Band 3, Heft 1

This paper presents a new approach to the multi-agent coverage path-planning problem. An efficient multi-robot coverage algorithm yields a coverage path for each robot, such that the union of all paths generates an almost full coverage of the terrain and the total coverage time is minimized. The proposed algorithm enables multiple robots with limited sensor capabilities to perform efficient coverage on a shared territory. Each robot is assigned to an exclusive route which enables it to carry out its tasks simultaneously, e.g., cleaning assigned floor area with minimal path overlapping. It is very difficult to cover all free space without visiting some locations more than once, but the occurrence of such events can be minimized with efficient algorithms. The proposed multi-robot coverage strategy directs a number of simple robots to cover an unknown area in a systematic manner. This is based on footprint data left by the randomized path-planning robots previously operated on that area. The developed path-planning algorithm has been applied to a simulated environment and robots to verify its effectiveness and performance in such an application.

We solve the problem of robot path planning using Dynamic Programming (DP) designed to perform well in case of a sudden path blockage. A conventional DP algorithm works well for real time scenarios only when the update frequency is high i.e. changes can be readily propagated. In case updates are costly, for a sudden blockage the robot continues moving along the wrong path or stands stationary. We propose a modified DP that has nodes with additional processing (called accelerating nodes) to enable different segments of the map to become informed about the blockage rapidly. We further quickly compute an alternative path in case of a blockage. Experimental results verify that usage of accelerating nodes makes the robot follow optimal paths in dynamic environments.

A modular robotic system and its fuzzy logic based controller are proposed for use in logging operations in forest environments with steep slopes. The Log-Carrying Robot (LCR) concept is composed of two modular wheeled robotic agents with individual wheel steering that connect to the ends of a log to a form a centrally controlled robot. A fuzzy controller specifies the desired direction of travel using four factors: the presence of obstacles, boundaries limiting the robot’s travel space, the heading of the goal position relative to the robot, and the slope of the terrain. The capabilities of the proposed controller are demonstrated in simulation using a rectangular robot with four individually actuated and steered wheels. Results indicate that the controller successfully steers the robot towards the goal position while avoiding obstacles using only eleven fuzzy rules. Additionally, the simple rules are shown to be effective at automatically compensating for sloped terrain by avoiding direct travel down hills, as well as adapting for various robot lengths.

Our long-term goal is to develop robots as social mediators that can support human-human communication in remote interaction scenarios. This paper explores the effects of an autonomous robot on human-human remote communication and studies participants’ preferences in comparison with a communication system not involving robots. We developed a platform for remote human-human communication in the context of a collaborative computer game. The exploratory study involved twenty pairs of participants who communicated using video conference software. Participants expressed more social cues when using the robot and sharing of their game experiences with each other. However, analyses of the interactions of the participants with each other and with the robot show that it is difficult for participants to familiarise themselves quickly with the robot while they can perform the same task more efficiently with conventional devices. These issues need to be carefully considered and addressed when designing human-human remote communication systems with robots as social mediators.

Central pattern generators (CPGs), with a basis is neurophysiological studies, are a type of neural network for the generation of rhythmic motion. While CPGs are being increasingly used in robot control, most applications are hand-tuned for a specific task and it is acknowledged in the field that generic methods and design principles for creating individual networks for a given task are lacking. This study presents an approach where the connectivity and oscillatory parameters of a CPG network are determined by an evolutionary algorithm with fitness evaluations in a realistic simulation with accurate physics. We apply this technique to a five-link planar walking mechanism to demonstrate its feasibility and performance. In addition, to see whether results from simulation can be acceptably transferred to real robot hardware, the best evolved CPG network is also tested on a real mechanism. Our results also confirm that the biologically inspired CPG model is well suited for legged locomotion, since a diverse manifestation of networks have been observed to succeed in fitness simulations during evolution.