Advances in Robot Navigation

Robot navigation includes different interrelated activities such as perception - obtaining and interpreting sensory information; exploration - the strategy that guides the robot to select the next direction to go; mapping - the construction of a spatial representation by using the sensory information perceived; localization - the strategy to estimate the robot position within the spatial map; path planning - the strategy to find a path towards a goal location being optimal or not; and path execution, where motor actions are determined and adapted to environmental changes. This book integrates results from the research work of authors all over the world, addressing the abovementioned activities and analyzing the critical implications of dealing with dynamic environments. Different solutions providing adaptive navigation are taken from nature inspiration, and diverse applications are described in the context of an important field of study: social robotics

Technical Report: A Contact-aware Feedback CPG System for Learning-based Locomotion Control in a Soft Snake Robot

Integrating contact-awareness into a soft snake robot and efficiently controlling its locomotion in response to contact information present significant challenges. This paper aims to solve contact-aware locomotion problem of a soft snake robot through developing bio-inspired contact-aware locomotion controllers. To provide effective contact information for the controllers, we develop a scale covered sensor structure mimicking natural snakes' \textit{scale sensilla}. In the design of control framework, our core contribution is the development of a novel sensory feedback mechanism of the Matsuoka central pattern generator (CPG) network. This mechanism allows the Matsuoka CPG system to work like a "spine cord" in the whole contact-aware control scheme, which simultaneously takes the stimuli including tonic input signals from the "brain" (a goal-tracking locomotion controller) and sensory feedback signals from the "reflex arc" (the contact reactive controller), and generate rhythmic signals to effectively actuate the soft snake robot to slither through densely allocated obstacles. In the design of the "reflex arc", we develop two types of reactive controllers -- 1) a reinforcement learning (RL) sensor regulator that learns to manipulate the sensory feedback inputs of the CPG system, and 2) a local reflexive sensor-CPG network that directly connects sensor readings and the CPG's feedback inputs in a special topology. These two reactive controllers respectively facilitate two different contact-aware locomotion control schemes. The two control schemes are tested and evaluated in the soft snake robot, showing promising performance in the contact-aware locomotion tasks. The experimental results also further verify the benefit of Matsuoka CPG system in bio-inspired robot controller design.Comment: 17 pages, 19 figure

Retina-Based Pipe-Like Object Tracking Implemented Through Spiking Neural Network on a Snake Robot

Vision based-target tracking ability is crucial to bio-inspired snake robots for exploring unknown environments. However, it is difficult for the traditional vision modules of snake robots to overcome the image blur resulting from periodic swings. A promising approach is to use a neuromorphic vision sensor (NVS), which mimics the biological retina to detect a target at a higher temporal frequency and in a wider dynamic range. In this study, an NVS and a spiking neural network (SNN) were performed on a snake robot for the first time to achieve pipe-like object tracking. An SNN based on Hough Transform was designed to detect a target with an asynchronous event stream fed by the NVS. Combining the state of snake motion analyzed by the joint position sensors, a tracking framework was proposed. The experimental results obtained from the simulator demonstrated the validity of our framework and the autonomous locomotion ability of our snake robot. Comparing the performances of the SNN model on CPUs and on GPUs, respectively, the SNN model showed the best performance on a GPU under a simplified and synchronous update rule while it possessed higher precision on a CPU in an asynchronous way

Neuromorphic Vision Based Multivehicle Detection and Tracking for Intelligent Transportation System

Neuromorphic vision sensor is a new passive sensing modality and a frameless sensor with a number of advantages over traditional cameras. Instead of wastefully sending entire images at fixed frame rate, neuromorphic vision sensor only transmits the local pixel-level changes caused by the movement in a scene"jats:italic" at the time they occur"/jats:italic". This results in advantageous characteristics, in terms of low energy consumption, high dynamic range, sparse event stream, and low response latency, which can be very useful in intelligent perception systems for modern intelligent transportation system (ITS) that requires efficient wireless data communication and low power embedded computing resources. In this paper, we propose the first neuromorphic vision based multivehicle detection and tracking system in ITS. The performance of the system is evaluated with a dataset recorded by a neuromorphic vision sensor mounted on a highway bridge. We performed a preliminary multivehicle tracking-by-clustering study using three classical clustering approaches and four tracking approaches. Our experiment results indicate that, by making full use of the low latency and sparse event stream, we could easily integrate an online tracking-by-clustering system running at a high frame rate, which far exceeds the real-time capabilities of traditional frame-based cameras. If the accuracy is prioritized, the tracking task can also be performed robustly at a relatively high rate with different combinations of algorithms. We also provide our dataset and evaluation approaches serving as the first neuromorphic benchmark in ITS and hopefully can motivate further research on neuromorphic vision sensors for ITS solutions. Document type: Articl

