Path planning for sensor data collecting mobile robot

A mobile robot employed for data collection is faced with the problem of travelling from an initial location to a final location while maintaining as close a distance as possible to all the sensors at a given time in the journey. Here we employ optimal control ideas in forming the necessary control commands for such a robot resulting not only the necessary acceleration commands for the underlying robot, but also the resulting trajectory. This approach can also be easily extended for the case of producing the optimal trajectory for an ariel vehicle used for data collection from indiscriminately scattered ad-hoc sensors located on the ground. We demonstrate the implementation of our algorithm using a Pioneer 3-AT robot.

Solution to robotic landmine detection through use of path planning and motor control

Low cost robotic detectors are a promising new approach to combat the disturbing landmine crisis. In this paper a low-cost robotic solution is proposed, we present several control techniques used to improve the precision of the robotic motion. A P and PD controller is applied, and it is concluded that a cascaded control system provides a more stable and accurate response. Two search patterns for landmine detection are considered, rectangular and spiral, these are used to analyse the effectiveness of the control system.

An adjustable force field for multiple robot mission and path planning

Mission and path planning for multiple robots in dynamic environments is required when multiple mobile robots or unmanned vehicles are used for geographically distributed tasks. Assigning tasks and paths for robots for cooperatively accomplishing a mission of reaching to number of target points are addressed in this paper. The methodology that is proposed is based on using an adjustable force field which is suitable for dynamic environment. From the force field analysis, the decisions to assign tasks for each robot are then made. The force field is also used to plan a collision free path for each robot. Adjustable weights for the force field model are proposed to satisfy the constraints of the motion. In this research, the constraints are the cooperation of the robots, the precedence between the targets and between robots, and the discrimination between different obstacles. Two simulations for mission and path planning in 2D and 3D dynamic spaces with multiple robots are presented based on the proposed adjustable force filed. The result of the mission and path planning for three robots cooperatively doing eight target points are shown.

Path planning for CNC machines considering centripetal acceleration and jerk

In planning an s-curve speed profile for a computer numerical control (CNC) machine, centripetal acceleration and its derivative have to be considered. In a CNC machine, these quantities dictate how much voltage and current should be applied to servo motor windings. In this paper, the necessity of considering centripetal jerk in speed profile generation especially in the look-ahead mode is explained. It is demonstrated that the magnitude of centripetal jerk is proportional to the curvature derivative of the path known as "sharpness". It is also explained that a proper limited jerk motion is only possible when a G2-continuous machining path is planned. Then using a simplified mathematical representation of clothoids, a novel method for approximating a given path with a sequence of clothoid segments is proposed. Using this method, a semi-parallel G2-continuous path with adjustable deviation from the original shape for a sample machining contour is generated. Maximum permissible feed rate for the generated path is also calculated.

Dynamic path determination policy for distributed multimedia content adaptation

Multimedia content adaptation allows the ever increasing variety of handheld devices such as Smartphones to access distributed rich media resources available on the Internet today. Path planning and determination is a fundamental problem in enhancing performance of distributed multimedia content adaptation systems. Most of the existing path determination mechanisms use static path determination criteria based solely on associating a path with a single behavior aggregate score. However, some criteria such as availability are best represented using different functionality rather than being accumulated into the aggregate score. Moreover, since selection criteria have different behavior towards the score, this principle need to be considered. In this paper, we propose a dynamic multi-criteria path determination policy that selects an optimal path to the content adaptation services that best meet the user preferences and QoS requirements. The performance of the proposed approach is studied in terms of score’s fairness and reliability under different variations. The results indicate that the proposed policy performs substantially better than the baseline policy.

Predictive motion planning for AUVs subject to strong time-varying currents and forecasting uncertainties

This paper presents a novel path planning method for minimizing the energy consumption of an autonomous underwater vehicle subjected to time varying ocean disturbances and forecast model uncertainty. The algorithm determines 4-Dimensional path candidates using Nonlinear Robust Model Predictive Control (NRMPC) and solutions optimised using A∗-like algorithms. Vehicle performance limits are incorporated into the algorithm with disturbances represented as spatial and temporally varying ocean currents with a bounded uncertainty in their predictions. The proposed algorithm is demonstrated through simulations using a 4-Dimensional, spatially distributed time-series predictive ocean current model. Results show the combined NRMPC and A∗ approach is capable of generating energy-efficient paths which are resistant to both dynamic disturbances and ocean model uncertainty.

