Data-driven clustering and Bernoulli merging for the Poisson multi-Bernoulli mixture filter

This paper proposes a clustering and merging approach for the Poisson multi-Bernoulli mixture (PMBM) filter to lower its computational complexity and make it suitable for multiple target tracking with a high number of targets. We define a measurement-driven clustering algorithm to reduce the data association problem into several subproblems, and we provide the derivation of the resulting clustered PMBM posterior density via Kullback-Leibler divergence minimisation. Furthermore, we investigate different strategies to reduce the number of single target hypotheses by approximating the posterior via merging and inter-track swapping of Bernoulli components. We evaluate the performance of the proposed algorithm on simulated tracking scenarios with more than one thousand targets.

Diaphragm Ankle Actuation for Efficient Series Elastic Legged Robot Hopping

Robots need lightweight legs for agile locomotion, and intrinsic series elastic compliance has proven to be a major ingredient for energy-efficient locomotion and robust locomotion control. Animals' anatomy and locomotion capabilities emphasize the importance of that lightweight legs and integrated, compact, series elastically actuated for distal leg joints. But unlike robots, animals achieve series elastic actuation by their muscle-tendon units. So far no designs are available that feature all characteristics of a perfect distal legged locomotion actuator; a low-weight and low-inertia design, with high mechanical efficiency, no stick and sliding friction, low mechanical complexity, high-power output while being easy to mount. Ideally, such an actuator can be controlled directly and without mechanical cross-coupling, for example remotely. With this goal in mind, we propose a low-friction, lightweight Series ELastic Diaphragm distal Actuator (SELDA) which meets many, although not all, of the above requirements. We develop, implement, and characterize a bioinspired robot leg that features a SELDA-actuated foot segment. We compare two leg configurations controlled by a central pattern generator that both feature agile forward hopping. By tuning SELDA's activation timing, we effectively adjust the robot's hopping height by 11% and its forward velocity by 14%, even with comparatively low power injection to the distal joint.