An immunological approach to mobile robot reactive navigation

Luh, Guan-Chun; Liu, Weiwen

doi:10.1016/j.asoc.2006.10.009

Cited by 38 publications

(24 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach is expensive, however, requiring additional on-board memory capacity, supplementary data processing capabilities, or both. "Reactive navigation" is an alternative mechanism that allows effective navigation, requiring the robot to interact only with its immediate environment, and to maintain only a state memory (7)(8)(9). However, without some form of memory, goal-oriented autonomous robots using reactive navigation have difficulty navigating toward a goal in complex environments and often become trapped by obstacles (8)(9)(10).…”

mentioning

confidence: 99%

“…"Reactive navigation" is an alternative mechanism that allows effective navigation, requiring the robot to interact only with its immediate environment, and to maintain only a state memory (7)(8)(9). However, without some form of memory, goal-oriented autonomous robots using reactive navigation have difficulty navigating toward a goal in complex environments and often become trapped by obstacles (8)(9)(10). Providing the robots with a spatial memory relating to the local environment only has proved sufficient for the autonomous units to efficiently solve complex navigational challenges (8).…”

mentioning

confidence: 99%

See 1 more Smart Citation

Slime mold uses an externalized spatial “memory” to navigate in complex environments

Reid

Latty

Dussutour

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

191

173

View full text Add to dashboard Cite

Spatial memory enhances an organism's navigational ability. Memory typically resides within the brain, but what if an organism has no brain? We show that the brainless slime mold Physarum polycephalum constructs a form of spatial memory by avoiding areas it has previously explored. This mechanism allows the slime mold to solve the U-shaped trap problem-a classic test of autonomous navigational ability commonly used in robotics-requiring the slime mold to reach a chemoattractive goal behind a U-shaped barrier. Drawn into the trap, the organism must rely on other methods than gradient-following to escape and reach the goal. Our data show that spatial memory enhances the organism's ability to navigate in complex environments. We provide a unique demonstration of a spatial memory system in a nonneuronal organism, supporting the theory that an externalized spatial memory may be the functional precursor to the internal memory of higher organisms. extracellular slime | protist | reactive navigation | amoeboid organism

show abstract

mentioning

confidence: 99%

mentioning

confidence: 99%

Slime mold uses an externalized spatial “memory” to navigate in complex environments

Reid

Latty

Dussutour

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

191

173

View full text Add to dashboard Cite

show abstract

“…Timmis and Knight [28], de Castro and von Zuben [47], and Dasgupta [48] developed basic models of artificial immune systems which are the sources of current AIS algorithms. Immune-inspired models have been applied on wide variety of research fields ranging from pattern recognition, such as classification and clustering, anomaly detection [49,50], and optimization [51,52], to robotics [53,54] and image processing [55,56]. The key characteristics of immune systems are learning, adaptability, memory mechanisms, and self-organization which are desirable to inspired algorithms.…”

Section: Immune Systemsmentioning

confidence: 99%

Credit Risk Prediction Using Fuzzy Immune Learning

Kamalloo

Abadeh

2014

Advances in Fuzzy Systems

View full text Add to dashboard Cite

The use of credit has grown considerably in recent years. Banks and financial institutions confront credit risks to conduct their business. Good management of these risks is a key factor to increase profitability. Therefore, every bank needs to predict the credit risks of its customers. Credit risk prediction has been widely studied in the field of data mining as a classification problem. This paper proposes a new classifier using immune principles and fuzzy rules to predict quality factors of individuals in banks. The proposed model is combined with fuzzy pattern classification to extract accurate fuzzy if-then rules. In our proposed model, we have used immune memory to remember good B cells during the cloning process. We have designed two forms of memory: simple memory and k-layer memory. Two real world credit data sets in UCI machine learning repository are selected as experimental data to show the accuracy of the proposed classifier. We compare the performance of our immune-based learning system with results obtained by several well-known classifiers. Results indicate that the proposed immune-based classification system is accurate in detecting credit risks.

show abstract

“…Figure 3 shows several results of formation path planning with obstacle avoidance. Initial positions of leader robot, and two follower robots are specified as (4,6) m, (1,5) m, and (10,2) m. The position of the goal is specified as (15,15) m. Positions of obstacles are specified as (8,2) m and (9,10) m. Size of the obstacle is specified as 0.5 m × 0.5 m. The desired formation shape is an equilateral triangle, and the desired distance is L d = 3 m. Figure 3(a) indicates the formation path planning trajectories with obstacle avoidance. Obstacles are on trajectories of follower robot 2 and leader robot.…”

Section: Formation With Obstacle Avoidancementioning

confidence: 99%

“…Luh and Liu [15] proposed a reactive immune network for mobile robot navigation in unknown environments. Adaptive virtual target method is integrated to solve the problem of local minima.…”

Section: Introductionmentioning

confidence: 99%