Simulating autonomous robot teams with Microsoft robotics studio

This paper presents an application of Microsoft Robotics Studio (MSRS) in which a team of six four wheel drive, ground based robots explore and map simulated terrain. The user has the ability to modify the terrain and assign destination objectives to the team while the simulation is running. The terrain is initially generated using a gray scale image, in which the intensity of each pixel in the image gives an altitude datum. The robots start with no knowledge of their surroundings, and map the terrain as they attempt to reach user-defined target objectives. The mapping process simulates the use of common sensory hardware to determine datum points, including provision for field of view, detection range, and measurement accuracy. If traversal of a mapped area is indicated by the users’ targeting commands, path planning heuristics developed for MSRS by the author in earlier work are used to determine an efficient series of waypoints to reach the objective. Mutability of terrain is also explored- the user is able to modify the terrain without stopping the simulation. This forces the robots to adapt to changing environmental conditions, and permits analysis of the robustness of mapping algorithms used when faced with a changing world.

Simulation-based learning for control of complex conveyor networks

This research investigated the problem of path planning in complex conveyor networks. A reinforcement learning approach was applied to derive a control strategy for routing traffic. The derived strategy was verified in real world systems and was found to improve network performance by prioritising traffic flows and balancing network load.

Simulating crowd phenomena in African markets

Crowd simulation is an important feature in the computer graphics field. Typical implementations simulate battle scenes, emergency situations, safety issues or add content to virtual environments. The problem stated in this paper falls in the last category. We present a crowd simulation behavioural model which allows us to simulate identified phenomena in popular local African markets such as narrow street flows and crowd formation around street performances. We propose a three-tier architecture model enable to produce intentions, perform path planning and control movement. We demonstrate that this approach produces the desired behaviour associated with crowds in an African market, which includes navigation, flow formation and circle creation.

Extracting common subtrees from decision trees

This paper explores an efficient technique for the extraction of common subtrees in decision trees. The method is based on a Suffix Tree string matching process and the algorithm is applied to the problem of finding common decision rules in path planning.

Solving the median shortest path problem in the planning and design of urban transportation networks using a vector labeling algorithm

This paper proposes an alternative algorithm to solve the median shortest path problem (MSPP) in the planning and design of urban transportation networks. The proposed vector labeling algorithm is based on the labeling of each node in terms of a multiple and conflicting vector of objectives which deletes cyclic, infeasible and extreme-dominated paths in the criteria space imposing cyclic break (CB), path cost constraint (PCC) and access cost parameter (ACP) respectively. The output of the algorithm is a set of Pareto optimal paths (POP) with an objective vector from predetermined origin to destination nodes. Thus, this paper formulates an algorithm to identify a non-inferior solution set of POP based on a non-dominated set of objective vectors that leaves the ultimate decision to decision-makers. A numerical experiment is conducted using an artificial transportation network in order to validate and compare results. Sensitivity analysis has shown that the proposed algorithm is more efficient and advantageous over existing solutions in terms of computing execution time and memory space used.

Continuous professional learning through school based strategic planning

There is ample evidence that in many countries school science is in difficulty, with declining student attitudes and uptake of science. This presentation argues that a key to addressing the problem lies in transforming teachers’ classroom practice, and that pedagogical innovation is best supported within a school context. Evidence for effective change will draw on the School Innovation in Science (SIS) initiative in Victoria, which has developed and evaluated a model to improve science teaching and learning across a school system. The model involves a framework for describing effective teaching and learning, and a strategy that allows schools flexibility to develop their practice to suit local conditions and to maintain ownership of the change process. SIS has proved successful in improving science teaching and learning in primary and secondary schools. Experience from SIS and related projects, from a national Australian science and literacy project, and from system wide science initiatives in Europe, will be used to explore the factors that affect the success and the path of innovation in schools.

Keeping all students on the learning path

A powerful notion to guide thinking about whole-class mathematics teaching is Vygotsky’s zone of proximal development (ZPD). Our research with primary and secondary teachers over the last six years has identified roles of teachers in relation to the ZPD, and ways of overcoming some typical barriers to students’ movement through their zones. Methods have included focus groups of experts, video analysis of classroom interactions, classroom observation, and analysis of lesson plans and teachers’ reflections teaching processes their outcomes. The research has involved the gradual development, trailing, evaluation, and adjustment of a six-component model for planning and teaching mathematics. The focus of this paper is on the use of one of its components, “differentiated learning trajectories”.

Continuous professional learning through school based strategic planning

There is ample evidence that in many countries school science is in difficulty, with declining student attitudes and uptake of science. This presentation argues that a key to addressing the problem lies in transforming teachers’ classroom practice, and that pedagogical innovation is best supported within a school context. Evidence for effective change will draw on the School Innovation in Science (SIS) initiative in Victoria, which has developed and evaluated a model to improve science teaching and learning across a school system. The model involves a framework for describing effective teaching and learning, and a strategy that allows schools flexibility to develop their practice to suit local conditions and to maintain ownership of the change process. SIS has proved successful in improving science teaching and learning in primary and secondary schools. Experience from SIS and related projects, from a national Australian science and literacy project, and from system wide science initiatives in Europe, will be used to explore the factors that affect the success and the path of innovation in schools.