung, Taiwan; This paper appears in: Systems, Man, and Cybernetics, Part B, IEEE Transactions on Publication Date: April 2004 Volume: 34, Issue: 2 On page(s): 997- 1006 ISSN: 1083-4419 INSPEC Accession Number: 8111511 Digital Object Identifier: 10.1109/TSMCB.2003.818557 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1275532 An evolutionary recurrent network which automates the design of recurrent neural/fuzzy networks using a new evolutionary learning algorithm is proposed in this paper. This new evolutionary learning algorithm is based on a hybrid of genetic algorithm (GA) and particle swarm optimization (PSO), and is thus called HGAPSO. In HGAPSO, individuals in a new generation are created, not only by crossover and mutation operation as in GA, but also by PSO. The concept of elite strategy is adopted in HGAPSO, where the upper-half of the best-performing individuals in a population are regarded as elites. However, instead of being reproduced directly to the next generation, these elites are first enhanced. The group constituted by the elites is regarded as a swarm, and each elite corresponds to a particle within it. In this regard, the elites are enhanced by PSO, an operation which mimics the maturing phenomenon in nature. These enhanced elites constitute half of the population in the new generation, whereas the other half is generated by performing crossover and mutation operation on these enhanced elites. HGAPSO is applied to recurrent neural/fuzzy network design as follows. For recurrent neural network, a fully connected recurrent neural network is designed and applied to a temporal sequence production problem. For recurrent fuzzy network design, a Takagi-Sugeno-Kang-type recurrent fuzzy network is designed and applied to dynamic plant control. The performance of HGAPSO is compared to both GA and PSO in these recurrent networks design problems, demonstrating its superiority. ---------------------------------------------------------------------------------- Decentralized Control of Autonomous Swarm Systems Using Artificial Potential Functions: Analytical Design Guidelines Dong Hun Kim, Hua Wang and Seiichi Shin Journal Journal of Intelligent and Robotic Systems Publisher Springer Netherlands ISSN 0921-0296 (Print) 1573-0409 (Online) Issue Volume 45, Number 4 / April, 2006 DOI 10.1007/s10846-006-9050-8 Pages 369-394 http://www.springerlink.com/content/t6g013162p754p70/ This paper presents a framework for decentralized control of self-organizing swarm systems based on the artificial potential functions (APFs). In this scheme, multiple agents in a swarm self-organize to flock and achieve formation control through attractive and repulsive forces among themselves using APFs. In particular, this paper presents a set of analytical guidelines for designing potential functions to avoid local minima for a number of representative scenarios. Specifically the following cases are addressed: 1) A non-reachable goal problem (a case that the potential of the goal is overwhelmed by the potential of an obstacle, 2) an obstacle collision problem (a case that the potential of the obstacle is overwhelmed by the potential of the goal), 3) an obstacle collision problem in swarm (a case that the potential of the obstacle is overwhelmed by potential of other robots in a group formation) and 4) an inter-robot collision problem (a case that the potential of the robot in a formation is overwhelmed by potential of the goal). The simulation results showed that the proposed scheme can effectively construct a self-organized swarm system with the capability of group formation, navigation and migration in the presence of obstacles.