The emergence of active perception - seeking conceptual foundations

The aim of this thesis is to explain the emergence of active perception. It takes an interdisciplinary approach, by providing the necessary conceptual foundations for active perception research - the key notions that bridge the conceptual gaps remaining in understanding emergent behaviours of active perception in the context of robotic implementations. On the one hand, the autonomous agent approach to mobile robotics claims that perception is active. On the other hand, while explanations of emergence have been extensively pursued in Artificial Life, these explanations have not yet successfully accounted for active perception.The main question dealt with in this thesis is how active perception systems, as behaviour -based autonomous systems, are capable of providing relatively optimal perceptual guidance in response to environmental challenges, which are somewhat unpredictable. The answer is: task -level emergence on grounds of complicatedly combined computational strategies, but this notion needs further explanation.To study the computational strategies undertaken in active perception re- search, the thesis surveys twelve implementations. On the basis of the surveyed implementations, discussions in this thesis show that the perceptual task executed in support of bodily actions does not arise from the intentionality of a homuncu- lus, but is identified automatically on the basis of the dynamic small mod- ules of particular robotic architectures. The identified tasks are accomplished by quasi -functional modules and quasi- action modules, which maintain transformations of perceptual inputs, compute critical variables, and provide guidance of sensory -motor movements to the most relevant positions for fetching further needed information. Given the nature of these modules, active perception emerges in a different fashion from the global behaviour seen in other autonomous agent research.The quasi- functional modules and quasi- action modules cooperate by estimating the internal cohesion of various sources of information in support of the envisaged task. Specifically, such modules basically reflect various computational facilities for a species to single out the most important characteristics of its ecological niche. These facilities help to achieve internal cohesion, by maintaining a stepwise evaluation over the previously computed information, the required task, and the most relevant features presented in the environment.Apart from the above exposition of active perception, the process of task - level emergence is understood with certain principles extracted from four models of life origin. First, the fundamental structure of active perception is identified as the stepwise computation. Second, stepwise computation is promoted from baseline to elaborate patterns, i.e. from a simple system to a combinatory system. Third, a core requirement for all stepwise computational processes is the comparison between collected and needed information in order to insure the contribution to the required task. Interestingly, this point indicates that active perception has an inherent pragmatist dimension.The understanding of emergence in the present thesis goes beyond the distinc- tion between external processes and internal representations, which some current philosophers argue is required to explain emergence. The additional factors are links of various knowledge sources, in which the role of conceptual foundations is two -fold. On the one hand, those conceptual foundations elucidate how various knowledge sources can be linked. On the other, they make possible an interdisci- plinary view of emergence. Given this two -fold role, this thesis shows the unity of task -level emergence. Thus, the thesis demonstrates a cooperation between sci- ence and philosophy for the purpose of understanding the integrity of emergent cognitive phenomena

Wireless mosaic eyes based robot path planning and control. Autonomous robot navigation using environment intelligence with distributed vision sensors.

As an attempt to steer away from developing an autonomous robot with complex centralised intelligence, this thesis proposes an intelligent environment infrastructure where intelligences are distributed in the environment through collaborative vision sensors mounted in a physical architecture, forming a wireless sensor network, to enable the navigation of unintelligent robots within that physical architecture. The aim is to avoid the bottleneck of centralised robot intelligence that hinders the application and exploitation of autonomous robot. A bio-mimetic snake algorithm is proposed to coordinate the distributed vision sensors for the generation of a collision free Reference-snake (R-snake) path during the path planning process. By following the R-snake path, a novel Accompanied snake (A-snake) method that complies with the robot's nonholonomic constraints for trajectory generation and motion control is introduced to generate real time robot motion commands to navigate the robot from its current position to the target position. A rolling window optimisation mechanism subject to control input saturation constraints is carried out for time-optimal control along the A-snake. A comprehensive simulation software and a practical distributed intelligent environment with vision sensors mounted on a building ceiling are developed. All the algorithms proposed in this thesis are first verified by the simulation and then implemented in the practical intelligent environment. A model car with less on-board intelligence is successfully controlled by the distributed vision sensors and demonstrated superior mobility