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Ashwini B P

Publications and source records attributed to Ashwini B P.

2 recordsLinked to original sources

Fault-Geometry-Aware Control Allocation for Emergency Quadrotor Landing under Single-Motor Loss of Effectiveness

Partial single-motor loss of effectiveness (LoE) changes the remaining control-authority geometry of a quadrotor and can change which control allocator performs best during emergency landing[cite: 11]. We compare three allocators in Crazyflie software-in-the-loop simulation: bounded weighted least squares (reported as PINV), a finite-candidate residual allocator (QP-lite), and the same residual allocator with scoring parameters tuned offline by the Cross-Entropy Method (CEM)[cite: 11]. For this study, fault geometry is represented by the known failed-motor index[cite: 11]. At $η=0.496$ remaining effectiveness, matched development trials select CEM for M1, M3, and M4 and PINV for M2; the M3 selection is a one-trial margin (29/30 versus 28/30)[cite: 11]. After freezing this routing map, the policy achieves 117/120 safe first-contact touchdowns on fresh seeds and 115/120 in a separate randomized-order integrated-supervisor evaluation[cite: 11]. For the critical M2/PINV branch, Firth regression gives a nominal $η_{50}\approx0.4956$ with a within-model 95% bootstrap interval of approximately [0.4955, 0.4956][cite: 11]. One-factor-at-a-time $\pm10\%$ plant perturbations shift $η_{50}$ by up to 0.05495 for thrust coefficient and 0.04974 for mass; the most frequent violated first-contact criterion also changes from vertical speed in many conditions to angular rate for mass -10% and thrust-coefficient +10%[cite: 11]. At fixed $η=0.496$, PINV has a higher M2 safe count than frozen CEM in 9/11 tested plant states and ties in 2/11; the two ties are both 0/30[cite: 11]. The experiments therefore distinguish motor-conditioned allocator selection from sensitivity to plant-model mismatch[cite: 11].

cs.RO↗

Analyzing travel time reliability of a bus route in a limited data set scenario: A case study

In this information era commuters prefer to know a reliable travel time to plan ahead of their journey using both public and private modes. In this direction reliability analysis using the location data of the buses is conducted in two folds in the current work; (i) Reliability analysis of a public transit service at route level, and (ii) Travel time reliability analysis of a route utilizing the location data of the buses. The reliability parameters assessed for public transit service are headway, passenger waiting time, travel speed, and travel time as per the Service Level Benchmarks for Urban Transport by the National Urban Transport Policy, Government of India. And travel time reliability parameters such as Buffer Time Index, Travel Time Index, and Planning Time Index are assessed as per Federal Highway Administration, Department of Transportation, U S. The study is conducted in Tumakuru city, India for a significant bus route in a limited data sources scenario. The results suggest that (i) the Level of Service of the public transit service needs improvement. (ii)around 30% excess of average travel time is needed as buffer time. (iii) more than double the amount of free flow travel time must be planned during peak hours and in the worst case. In the future, the analysis conducted for the route can be extended for citywide performance analysis in both folds. Also, the same method can be applied to cities with similar demographics and traffic-related infrastructure.

physics.soc-ph↗