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The global motion planning problem for tree climbing is challenging, as trees have an irregular and complex shape. To the best of the author’s knowledge, there is no related study that focuses on the global motion planning problem for tree-climbing robot. Aracil [17] proposed a motion planning method to allow the Parallel Climbing Robot to climb a trunk. However, this work merely discussed the local motion planning problem according to local information. There are many motion planning approaches for climbing in artificial structures, such as walls and glass windows [10, 11, 21]. However, these structures are different from trees, and the approaches are thus not suitable for tree-climbing problems.

The proposed tree gripper introduced in Chapter 4 is simple in control. It also implies that it has less controllability. In order to tackle the variant environment in limited controllability, the setting of the gripper should be optimized so as to maximize the fastening force in a wide range of surface curvature. The optimized items include the spine installation angle and the spring force distribution so as to provide optimal spine insert angle and direction of action force over a wide range of surface curvatures. On-tree experiments of the proposed mechanism will be presented at the end of this chapter to evaluate the optimization results.

Arboreal habitats are complex environments that pose numerous challenges to arboreal animals. These include climbing on cylindrical branches with variable diameters and inclined angles, moving in narrow spaces and facing obstacles. Through billions of years of evolution, many types of arboreal animals have evolved and developed diverse methods for moving around in complex arboreal environments.

There are several robots developed in the literature that are capable of climbing trees. Some of the robots are specifically designed for tree climbing while some of them are designed for moving in multiple terrains including trees. In this chapter, the details of these robots are introduced to provide a whole picture of the field of the tree-climbing technology.

Based on the limitation of the existing tree-climbing robots, one of the motivations of the work presented in this book is to develop a novel type of tree-climbing robot that can assist or replace humans in performing tasks on trees. The robot should be applicable to various pursuits such as harvesting, tree maintenance and observation of arboreal animals.

Fig. 6.1 shows the configuration of Treebot. In the notations, the superscripts r and f denote the front and rear gripper frame respectively. l f and l r represent the distance from the end of the continuum body to the center of the front and rear gripper respectively. h g denotes the distance between the base of the gripper and the continuum body. The reference frames for the front and rear grippers are also illustrated in the figure. The direction of a gripper refers to the direction along the positive z-axis, where a normal direction refers to the direction toward the positive x-axis.

Space robots are playing significant roles in the maintenance and repair of space station and satellites and other future space services. The motion trajectory planning is a key problem for accomplishing above missions. In order to obtain the high efficiency, safety motion trajectory of space robot, the motion trajectory should be optimized in advance. This paper describes the multi-objective optimization for optimizing the motion trajectory of space robot using a multi-objective particle swarm optimization (MOPSO). In this formulation, the multi-objective function is generated which includes some parameters such as motion time, dynamic disturbance, and jerk, and so on. Then a number of relative parameters can be simultaneously optimized through searching in the parameter space using MOPSO algorithms. The simulation results attest that MOPSO algorithm has satisfactory performance and real value in fact.

The manipulation of biological objects is a key technology necessary for many new applications in Bio-MEMS. In this paper, we will report on our preliminary experimental work in using an Ionic Conducting Polymer Film (ICPF) to develop a biological cell robotic gripper. The ability of ICPF actuators to give large deflection with small input voltage (∼5V) will allow many new applications to be developed, spanning from biology to underwater MEMS and artificial muscles. A laser micromachining process is introduced to fabricate arrays of ICPF griping devices, which can be potentially integrated onto a PCB board to develop a micro manipulation system. Individual multi-finger grippers with dimensions of 200μm × 200μm × 3000μm for each finger were realized. We will report on the design, fabrication procedures, and operating performance of these micro-grippers. Further development in the reduction of size of these actuators will enable effective control of underwater micro objects and lead to new frontiers in cellular manipulation.

This paper presents an approach for solving the driving load forecasting problem based on Cascade Neural Networks with node-decoupled extended Kalman Filtering (CNN-NDEKF). Because of the inherent advantages, hybrid electric vehicles (HEV) are being given more and more attention. The power control strategy of HEVs is the key technology which determines the HEV's efficiency and pollutive emission level. Since the extent of improvement involved with HEV power control strategies greatly depends on the future driving load forecasting, in this paper, we attempt to achieve driving load forecasting using CNN-NDEKF. Instead of forecasting the entire load sequence, we define 5 load levels by a fuzzy logic method and then we forecast the load level. Simulation study is given to illustrate the feasibility of the driving load forecasting approach.

This paper presents an intelligent vehicle security system for handling the vehicle thefts problem under the framework of capturing and analyzing dynamic human behaviors. Since human driving skill is a kind of dynamic biometrical feature which is complex and difficult to imitate, it is unique and more secure than static features such as password, fingerprint and face. By utilizing this dynamic property we focus on the research ideal of classifying the drivers into authorized ones and unauthorized ones by modeling their individual driving performance. Firstly, we develop an experimental system architecture. We collect the data of steering, acceleration and braking directly from human driving behaviors as inputs to the system, which aims to achieve better robustness and efficiency. Then, we use fast fourier transform (FFT), principal component analysis (PCA) and independent component analysis (ICA) for data reduction. The features extracted are sent to support vector machine (SVM) for learning and recognition. In the next step, we embed the intelligent classifier into a security system to identify the authorized drivers in response to the real time driving performances. Finally, the experimental results verify that the proposed method is valid and useful against the vehicle thefts problem with a success rate of around 80%.

Automatical operation of space robots is a challenging and ultimate goal of space servicing. In this paper, we present a novel approach for tracking and catching operation of space robots based on learning and transferring human control strategies (HCS). We firstly use an efficient support vector machine (SVM) to parameterize the model of HCS, and then develop a new SVM-based leaning structure to improve HCS in tracking and capturing control. The approach is fundamentally valuable in dealing with some problems such as small sample data and local minima, which makes it efficient in modeling, understanding and transferring its learning process. The simulation results demonstrate that the proposed method is useful and feasible in tracking trajectory and catching objects autonomously.

Optimal design for urban mass transit network is the precondition and basis to establish an effective public transportation system. Transit network optimization and headway optimization are two of the most important issues to be dealt with. In this paper, a transit network optimization model is firstly proposed to maximize the nonstop passenger flow. Moreover, this paper puts forwards the optimization model of headways for all the transit routes in the optimized network. Since all the two models can be boiled down to the NP-hard problem, two kinds of evolutionary algorithms, i.e., ant colony algorithm and improved genetic algorithm are introduced to solve the problems respectively. Finally, a case study in a typical city is introduced to explain the validity of the proposed methods.