Before unmanned aircraft can fly safely in civil airspace, robust airborne collision avoidance systems must be developed. Instead of hand-crafting a collision avoidance algorithm for every combination of sensor and aircraft configuration, we investigate the automatic generation of collision avoidance algorithms given models of aircraft dynamics, sensor performance, and intruder behavior. By formulating the problem of collision avoidance as a Markov Decision Process (MDP) for sensors that provide precise localization of the intruder aircraft, or a Partially Observable Markov Decision Process (POMDP) for sensors that have positional uncertainty or limited field-of-view constraints, generic MDP/POMDP solvers can be used to generate avoidance strategies that optimize a cost function that balances flight-plan deviation with collision. Experimental results demonstrate the suitability of such an approach using four different sensor modalities and a parametric aircraft performance model.

Small screen and wearable devices play a key role in most of our daily tasks and activities. However, depending on the context, users can easily experience situationally induced impairments and disabilities (SIIDs). Previous studies have defined SIIDs as a new type of impairment in which an able-bodied user’s behaviour is impaired by the context including the characteristics of a device and the environment. This article systematically reviews the empirical studies on the effect of context on SIIDs. In particular, this review aims to answer the following two research questions: Which contextual factors have been examined in the literature that can cause SIIDs and how different contextual factors affect small screen and wearable device users’ performance. This article systematically reviews 187 publications under a framework that has five factors for context analysis: physical, temporal, social, task, and technical contexts. This review shows that a significant amount of empirical studies have been conducted focusing on some factors such as mobility but there still are some factors such as social factors that need to be further considered for SIIDs. Finally, some factors have shown to have significant impact on users’ performance such as multitasking but not all factors has been empirically demonstrated to have an effect on users’ performance.

There are many methods that address navigation and path planning for mobile robots with non-holonomic motion constraints using clever techniques and exploiting application-specific data, but it is always better not to have any such constraints at all. In this document we re-examine the capabilities of some popular driving mechanisms from a different perspective and describe a method to obtain holonomic motion using those mechanisms. The main idea is to not concentrate on the center of the driving mechanism (which is the usual choice) as the reference point for our calculations, but to select another point whose motion in the x-y plane is not constrained in any direction, and which is also a logical and useful substitute for the center. In addition to the derivation of the forward and inverse kinematics equations for the new reference point, we also explain how to further simplify the design of a controller which uses the described method to compute motion commands for the robot. In order to illustrate the ideas, we present graphs that were plotted using the actual parameter values for a synchronous-drive research robot.