The Swedish Institute of Computer Science Parasitic Interference Directional Antenna (SPIDA) is an electrically switched directional antenna that uses switched beamforming techniques to shape the antenna radiation pattern focusing the transmitted power in a given direction, increasing the maximum gain, and simultaneously reducing interference in other directions. This work extends the use of the SPIDA antenna, showing that using multiple director elements results in an improved performance in terms of maximum gain, narrower Half Power Beamwidth (HPBW), and a lower module of the S11 parameter. Measurements show that using three directors improves the maximum gain about 1.4 dB (6.8 dBi for the single director element antenna against 8.2 dBi for the antenna with three directors); the input impedance matching was also improved, obtaining a module of S11 parameter of -9.8 dB at the central frequency ( fc = 2.4525 GHz) against -7.5 dB for the antenna with a single director element. Finally, new intermediate directions of transmission can be achieved by using two successive director elements, where the power is focused in the bisectrix of the angle formed by the two directors. This converts a six-sector antenna like the SPIDA into a twelve-sector antenna without changing the hardware.

Path planning in dynamic environments with moving obstacles is computationally complex since it requires modeling time as an additional dimension. While in other domains there are state dominance relationships that can significantly reduce the complexity of the search, in dynamic environments such relationships do not exist. This paper presents a novel state dominance relationship tailored specifically for dynamic environments, and presents a planner that uses that property to plan paths over ten times faster than without using state dominance.

Search-based planning is widely used for mobile robot motion planning because of its guarantees of optimality and completeness. In continuous state-spaces, however, most existing approaches have significant limitations in terms of optimality and completeness because of the underlying grid used. We propose an approach that eliminates the dependency on grids by using more general equivalence classes to quickly find an initial solution and instead of pruning states that fall within an equivalence class and have higher cost, we use an inflated heuristic to lower the priority of these states in the search. In further iterations, we reduce the inflated heuristic in a principled way, thus providing fast solutions with provable suboptimality bounds that can be improved as time allows. The proposed approach produces smooth paths with the resolution dictated by the action set. Finer action sets produce higher resolution paths that are more computationally intensive to calculate and coarser action sets produce lower resolution paths that are faster to compute. To the best of our knowledge, this is the first algorithm that is able to plan in continuous state-spaces with provable guarantees on completeness and bounds on suboptimality for a given action set. Experimental results on 3D (x,y,theta) path planning show that, on average, this approach is able to find paths in less than two seconds that are within 2% of the optimal path cost in worlds of up to 1000x1000 m with a minimum step size of one meter.

This paper presents two new approaches that enable the use of linear landmarks for planning paths with uncertainty in position in outdoor environments. The first approach uses a combination of forward simulation and entropy to reduce the dimensionality of the search space, while still preserving most of the information required to propagate a full covariance matrix. The second approach adds incremental binning to improve the quality of the solution while still keeping the dimensionality of the search space relatively low. These approaches provide a better compromise of speed and quality of the solution than most existing approaches, and are able to successfully utilize linear landmarks in large outdoor environments.

This paper presents two new approaches to planning with uncertainty in position that achieve better performance than existing techniques and that are able to incorporate changes in the environment in near real-time. Both approaches reuse previous searches and replan when changes in the environment are detected. The first approach, called replanning with prior map updates, assumes that changes in the prior map originate from the same source as the original prior map. Therefore, the updates are registered with the existing map, but not with the position of the robot. The resulting path after applying the updates is the same as if the updates had been present in the original prior map. The second approach, called replanning with sensor updates, assumes that changes in the prior map originate from on-board sensors. Therefore, the updates are registered with the robot, but not with the existing map. The resulting path after applying the updates is not the same path that would be found if the updates had taken place in the original prior map. Replanning with prior map updates achieves a speed-up to one order of magnitude with respect to forward planning from scratch, while replanning with sensor updates achieves a speedup of almost two orders of magnitude.

We present a novel approach to mobile robot navigation that enables navigation in outdoor environments without GPS. The approach uses a path planner that calculates optimal paths while considering uncertainty in position and that uses landmarks to localize the vehicle as part of the planning process. The landmarks are simple, possibly aliased, features that have been previously identified in a high-resolution map. These landmarks are combined with an estimate of the position of the vehicle to create unique and robust features. This approach reduces or eliminates the need for GPS and enables the use of prior maps with imperfect map registration.

We introduce a resolution-optimal path planner that considers uncertainty while optimizing any monotonic objective function such as mobility cost, risk, or energy expended. The resulting path minimizes the expected cost of the objective function, while ensuring that the uncertainty in the position of the robot does not compromise the safety of the robot or the reachability of the goal. Although the problem domain is stochastic in nature, our algorithm takes advantage of deterministic path-planning techniques to achieve significant performance improvements.